MCMC for parameters estimation by bayesian approach

International Nuclear Information System (INIS)

Ait Saadi, H.; Ykhlef, F.; Guessoum, A.

2011-01-01

This article discusses the parameter estimation for dynamic system by a Bayesian approach associated with Markov Chain Monte Carlo methods (MCMC). The MCMC methods are powerful for approximating complex integrals, simulating joint distributions, and the estimation of marginal posterior distributions, or posterior means. The MetropolisHastings algorithm has been widely used in Bayesian inference to approximate posterior densities. Calibrating the proposal distribution is one of the main issues of MCMC simulation in order to accelerate the convergence.

Nuovi Metodi di visualizzazione geografica: l'approccio Focus+Glue+Context

Directory of Open Access Journals (Sweden)

Flavio Lupia

2010-03-01

Full Text Available New cartographic visualization methods: the Focus+Glue+Context approach Focus+Glue+Context is a new cartographic visualization method specifically designed to solve the fruition problems connected with the use of mobile devices and web mapping services. The objective of the F+G+C approach is to reduce users cognitive efforts when reading a map: to do so, the area of interest is ‘highlighted’ in a lower and more detailed scale through a fisheye lens effect, while the sorrouding context, useful to the user to determine the items relationships in a map, is maintained on a higher scale.

Nuovi Metodi di visualizzazione geografica: l'approccio Focus+Glue+Context

Directory of Open Access Journals (Sweden)

Flavio Lupia

2010-03-01

Full Text Available New cartographic visualization methods:the Focus+Glue+Context approachFocus+Glue+Context is a new cartographic visualization method specifically designed to solve the fruition problems connected with the use of mobile devices and web mapping services. The objective of the F+G+C approach is to reduce users cognitive efforts when reading a map: to do so, the area of interest is ‘highlighted’ in a lower and more detailed scale through a fisheye lens effect, while the sorrouding context, useful to the user to determine the items relationships in a map, is maintained on a higher scale.

Comparison between Fisherian and Bayesian approach to ...

African Journals Online (AJOL)

... of its simplicity and optimality properties is normally used for two group cases. However, Bayesian approach is found to be better than Fisher's approach because of its low misclassification error rate. Keywords: variance-covariance matrices, centroids, prior probability, mahalanobis distance, probability of misclassification ...

Double Coaxial Microcatheter Technique for Glue Embolization of Renal Arteriovenous Malformations

International Nuclear Information System (INIS)

Uchikawa, Yoko; Mori, Kensaku; Shiigai, Masanari; Konishi, Takahiro; Hoshiai, Sodai; Ishigro, Toshitaka; Hiyama, Takashi; Nakai, Yasunobu; Minami, Manabu

2015-01-01

PurposeTo demonstrate the technical benefit of the double coaxial microcatheter technique for embolization of renal arteriovenous malformations (AVMs) with n-butyl cyanoacrylate and iodized oil (glue).Materials and MethodsSix consecutive patients (1 man and 5 women; mean age 61 years; range 44–77 years) with renal AVMs were included. Five patients had hematuria, and one had a risk of heart failure due to a large intrarenal arteriovenous shunt. All patients underwent transarterial embolization using glue and the double coaxial microcatheter technique with outer 2.6F and inner 1.9F microcatheters. After glue injection, the inner microcatheter was retracted, while the outer microcatheter was retained. We assessed the complications and clinical outcomes of this technique.ResultsTechnical success was achieved in all patients. In 9 sessions, 34 feeding arteries were embolized with glue using the double coaxial microcatheter technique, 1 was embolized with glue using a single microcatheter, and 2 were embolized with coils. The double coaxial microcatheter technique was useful for selecting small tortuous feeding arteries, preventing glue reflux to the proximal arteries, and approaching multiple feeding arteries without complete retraction of the microcatheters. As a minor complication, glue migrated into the venous system in four patients without any sequelae. In all patients, favorable clinical outcomes, including hematuria cessation in five patients and improvement of the large intrarenal arteriovenous shunt in one patient, were obtained without deterioration of renal function.ConclusionGlue embolization with the double coaxial microcatheter technique was useful for treating renal AVMs with multiple tortuous feeding arteries

Double Coaxial Microcatheter Technique for Glue Embolization of Renal Arteriovenous Malformations

Energy Technology Data Exchange (ETDEWEB)

Uchikawa, Yoko, E-mail: jauchikawa@gmail.com [University of Tsukuba Hospital, Department of Radiology (Japan); Mori, Kensaku, E-mail: moriken@md.tsukuba.ac.jp [University of Tsukuba, Department of Radiology, Faculty of Medicine (Japan); Shiigai, Masanari, E-mail: m-41gai@yahoo.co.jp [Tsukuba Medical Center Hospital, Department of Radiology (Japan); Konishi, Takahiro, E-mail: soratobukangaruu@gmail.com [University of Tsukuba Hospital, Department of Radiology (Japan); Hoshiai, Sodai, E-mail: hoshiai@sb4.so-net.ne.jp [Ibaraki Prefectural Central Hospital, Department of Radiology (Japan); Ishigro, Toshitaka, E-mail: suzutokei@gmail.com; Hiyama, Takashi, E-mail: med-tak@hotmail.com [University of Tsukuba Hospital, Department of Radiology (Japan); Nakai, Yasunobu, E-mail: nakaiya@tmch.or.jp [Tsukuba Medical Center Hospital, Department of Neurosurgery (Japan); Minami, Manabu, E-mail: mminami@md.tsukuba.ac.jp [University of Tsukuba, Department of Radiology, Faculty of Medicine (Japan)

2015-10-15

PurposeTo demonstrate the technical benefit of the double coaxial microcatheter technique for embolization of renal arteriovenous malformations (AVMs) with n-butyl cyanoacrylate and iodized oil (glue).Materials and MethodsSix consecutive patients (1 man and 5 women; mean age 61 years; range 44–77 years) with renal AVMs were included. Five patients had hematuria, and one had a risk of heart failure due to a large intrarenal arteriovenous shunt. All patients underwent transarterial embolization using glue and the double coaxial microcatheter technique with outer 2.6F and inner 1.9F microcatheters. After glue injection, the inner microcatheter was retracted, while the outer microcatheter was retained. We assessed the complications and clinical outcomes of this technique.ResultsTechnical success was achieved in all patients. In 9 sessions, 34 feeding arteries were embolized with glue using the double coaxial microcatheter technique, 1 was embolized with glue using a single microcatheter, and 2 were embolized with coils. The double coaxial microcatheter technique was useful for selecting small tortuous feeding arteries, preventing glue reflux to the proximal arteries, and approaching multiple feeding arteries without complete retraction of the microcatheters. As a minor complication, glue migrated into the venous system in four patients without any sequelae. In all patients, favorable clinical outcomes, including hematuria cessation in five patients and improvement of the large intrarenal arteriovenous shunt in one patient, were obtained without deterioration of renal function.ConclusionGlue embolization with the double coaxial microcatheter technique was useful for treating renal AVMs with multiple tortuous feeding arteries.

A Bayesian Nonparametric Approach to Factor Analysis

DEFF Research Database (Denmark)

Piatek, Rémi; Papaspiliopoulos, Omiros

2018-01-01

This paper introduces a new approach for the inference of non-Gaussian factor models based on Bayesian nonparametric methods. It relaxes the usual normality assumption on the latent factors, widely used in practice, which is too restrictive in many settings. Our approach, on the contrary, does no...

Probabilistic Damage Characterization Using the Computationally-Efficient Bayesian Approach

Science.gov (United States)

Warner, James E.; Hochhalter, Jacob D.

2016-01-01

This work presents a computationally-ecient approach for damage determination that quanti es uncertainty in the provided diagnosis. Given strain sensor data that are polluted with measurement errors, Bayesian inference is used to estimate the location, size, and orientation of damage. This approach uses Bayes' Theorem to combine any prior knowledge an analyst may have about the nature of the damage with information provided implicitly by the strain sensor data to form a posterior probability distribution over possible damage states. The unknown damage parameters are then estimated based on samples drawn numerically from this distribution using a Markov Chain Monte Carlo (MCMC) sampling algorithm. Several modi cations are made to the traditional Bayesian inference approach to provide signi cant computational speedup. First, an ecient surrogate model is constructed using sparse grid interpolation to replace a costly nite element model that must otherwise be evaluated for each sample drawn with MCMC. Next, the standard Bayesian posterior distribution is modi ed using a weighted likelihood formulation, which is shown to improve the convergence of the sampling process. Finally, a robust MCMC algorithm, Delayed Rejection Adaptive Metropolis (DRAM), is adopted to sample the probability distribution more eciently. Numerical examples demonstrate that the proposed framework e ectively provides damage estimates with uncertainty quanti cation and can yield orders of magnitude speedup over standard Bayesian approaches.

[Bayesian approach for the cost-effectiveness evaluation of healthcare technologies].

Science.gov (United States)

Berchialla, Paola; Gregori, Dario; Brunello, Franco; Veltri, Andrea; Petrinco, Michele; Pagano, Eva

2009-01-01

The development of Bayesian statistical methods for the assessment of the cost-effectiveness of health care technologies is reviewed. Although many studies adopt a frequentist approach, several authors have advocated the use of Bayesian methods in health economics. Emphasis has been placed on the advantages of the Bayesian approach, which include: (i) the ability to make more intuitive and meaningful inferences; (ii) the ability to tackle complex problems, such as allowing for the inclusion of patients who generate no cost, thanks to the availability of powerful computational algorithms; (iii) the importance of a full use of quantitative and structural prior information to produce realistic inferences. Much literature comparing the cost-effectiveness of two treatments is based on the incremental cost-effectiveness ratio. However, new methods are arising with the purpose of decision making. These methods are based on a net benefits approach. In the present context, the cost-effectiveness acceptability curves have been pointed out to be intrinsically Bayesian in their formulation. They plot the probability of a positive net benefit against the threshold cost of a unit increase in efficacy.A case study is presented in order to illustrate the Bayesian statistics in the cost-effectiveness analysis. Emphasis is placed on the cost-effectiveness acceptability curves. Advantages and disadvantages of the method described in this paper have been compared to frequentist methods and discussed.

Contact dermatitis to ethyl-cyanoacrylate-containing glue.

Science.gov (United States)

Belsito, D V

1987-10-01

3 patients with contact dermatitis to an ethyl cyanoacrylate glue are presented. Although reactions to cyanoacrylate glues are considered rare, more widespread use of these products by nail salons is likely to be associated with an increased incidence of positive reactions. All 3 of our patients came into contact with the glue during "nail wrapping". In this process, ethyl cyanoacrylate or another "instant glue" is used to adhere glue-impregnated silk or linen to the nail plate which is then filed to shape the nail. This procedure creates fine acrylic-containing dust which may facilitate an allergic response. Fine particulate matter may be transferred to other distant cutaneous sites, such as the eyelids, resulting in more widespread cutaneous eruptions. Dermatologists in areas where nail wrapping is becoming more fashionable are advised to be alert to potential cyanoacrylate glue allergies which present as periungual eczema which may be associated with eyelid dermatitis and features of nummular dermatitis particularly over the dorsal hand.

A Bayesian approach to person perception.

Science.gov (United States)

Clifford, C W G; Mareschal, I; Otsuka, Y; Watson, T L

2015-11-01

Here we propose a Bayesian approach to person perception, outlining the theoretical position and a methodological framework for testing the predictions experimentally. We use the term person perception to refer not only to the perception of others' personal attributes such as age and sex but also to the perception of social signals such as direction of gaze and emotional expression. The Bayesian approach provides a formal description of the way in which our perception combines current sensory evidence with prior expectations about the structure of the environment. Such expectations can lead to unconscious biases in our perception that are particularly evident when sensory evidence is uncertain. We illustrate the ideas with reference to our recent studies on gaze perception which show that people have a bias to perceive the gaze of others as directed towards themselves. We also describe a potential application to the study of the perception of a person's sex, in which a bias towards perceiving males is typically observed. Copyright © 2015 Elsevier Inc. All rights reserved.

A computational Bayesian approach to dependency assessment in system reliability

International Nuclear Information System (INIS)

Yontay, Petek; Pan, Rong

2016-01-01

Due to the increasing complexity of engineered products, it is of great importance to develop a tool to assess reliability dependencies among components and systems under the uncertainty of system reliability structure. In this paper, a Bayesian network approach is proposed for evaluating the conditional probability of failure within a complex system, using a multilevel system configuration. Coupling with Bayesian inference, the posterior distributions of these conditional probabilities can be estimated by combining failure information and expert opinions at both system and component levels. Three data scenarios are considered in this study, and they demonstrate that, with the quantification of the stochastic relationship of reliability within a system, the dependency structure in system reliability can be gradually revealed by the data collected at different system levels. - Highlights: • A Bayesian network representation of system reliability is presented. • Bayesian inference methods for assessing dependencies in system reliability are developed. • Complete and incomplete data scenarios are discussed. • The proposed approach is able to integrate reliability information from multiple sources at multiple levels of the system.

Accurate phenotyping: Reconciling approaches through Bayesian model averaging.

Directory of Open Access Journals (Sweden)

Carla Chia-Ming Chen

Full Text Available Genetic research into complex diseases is frequently hindered by a lack of clear biomarkers for phenotype ascertainment. Phenotypes for such diseases are often identified on the basis of clinically defined criteria; however such criteria may not be suitable for understanding the genetic composition of the diseases. Various statistical approaches have been proposed for phenotype definition; however our previous studies have shown that differences in phenotypes estimated using different approaches have substantial impact on subsequent analyses. Instead of obtaining results based upon a single model, we propose a new method, using Bayesian model averaging to overcome problems associated with phenotype definition. Although Bayesian model averaging has been used in other fields of research, this is the first study that uses Bayesian model averaging to reconcile phenotypes obtained using multiple models. We illustrate the new method by applying it to simulated genetic and phenotypic data for Kofendred personality disorder-an imaginary disease with several sub-types. Two separate statistical methods were used to identify clusters of individuals with distinct phenotypes: latent class analysis and grade of membership. Bayesian model averaging was then used to combine the two clusterings for the purpose of subsequent linkage analyses. We found that causative genetic loci for the disease produced higher LOD scores using model averaging than under either individual model separately. We attribute this improvement to consolidation of the cores of phenotype clusters identified using each individual method.

Rejoining of cut wounds by engineered gelatin-keratin glue.

Science.gov (United States)

Thirupathi Kumara Raja, S; Thiruselvi, T; Sailakshmi, G; Ganesh, S; Gnanamani, A

2013-08-01

Rejoining of cut tissue ends of a critical site challenges clinicians. The toxicity, antigenicity, low adhesive strength, flexibility, swelling and cost of the currently employed glue demands an alternative. Engineered gelatin-keratin glue (EGK-glue) described in the present study was found to be suitable for wet tissue approximation. EGK-glue was prepared by engineering gelatin with caffeic acid using EDC and conjugating with keratin by periodate oxidation. UV-visible, (1)H NMR and circular dichroism analyses followed by experiments on gelation time, rheology, gel adhesive strength (in vitro), wet tissue approximation (in vivo), H&E staining of tissue sections at scheduled time intervals and tensile strength of the healed skin were carried out to assess the effectiveness of the EGK-glue in comparison with fibrin glue and cyanoacrylate. Results of UV-visible, NMR and CD analyses confirmed the functionalization and secondary structural changes. Increasing concentration of keratin reduces the gelation time (glue for clinical applications. Tissue approximation property assessed using the incision wound model (Wistar strain) in comparison with cyanoacrylate and fibrin glue suggested, that EGK-glue explicitly accelerates the rejoining of tissue with a 1.86 fold increase in skin tensile strength after healing. Imparting quinone moiety to gelatin-keratin conjugates through caffeic acid and a weaker oxidizing agent provides an adhesive glue with appreciable strength, and hemocompatible, cytocompatible and biodegradable properties, which, rejoin the cut tissue ends effectively. EGK-glue obtained in the present study finds wide biomedical/clinical applications. Copyright © 2013 Elsevier B.V. All rights reserved.

Bayesian logistic regression approaches to predict incorrect DRG assignment.

Science.gov (United States)

Suleiman, Mani; Demirhan, Haydar; Boyd, Leanne; Girosi, Federico; Aksakalli, Vural

2018-05-07

Episodes of care involving similar diagnoses and treatments and requiring similar levels of resource utilisation are grouped to the same Diagnosis-Related Group (DRG). In jurisdictions which implement DRG based payment systems, DRGs are a major determinant of funding for inpatient care. Hence, service providers often dedicate auditing staff to the task of checking that episodes have been coded to the correct DRG. The use of statistical models to estimate an episode's probability of DRG error can significantly improve the efficiency of clinical coding audits. This study implements Bayesian logistic regression models with weakly informative prior distributions to estimate the likelihood that episodes require a DRG revision, comparing these models with each other and to classical maximum likelihood estimates. All Bayesian approaches had more stable model parameters than maximum likelihood. The best performing Bayesian model improved overall classification per- formance by 6% compared to maximum likelihood, with a 34% gain compared to random classification, respectively. We found that the original DRG, coder and the day of coding all have a significant effect on the likelihood of DRG error. Use of Bayesian approaches has improved model parameter stability and classification accuracy. This method has already lead to improved audit efficiency in an operational capacity.

Identification of transmissivity fields using a Bayesian strategy and perturbative approach

Science.gov (United States)

Zanini, Andrea; Tanda, Maria Giovanna; Woodbury, Allan D.

2017-10-01

The paper deals with the crucial problem of the groundwater parameter estimation that is the basis for efficient modeling and reclamation activities. A hierarchical Bayesian approach is developed: it uses the Akaike's Bayesian Information Criteria in order to estimate the hyperparameters (related to the covariance model chosen) and to quantify the unknown noise variance. The transmissivity identification proceeds in two steps: the first, called empirical Bayesian interpolation, uses Y* (Y = lnT) observations to interpolate Y values on a specified grid; the second, called empirical Bayesian update, improve the previous Y estimate through the addition of hydraulic head observations. The relationship between the head and the lnT has been linearized through a perturbative solution of the flow equation. In order to test the proposed approach, synthetic aquifers from literature have been considered. The aquifers in question contain a variety of boundary conditions (both Dirichelet and Neuman type) and scales of heterogeneities (σY2 = 1.0 and σY2 = 5.3). The estimated transmissivity fields were compared to the true one. The joint use of Y* and head measurements improves the estimation of Y considering both degrees of heterogeneity. Even if the variance of the strong transmissivity field can be considered high for the application of the perturbative approach, the results show the same order of approximation of the non-linear methods proposed in literature. The procedure allows to compute the posterior probability distribution of the target quantities and to quantify the uncertainty in the model prediction. Bayesian updating has advantages related both to the Monte-Carlo (MC) and non-MC approaches. In fact, as the MC methods, Bayesian updating allows computing the direct posterior probability distribution of the target quantities and as non-MC methods it has computational times in the order of seconds.

A Bayesian approach to meta-analysis of plant pathology studies.

Science.gov (United States)

Mila, A L; Ngugi, H K

2011-01-01

Bayesian statistical methods are used for meta-analysis in many disciplines, including medicine, molecular biology, and engineering, but have not yet been applied for quantitative synthesis of plant pathology studies. In this paper, we illustrate the key concepts of Bayesian statistics and outline the differences between Bayesian and classical (frequentist) methods in the way parameters describing population attributes are considered. We then describe a Bayesian approach to meta-analysis and present a plant pathological example based on studies evaluating the efficacy of plant protection products that induce systemic acquired resistance for the management of fire blight of apple. In a simple random-effects model assuming a normal distribution of effect sizes and no prior information (i.e., a noninformative prior), the results of the Bayesian meta-analysis are similar to those obtained with classical methods. Implementing the same model with a Student's t distribution and a noninformative prior for the effect sizes, instead of a normal distribution, yields similar results for all but acibenzolar-S-methyl (Actigard) which was evaluated only in seven studies in this example. Whereas both the classical (P = 0.28) and the Bayesian analysis with a noninformative prior (95% credibility interval [CRI] for the log response ratio: -0.63 to 0.08) indicate a nonsignificant effect for Actigard, specifying a t distribution resulted in a significant, albeit variable, effect for this product (CRI: -0.73 to -0.10). These results confirm the sensitivity of the analytical outcome (i.e., the posterior distribution) to the choice of prior in Bayesian meta-analyses involving a limited number of studies. We review some pertinent literature on more advanced topics, including modeling of among-study heterogeneity, publication bias, analyses involving a limited number of studies, and methods for dealing with missing data, and show how these issues can be approached in a Bayesian framework

Modelling of population dynamics of red king crab using Bayesian approach

Directory of Open Access Journals (Sweden)

Bakanev Sergey ...

2012-10-01

Modeling population dynamics based on the Bayesian approach enables to successfully resolve the above issues. The integration of the data from various studies into a unified model based on Bayesian parameter estimation method provides a much more detailed description of the processes occurring in the population.

FIBRIN GLUE DAN APLIKASINYA

Directory of Open Access Journals (Sweden)

Agi Harliani S

2015-08-01

Full Text Available Fibrin Tissue Adhesive (FTA, Fibrin Sealant (FS or Fibrin Glue (FG are names given to a group of product that lead to the formation of fibrin clot at the site of application. Fibrin Glue represents a new revolution for local haemostatic, which produced by based on the understanding about blood coagulation process. The mechanism of FG mimics the last stage of blood coagulation process. Haemophilia, is a congenital inherited bleeding disorder, characterized by repeated bleeding episodes. The basic pathology is deficiency of factor VIII (hemophilia A or factor IX (hemophilia B. At bleeding episodes, hemophilia patients need replacement therapy. Hemophilia patients need transfusion of cryoprecipitate, Fresh Frozen Plasma (FFP or factor concentrate as replacement therapy. Oral surgery, dental extraction, circumcision, and orthopedic operations are the most important indications for fibrin glue in hemophilia care. As haemostatic local, FG minimizes bleeding, reducing the need of transfusion or factor concentrate, reducing the complication of transfusion, hospitalization and cost.

Safety of Synthetic Glue Used for Laparoscopic Prolapse Treatment.

Science.gov (United States)

Sarasa Castelló, Núria; Toth, Alexandra; Canis, Michel; Botchorishvilli, Revaz

2017-12-29

We detected mesh erosion and serious postoperative complications in 3 women after performing laparoscopic promontofixation (LPF) using glue for mesh fixation. Glue, largely used in hernia surgery repair, is proposed by some gynecologic surgeons because it saves time and is easier to use than traditional sutures. We report 3 cases of postoperative complications after LPF in which glue had been used and provide research in the published literature about the use of glue in LPF. A research of glue use in gynecology mesh fixation was performed through PubMed on October 2016. The search was done using the Medical Subject Heading terms "POP" & "Laparoscopy" & "surgical Mesh" and the word either "glue" or "adhesive. Only 2 articles were found: Willecocq et al [1] and Estrade et al [2]. Neither study focused on postoperative complications. In this publication, we accurately edited video surgeries with an instructive purpose. University Hospital of Clermont-Ferrand, France. Patient A, a 65-year-old woman, complained of pelvic pain and vaginal discharge 1 month after LPF (polypropylene mesh and glue had been used). Wall mesh exposure and purulent discharge were noted. She received antibiotics and underwent mesh ablation surgery; debris of the glue was easily identified. Patient B, a 65-year-old lady with previous hysterectomy consulted for a bulging feeling in her vagina (classification: cystocele +2; rectocele +3 stage). An LPF was performed using polypropylene soft nonabsorbable mesh and glue. One month later, an apical defect of vaginal epithelialization was detected; she received long estrogenic local treatment but had to undergo surgery when presenting malodorous discharge and mesh exposure. The exposed mesh was removed, and pieces of glue were identified, having avoided mesh attachment. Patient C had a previous abdominal hysterectomy and promontofixation using a polyester mesh with glue. She consulted to us for vaginal mesh erosion covered with purulent discharge 3

Testing adaptive toolbox models: a Bayesian hierarchical approach.

Science.gov (United States)

Scheibehenne, Benjamin; Rieskamp, Jörg; Wagenmakers, Eric-Jan

2013-01-01

Many theories of human cognition postulate that people are equipped with a repertoire of strategies to solve the tasks they face. This theoretical framework of a cognitive toolbox provides a plausible account of intra- and interindividual differences in human behavior. Unfortunately, it is often unclear how to rigorously test the toolbox framework. How can a toolbox model be quantitatively specified? How can the number of toolbox strategies be limited to prevent uncontrolled strategy sprawl? How can a toolbox model be formally tested against alternative theories? The authors show how these challenges can be met by using Bayesian inference techniques. By means of parameter recovery simulations and the analysis of empirical data across a variety of domains (i.e., judgment and decision making, children's cognitive development, function learning, and perceptual categorization), the authors illustrate how Bayesian inference techniques allow toolbox models to be quantitatively specified, strategy sprawl to be contained, and toolbox models to be rigorously tested against competing theories. The authors demonstrate that their approach applies at the individual level but can also be generalized to the group level with hierarchical Bayesian procedures. The suggested Bayesian inference techniques represent a theoretical and methodological advancement for toolbox theories of cognition and behavior.

Bayesian approach for the reliability assessment of corroded interdependent pipe networks

International Nuclear Information System (INIS)

Ait Mokhtar, El Hassene; Chateauneuf, Alaa; Laggoune, Radouane

2016-01-01

Pipelines under corrosion are subject to various environment conditions, and consequently it becomes difficult to build realistic corrosion models. In the present work, a Bayesian methodology is proposed to allow for updating the corrosion model parameters according to the evolution of environmental conditions. For reliability assessment of dependent structures, Bayesian networks are used to provide interesting qualitative and quantitative description of the information in the system. The qualitative contribution lies in the modeling of complex system, composed by dependent pipelines, as a Bayesian network. The quantitative one lies in the evaluation of the dependencies between pipelines by the use of a new method for the generation of conditional probability tables. The effectiveness of Bayesian updating is illustrated through an application where the new reliability of degraded (corroded) pipe networks is assessed. - Highlights: • A methodology for Bayesian network modeling of pipe networks is proposed. • Bayesian approach based on Metropolis - Hastings algorithm is conducted for corrosion model updating. • The reliability of corroded pipe network is assessed by considering the interdependencies between the pipelines.

Glue Film Thickness Measurements by Spectral Reflectance

International Nuclear Information System (INIS)

Marshall, B.R.

2010-01-01

Spectral reflectance was used to determine the thickness of thin glue layers in a study of the effect of the glue on radiance and reflectance measurements of shocked-tin substrates attached to lithium fluoride windows. Measurements based on profilometry of the components were found to be inaccurate due to flatness variations and deformation of the tin substrate under pressure during the gluing process. The accuracy of the spectral reflectance measurements were estimated to be ±0.5 (micro)m, which was sufficient to demonstrate a convincing correlation between glue thickness and shock-generated light.

Glue Film Thickness Measurements by Spectral Reflectance

Energy Technology Data Exchange (ETDEWEB)

B. R. Marshall

2010-09-20

Spectral reflectance was used to determine the thickness of thin glue layers in a study of the effect of the glue on radiance and reflectance measurements of shocked-tin substrates attached to lithium fluoride windows. Measurements based on profilometry of the components were found to be inaccurate due to flatness variations and deformation of the tin substrate under pressure during the gluing process. The accuracy of the spectral reflectance measurements were estimated to be ±0.5 μm, which was sufficient to demonstrate a convincing correlation between glue thickness and shock-generated light.

Approaches in highly parameterized inversion: bgaPEST, a Bayesian geostatistical approach implementation with PEST: documentation and instructions

Science.gov (United States)

Fienen, Michael N.; D'Oria, Marco; Doherty, John E.; Hunt, Randall J.

2013-01-01

The application bgaPEST is a highly parameterized inversion software package implementing the Bayesian Geostatistical Approach in a framework compatible with the parameter estimation suite PEST. Highly parameterized inversion refers to cases in which parameters are distributed in space or time and are correlated with one another. The Bayesian aspect of bgaPEST is related to Bayesian probability theory in which prior information about parameters is formally revised on the basis of the calibration dataset used for the inversion. Conceptually, this approach formalizes the conditionality of estimated parameters on the specific data and model available. The geostatistical component of the method refers to the way in which prior information about the parameters is used. A geostatistical autocorrelation function is used to enforce structure on the parameters to avoid overfitting and unrealistic results. Bayesian Geostatistical Approach is designed to provide the smoothest solution that is consistent with the data. Optionally, users can specify a level of fit or estimate a balance between fit and model complexity informed by the data. Groundwater and surface-water applications are used as examples in this text, but the possible uses of bgaPEST extend to any distributed parameter applications.

The CostGlue XML Schema

OpenAIRE

Furfari, Francesco; Potort?, Francesco; Savić, Dragan

2008-01-01

An XML schema for scientific metadata is described. It is used for the CostGlue archival program, developed in the framework of the European Union COST Action 285: "Modelling and simulation tools for research in emerging multi-service telecommunications". The schema is freely available under the GNU LGPL license at http://wnet.isti.cnr.it/software/costglue/schema/2007/CostGlue.xsd, or at its official repository, at http://lt.fe.uni-lj. si/costglue/schema/2007/costglue.xsd.

Bayesian Mediation Analysis

OpenAIRE

Yuan, Ying; MacKinnon, David P.

2009-01-01

This article proposes Bayesian analysis of mediation effects. Compared to conventional frequentist mediation analysis, the Bayesian approach has several advantages. First, it allows researchers to incorporate prior information into the mediation analysis, thus potentially improving the efficiency of estimates. Second, under the Bayesian mediation analysis, inference is straightforward and exact, which makes it appealing for studies with small samples. Third, the Bayesian approach is conceptua...

A Bayesian approach to particle identification in ALICE

Energy Technology Data Exchange (ETDEWEB)

Wilkinson, Jeremy [Physikalisches Institut, Ruprecht-Karls-Universitaet Heidelberg (Germany); Collaboration: ALICE-Collaboration

2016-07-01

Particle identification (PID) is one of the major strengths of the ALICE detector at the LHC, and provides essential insight into quark-gluon plasma formation in heavy-ion collisions. PID is most effective when complementary identification techniques (such as specific energy loss in the Time Projection Chamber, or flight times measured by the Time Of Flight detector) are combined, however with standard PID techniques it can be difficult to combine these signals, especially when detectors with non-Gaussian responses are used. Here, an alternative probabilistic PID approach based on Bayes' theorem will be presented. This method facilitates the combination of different detector technologies based on the combined probability of a particle type to produce the signals measured in various detectors. The Bayesian PID approach will be briefly outlined, and benchmark analyses will be presented for high-purity samples of pions, kaons, and protons, as well as for the two-pronged decay D{sup 0} → K{sup -}π{sup +}, comparing the performance of the standard PID approach with that of the Bayesian approach. Finally, prospects for measuring the Λ{sub c} baryon in the three-pronged decay channel Λ{sub c}{sup +} → pK{sup -}π{sup +} are presented.

Three-dimensional glue detection and evaluation based on linear structured light

Science.gov (United States)

Xiao, Zhitao; Yang, Ruipeng; Geng, Lei; Liu, Yanbei

2018-01-01

During the online glue detection of body in white (BIW), the purpose of traditional glue detection based on machine vision is the localization and segmentation of glue, which is dissatisfactory for estimating the uniformity of glue with complex shape. A three-dimensional glue detection method based on the linear structured light and the movement parameters of robot is proposed. Firstly, the linear structured light and epipolar constraint algorithm are used for sign matching of binocular vision. Then, hand-eye relationship between robot and binocular camera is utilized to unified coordinate system. Finally, a structured light stripe extraction method is proposed to extract the sub-pixel coordinates of the light strip center. Experiments results demonstrate that the propose method can estimate the shape of glue accurately. For three kinds of glue with complex shape and uneven illumination, our method can detect the positions of blemishes. The absolute error of measurement is less than 1.04mm and the relative error is less than 10% respectively, which is suitable for online glue detection in BIW.

Bayesian methods for data analysis

CERN Document Server

Carlin, Bradley P.

2009-01-01

Approaches for statistical inference Introduction Motivating Vignettes Defining the Approaches The Bayes-Frequentist Controversy Some Basic Bayesian Models The Bayes approach Introduction Prior Distributions Bayesian Inference Hierarchical Modeling Model Assessment Nonparametric Methods Bayesian computation Introduction Asymptotic Methods Noniterative Monte Carlo Methods Markov Chain Monte Carlo Methods Model criticism and selection Bayesian Modeling Bayesian Robustness Model Assessment Bayes Factors via Marginal Density Estimation Bayes Factors

How to practise Bayesian statistics outside the Bayesian church: What philosophy for Bayesian statistical modelling?

NARCIS (Netherlands)

Borsboom, D.; Haig, B.D.

2013-01-01

Unlike most other statistical frameworks, Bayesian statistical inference is wedded to a particular approach in the philosophy of science (see Howson & Urbach, 2006); this approach is called Bayesianism. Rather than being concerned with model fitting, this position in the philosophy of science

Comparison of two fibrin glues in anastomoses and skin closure.

Science.gov (United States)

Park, W; Kim, W H; Lee, C H; Kim, D Y; Choi, J H; Huh, J W; Sung, H M; Kim, I S; Kweon, O K

2002-09-01

To control intra-operative haemorrhage, fibrin glues are preferred by many surgeons because of their biological advantages and convenience of application. Manufacturers have developed a few kinds of fibrin glues with a little difference in their composition. This study was to compare the effectiveness of two commercially available fibrin glues; Greenplast (Green Cross P. D. Company, Yongin, Korea) and Tisseel (Baxter-Immuno AG, Vienna, Austria). They were applied experimentally to several kinds of surgery in dogs - renal vessel anastomosis, partial splenectomy, intestinal anastomosis and incision skin wound - and evaluated for their haemostatic and adhesive effects. When the two glues were applied in renal vessel anastomosis, the amount of haemorrhage in artery and vein decreased significantly. They also decreased the haemorrhage in partial splenectomy. At 10 min after application of the glues to an incision skin wound, the tensile strengths developed were significantly higher than that of control. The present study indicates that two-component fibrin glues have a haemostatic effect as a mechanical barrier in renal vessel anastomosis and an adhesive effect in the early stage of incision skin wound closure, and the two glues have similar effects with no complications.

Comparison between the basic least squares and the Bayesian approach for elastic constants identification

Science.gov (United States)

Gogu, C.; Haftka, R.; LeRiche, R.; Molimard, J.; Vautrin, A.; Sankar, B.

2008-11-01

The basic formulation of the least squares method, based on the L2 norm of the misfit, is still widely used today for identifying elastic material properties from experimental data. An alternative statistical approach is the Bayesian method. We seek here situations with significant difference between the material properties found by the two methods. For a simple three bar truss example we illustrate three such situations in which the Bayesian approach leads to more accurate results: different magnitude of the measurements, different uncertainty in the measurements and correlation among measurements. When all three effects add up, the Bayesian approach can have a large advantage. We then compared the two methods for identification of elastic constants from plate vibration natural frequencies.

Kaolin Quality Prediction from Samples: A Bayesian Network Approach

International Nuclear Information System (INIS)

Rivas, T.; Taboada, J.; Ordonez, C.; Matias, J. M.

2009-01-01

We describe the results of an expert system applied to the evaluation of samples of kaolin for industrial use in paper or ceramic manufacture. Different machine learning techniques - classification trees, support vector machines and Bayesian networks - were applied with the aim of evaluating and comparing their interpretability and prediction capacities. The predictive capacity of these models for the samples analyzed was highly satisfactory, both for ceramic quality and paper quality. However, Bayesian networks generally proved to be the most useful technique for our study, as this approach combines good predictive capacity with excellent interpretability of the kaolin quality structure, as it graphically represents relationships between variables and facilitates what-if analyses.

Caged Molecular Glues as Photoactivatable Tags for Nuclear Translocation of Guests in Living Cells.

Science.gov (United States)

Arisaka, Akio; Mogaki, Rina; Okuro, Kou; Aida, Takuzo

2018-02-21

We developed dendritic caged molecular glues ( Caged Glue-R) as tags for nucleus-targeted drug delivery, whose multiple guanidinium ion (Gu + ) pendants are protected by an anionic photocleavable unit (butyrate-substituted nitroveratryloxycarbonyl; BA NVOC). Negatively charged Caged Glue-R hardly binds to anionic biomolecules because of their electrostatic repulsion. However, upon exposure of Caged Glue-R to UV light or near-infrared (NIR) light, the BA NVOC groups of Caged Glue-R are rapidly detached to yield an uncaged molecular glue ( Uncaged Glue-R) that carries multiple Gu + pendants. Because Gu + forms a salt bridge with PO 4 - , Uncaged Glue-R tightly adheres to anionic biomolecules such as DNA and phospholipids in cell membranes by a multivalent salt-bridge formation. When tagged with Caged Glue-R, guests can be taken up into living cells via endocytosis and hide in endosomes. However, when the Caged Glue-R tag is photochemically uncaged to form Uncaged Glue-R, the guests escape from the endosome and migrate into the cytoplasm followed by the cell nucleus. We demonstrated that quantum dots (QDs) tagged with Caged Glue-R can be delivered efficiently to cell nuclei eventually by irradiation with light.

A Bayesian approach to estimating variance components within a multivariate generalizability theory framework.

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Jiang, Zhehan; Skorupski, William

2017-12-12

In many behavioral research areas, multivariate generalizability theory (mG theory) has been typically used to investigate the reliability of certain multidimensional assessments. However, traditional mG-theory estimation-namely, using frequentist approaches-has limits, leading researchers to fail to take full advantage of the information that mG theory can offer regarding the reliability of measurements. Alternatively, Bayesian methods provide more information than frequentist approaches can offer. This article presents instructional guidelines on how to implement mG-theory analyses in a Bayesian framework; in particular, BUGS code is presented to fit commonly seen designs from mG theory, including single-facet designs, two-facet crossed designs, and two-facet nested designs. In addition to concrete examples that are closely related to the selected designs and the corresponding BUGS code, a simulated dataset is provided to demonstrate the utility and advantages of the Bayesian approach. This article is intended to serve as a tutorial reference for applied researchers and methodologists conducting mG-theory studies.

Endoscopic use of cyanoacrylate glue in the treatment of urethral fistula

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Andre Ramos Sorgi Macedo

2013-07-01

Full Text Available Purpose The aim of this video is to demonstrate an endoscopic and minimally invasive repair of an urethrocutaneous fistula with cyanoacrylate glue. Materials and Methods: A 56 year-old-man with post-infectious urethral stricture and recurrent perineal abscess formation due to urethral fistulas. Results The operative time was 60 minutes, no major complications were observed perioperatively and postoperatively. At a follow-up time of 6 months the patient had no evidence of recurrent fistula and abscess formation. CONCLUSIONS The endoscopic use of cyanoacrylate glue represents a safe and minimally invasive approach that might be offered as a first line option for the treatment of urinary fistulas in selected patients, especially those with narrow and long tracts.

A Bayesian sequential processor approach to spectroscopic portal system decisions

Energy Technology Data Exchange (ETDEWEB)

Sale, K; Candy, J; Breitfeller, E; Guidry, B; Manatt, D; Gosnell, T; Chambers, D

2007-07-31

The development of faster more reliable techniques to detect radioactive contraband in a portal type scenario is an extremely important problem especially in this era of constant terrorist threats. Towards this goal the development of a model-based, Bayesian sequential data processor for the detection problem is discussed. In the sequential processor each datum (detector energy deposit and pulse arrival time) is used to update the posterior probability distribution over the space of model parameters. The nature of the sequential processor approach is that a detection is produced as soon as it is statistically justified by the data rather than waiting for a fixed counting interval before any analysis is performed. In this paper the Bayesian model-based approach, physics and signal processing models and decision functions are discussed along with the first results of our research.

A Bayesian hierarchical approach to comparative audit for carotid surgery.

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Kuhan, G; Marshall, E C; Abidia, A F; Chetter, I C; McCollum, P T

2002-12-01

the aim of this study was to illustrate how a Bayesian hierarchical modelling approach can aid the reliable comparison of outcome rates between surgeons. retrospective analysis of prospective and retrospective data. binary outcome data (death/stroke within 30 days), together with information on 15 possible risk factors specific for CEA were available on 836 CEAs performed by four vascular surgeons from 1992-99. The median patient age was 68 (range 38-86) years and 60% were men. the model was developed using the WinBUGS software. After adjusting for patient-level risk factors, a cross-validatory approach was adopted to identify "divergent" performance. A ranking exercise was also carried out. the overall observed 30-day stroke/death rate was 3.9% (33/836). The model found diabetes, stroke and heart disease to be significant risk factors. There was no significant difference between the predicted and observed outcome rates for any surgeon (Bayesian p -value>0.05). Each surgeon had a median rank of 3 with associated 95% CI 1.0-5.0, despite the variability of observed stroke/death rate from 2.9-4.4%. After risk adjustment, there was very little residual between-surgeon variability in outcome rate. Bayesian hierarchical models can help to accurately quantify the uncertainty associated with surgeons' performance and rank.

Bayesian approach to analyzing holograms of colloidal particles.

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Dimiduk, Thomas G; Manoharan, Vinothan N

2016-10-17

We demonstrate a Bayesian approach to tracking and characterizing colloidal particles from in-line digital holograms. We model the formation of the hologram using Lorenz-Mie theory. We then use a tempered Markov-chain Monte Carlo method to sample the posterior probability distributions of the model parameters: particle position, size, and refractive index. Compared to least-squares fitting, our approach allows us to more easily incorporate prior information about the parameters and to obtain more accurate uncertainties, which are critical for both particle tracking and characterization experiments. Our approach also eliminates the need to supply accurate initial guesses for the parameters, so it requires little tuning.

Review of Reliability-Based Design Optimization Approach and Its Integration with Bayesian Method

Science.gov (United States)

Zhang, Xiangnan

2018-03-01

A lot of uncertain factors lie in practical engineering, such as external load environment, material property, geometrical shape, initial condition, boundary condition, etc. Reliability method measures the structural safety condition and determine the optimal design parameter combination based on the probabilistic theory. Reliability-based design optimization (RBDO) is the most commonly used approach to minimize the structural cost or other performance under uncertainty variables which combines the reliability theory and optimization. However, it cannot handle the various incomplete information. The Bayesian approach is utilized to incorporate this kind of incomplete information in its uncertainty quantification. In this paper, the RBDO approach and its integration with Bayesian method are introduced.

Compounds from silicones alter enzyme activity in curing barnacle glue and model enzymes.

Science.gov (United States)

Rittschof, Daniel; Orihuela, Beatriz; Harder, Tilmann; Stafslien, Shane; Chisholm, Bret; Dickinson, Gary H

2011-02-17

Attachment strength of fouling organisms on silicone coatings is low. We hypothesized that low attachment strength on silicones is, in part, due to the interaction of surface available components with natural glues. Components could alter curing of glues through bulk changes or specifically through altered enzyme activity. GC-MS analysis of silicone coatings showed surface-available siloxanes when the coatings were gently rubbed with a cotton swab for 15 seconds or given a 30 second rinse with methanol. Mixtures of compounds were found on 2 commercial and 8 model silicone coatings. The hypothesis that silicone components alter glue curing enzymes was tested with curing barnacle glue and with commercial enzymes. In our model, barnacle glue curing involves trypsin-like serine protease(s), which activate enzymes and structural proteins, and a transglutaminase which cross-links glue proteins. Transglutaminase activity was significantly altered upon exposure of curing glue from individual barnacles to silicone eluates. Activity of purified trypsin and, to a greater extent, transglutaminase was significantly altered by relevant concentrations of silicone polymer constituents. Surface-associated silicone compounds can disrupt glue curing and alter enzyme properties. Altered curing of natural glues has potential in fouling management.

A Bayesian concept learning approach to crowdsourcing

DEFF Research Database (Denmark)

Viappiani, P.; Zilles, S.; Hamilton, H.J.

2011-01-01

techniques, inference methods, and query selection strategies to assist a user charged with choosing a configuration that satisfies some (partially known) concept. Our model is able to simultaneously learn the concept definition and the types of the experts. We evaluate our model with simulations, showing......We develop a Bayesian approach to concept learning for crowdsourcing applications. A probabilistic belief over possible concept definitions is maintained and updated according to (noisy) observations from experts, whose behaviors are modeled using discrete types. We propose recommendation...

A multi-agent systems approach to distributed bayesian information fusion

NARCIS (Netherlands)

Pavlin, G.; de Oude, P.; Maris, M.; Nunnink, J.; Hood, T.

2010-01-01

This paper introduces design principles for modular Bayesian fusion systems which can (i) cope with large quantities of heterogeneous information and (ii) can adapt to changing constellations of information sources on the fly. The presented approach exploits the locality of relations in causal

Research on preparation of phosphate-modified animal glue binder for foundry use

Science.gov (United States)

Wang, Tian-Shu; Liu, Wei-Hua; Li, Ying-Min

2018-03-01

In this paper, three phosphates were used as modifiers to modify animal glue binder. The structural characteristics and thermal properties of animal glue binder treated with phosphates were studied by Fourier transform-infrared spectroscopy, gel permeation chromatography and derivative thermogravimetric analysis. The results showed that the modified animal glue binder had better sand tensile strength and lower viscosity than untreated animal glue binder. The best modification process was as follows: the optimal amount of sodium carbonate was 4 wt% to animal glue; the optimal weight ratio of the modifiers was sodium pyrophosphate : sodium tripolyphosphate : sodium hexametaphosphate : animal glue = 3 : 3 : 4 : 100, and the optimal reaction should be performed at 80°C for a reaction time of 120 min. A final tensile strength of approximately 3.20 MPa was achieved and the viscosity value was approximately 880 mPa s.

Role of cyanoacrylate glue therapy in enteral SEMS bleeding - A nightmare

Directory of Open Access Journals (Sweden)

Virukalpatti Goparathinam Mohanprasad

2013-01-01

Full Text Available Over the past century, the use of stents has evolved to a point where they are now used extensively throughout the gastrointestinal tract. Endoscopic stenting has become widely used for treatment of gastrointestinal and hepatobiliary strictures. Metallic stents are deployed in malignant strictures as a palliative procedure. Adverse events of stenting include perforation, migration, bleeding, occlusion, and pain. Finally, the use of multidisciplinary teams which include endoscopists, interventional radiologists, and surgeons allows for the exchange of ideas and procedural planning necessary for successful innovation. We present a case of successful cessation of bleeding by using cyanoacrylate glue therapy in post-enteral self-expandable metallic stent bleeding. In conclusion, glue therapy may also be considered as a mode of treatment other than conservative approach and angiographic coil embolization.

A Nonparametric Bayesian Approach For Emission Tomography Reconstruction

International Nuclear Information System (INIS)

Barat, Eric; Dautremer, Thomas

2007-01-01

We introduce a PET reconstruction algorithm following a nonparametric Bayesian (NPB) approach. In contrast with Expectation Maximization (EM), the proposed technique does not rely on any space discretization. Namely, the activity distribution--normalized emission intensity of the spatial poisson process--is considered as a spatial probability density and observations are the projections of random emissions whose distribution has to be estimated. This approach is nonparametric in the sense that the quantity of interest belongs to the set of probability measures on R k (for reconstruction in k-dimensions) and it is Bayesian in the sense that we define a prior directly on this spatial measure. In this context, we propose to model the nonparametric probability density as an infinite mixture of multivariate normal distributions. As a prior for this mixture we consider a Dirichlet Process Mixture (DPM) with a Normal-Inverse Wishart (NIW) model as base distribution of the Dirichlet Process. As in EM-family reconstruction, we use a data augmentation scheme where the set of hidden variables are the emission locations for each observed line of response in the continuous object space. Thanks to the data augmentation, we propose a Markov Chain Monte Carlo (MCMC) algorithm (Gibbs sampler) which is able to generate draws from the posterior distribution of the spatial intensity. A difference with EM is that one step of the Gibbs sampler corresponds to the generation of emission locations while only the expected number of emissions per pixel/voxel is used in EM. Another key difference is that the estimated spatial intensity is a continuous function such that there is no need to compute a projection matrix. Finally, draws from the intensity posterior distribution allow the estimation of posterior functionnals like the variance or confidence intervals. Results are presented for simulated data based on a 2D brain phantom and compared to Bayesian MAP-EM

Fibrin glue as agent for sealing corneal and conjunctival wound leaks.

Science.gov (United States)

Scalcione, C; Ortiz-Vaquerizas, D; Said, D G; Dua, H S

2018-02-01

PurposeTo describe a novel use of fibrin glue in managing leaking blebs and leaking wounds following trauma or surgery.MethodsInterventional case series.ResultsWe report eight patients, including three where intra-operative or immediate post-penetrating keratoplasty recalcitrant leaks from the graft-host junction and/or openings created by the needle pass, were noted. All three had thin recipient beds in the sector of leak. This was managed by intra-cameral injection of fibrin glue in the affected quadrant. This stopped the leak and allowed the defect to heal. One patient of Descemets-stripping-endothelial-keratoplasty had leak from the surgical wound, which was also sealed with fibrin glue. Two patients with leaking glaucoma-surgery-related blebs were treated with intra-bleb injection of fibrin glue to stop the leak. One patient with a penetrating corneal injury with a metal wire had a brisk leak upon removal of the wire. This was sealed with fibrin glue. Another patient of chemical burn with spontaneous leaks was managed by glue injection in the perforations. Transient rise of intraocular pressure in one patient with a leaking bleb was the only adverse event recorded.ConclusionThis novel adaptation of the application of fibrin glue can help to deal with persistent intra-operative, post-operative and traumatic aqueous and air leaks.

A Bayesian Approach to Person Fit Analysis in Item Response Theory Models. Research Report.

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Glas, Cees A. W.; Meijer, Rob R.

A Bayesian approach to the evaluation of person fit in item response theory (IRT) models is presented. In a posterior predictive check, the observed value on a discrepancy variable is positioned in its posterior distribution. In a Bayesian framework, a Markov Chain Monte Carlo procedure can be used to generate samples of the posterior distribution…

Bayesian methods for hackers probabilistic programming and Bayesian inference

CERN Document Server

Davidson-Pilon, Cameron

2016-01-01

Bayesian methods of inference are deeply natural and extremely powerful. However, most discussions of Bayesian inference rely on intensely complex mathematical analyses and artificial examples, making it inaccessible to anyone without a strong mathematical background. Now, though, Cameron Davidson-Pilon introduces Bayesian inference from a computational perspective, bridging theory to practice–freeing you to get results using computing power. Bayesian Methods for Hackers illuminates Bayesian inference through probabilistic programming with the powerful PyMC language and the closely related Python tools NumPy, SciPy, and Matplotlib. Using this approach, you can reach effective solutions in small increments, without extensive mathematical intervention. Davidson-Pilon begins by introducing the concepts underlying Bayesian inference, comparing it with other techniques and guiding you through building and training your first Bayesian model. Next, he introduces PyMC through a series of detailed examples a...

Towards GLUE 2: evolution of the computing element information model

International Nuclear Information System (INIS)

Andreozzi, S; Burke, S; Field, L; Konya, B

2008-01-01

A key advantage of Grid systems is the ability to share heterogeneous resources and services between traditional administrative and organizational domains. This ability enables virtual pools of resources to be created and assigned to groups of users. Resource awareness, the capability of users or user agents to have knowledge about the existence and state of resources, is required in order utilize the resource. This awareness requires a description of the services and resources typically defined via a community-agreed information model. One of the most popular information models, used by a number of Grid infrastructures, is the GLUE Schema, which provides a common language for describing Grid resources. Other approaches exist, however they follow different modeling strategies. The presence of different flavors of information models for Grid resources is a barrier for enabling inter-Grid interoperability. In order to solve this problem, the GLUE Working Group in the context of the Open Grid Forum was started. The purpose of the group is to oversee a major redesign of the GLUE Schema which should consider the successful modeling choices and flaws that have emerged from practical experience and modeling choices from other initiatives. In this paper, we present the status of the new model for describing computing resources as the first output from the working group with the aim of dissemination and soliciting feedback from the community

A Dynamic Bayesian Approach to Computational Laban Shape Quality Analysis

Directory of Open Access Journals (Sweden)

Dilip Swaminathan

2009-01-01

kinesiology. LMA (especially Effort/Shape emphasizes how internal feelings and intentions govern the patterning of movement throughout the whole body. As we argue, a complex understanding of intention via LMA is necessary for human-computer interaction to become embodied in ways that resemble interaction in the physical world. We thus introduce a novel, flexible Bayesian fusion approach for identifying LMA Shape qualities from raw motion capture data in real time. The method uses a dynamic Bayesian network (DBN to fuse movement features across the body and across time and as we discuss can be readily adapted for low-cost video. It has delivered excellent performance in preliminary studies comprising improvisatory movements. Our approach has been incorporated in Response, a mixed-reality environment where users interact via natural, full-body human movement and enhance their bodily-kinesthetic awareness through immersive sound and light feedback, with applications to kinesiology training, Parkinson's patient rehabilitation, interactive dance, and many other areas.

Experimental closure of gunshot wounds by fibrin glue with antibiotics in pigs

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Đenić Nebojša

2015-01-01

Full Text Available Background/Aim. Gunshot wounds caused by the automatic rifle M70AB2 (AK-47 7.62 mm, after the primary surgical management, were closed with delayed primary suture during the next four to seven days. This period coincides with the fibroblastic phase of wound healing. Fibrin glue is used as a local hemostatic and as a matrix for the local dosed release of antibiotics. Antibiotics addition to fibrin glue resulted in continuous diffusion into the surrounding next 4 to 7 days. The aim of this study was to create the preconditions for gunshot wounds closing without complications by the application of fibrin glue with antibiotics 24 h after primary surgical treatment. Methods. A total of 14 pigs were wounded in the gluteofemoral region by the bullet M67, initial velocity of 720 m/s. All wounded animals were surgically treated according to the principles of the warsurgery doctrine. Seven wounds were closed with primary delayed suture four days after the primary surgical treatment (traditional approach. Fibrin glue with antibiotics was introduced in seven wounds during the primary surgical treatment and primary delayed suture was done after 24 h. The macroscopic appearance and the clinical assessment of the wound were done during the primary surgical treatment and during its revision after 24 h, as well as histopathological findings at the days 4 and 7 after wounding. Results. Gunshot wounds caused by the automatic rifle M70AB2 (AK-47 7.62 mm, and treated with fibrin glue with antibiotics after primary surgical management, were closed with primary delayed suture after 24 h. In further wound evolution there were no complications. Conclusion. Uncomplicated soft-tissue wounds caused by an automatic M70AB2 rifle may be closed primarily with delayed suture without the risk of developing complications if on revision, 24 h after primary surgery, there were no present necrotic tissues, hematoma, and any signs of infection when fibrin glue with antibiotics

A Bayesian network approach to the database search problem in criminal proceedings

Science.gov (United States)

2012-01-01

Background The ‘database search problem’, that is, the strengthening of a case - in terms of probative value - against an individual who is found as a result of a database search, has been approached during the last two decades with substantial mathematical analyses, accompanied by lively debate and centrally opposing conclusions. This represents a challenging obstacle in teaching but also hinders a balanced and coherent discussion of the topic within the wider scientific and legal community. This paper revisits and tracks the associated mathematical analyses in terms of Bayesian networks. Their derivation and discussion for capturing probabilistic arguments that explain the database search problem are outlined in detail. The resulting Bayesian networks offer a distinct view on the main debated issues, along with further clarity. Methods As a general framework for representing and analyzing formal arguments in probabilistic reasoning about uncertain target propositions (that is, whether or not a given individual is the source of a crime stain), this paper relies on graphical probability models, in particular, Bayesian networks. This graphical probability modeling approach is used to capture, within a single model, a series of key variables, such as the number of individuals in a database, the size of the population of potential crime stain sources, and the rarity of the corresponding analytical characteristics in a relevant population. Results This paper demonstrates the feasibility of deriving Bayesian network structures for analyzing, representing, and tracking the database search problem. The output of the proposed models can be shown to agree with existing but exclusively formulaic approaches. Conclusions The proposed Bayesian networks allow one to capture and analyze the currently most well-supported but reputedly counter-intuitive and difficult solution to the database search problem in a way that goes beyond the traditional, purely formulaic expressions

Optimal speech motor control and token-to-token variability: a Bayesian modeling approach.

Science.gov (United States)

Patri, Jean-François; Diard, Julien; Perrier, Pascal

2015-12-01

The remarkable capacity of the speech motor system to adapt to various speech conditions is due to an excess of degrees of freedom, which enables producing similar acoustical properties with different sets of control strategies. To explain how the central nervous system selects one of the possible strategies, a common approach, in line with optimal motor control theories, is to model speech motor planning as the solution of an optimality problem based on cost functions. Despite the success of this approach, one of its drawbacks is the intrinsic contradiction between the concept of optimality and the observed experimental intra-speaker token-to-token variability. The present paper proposes an alternative approach by formulating feedforward optimal control in a probabilistic Bayesian modeling framework. This is illustrated by controlling a biomechanical model of the vocal tract for speech production and by comparing it with an existing optimal control model (GEPPETO). The essential elements of this optimal control model are presented first. From them the Bayesian model is constructed in a progressive way. Performance of the Bayesian model is evaluated based on computer simulations and compared to the optimal control model. This approach is shown to be appropriate for solving the speech planning problem while accounting for variability in a principled way.

Remotely Sensed Monitoring of Small Reservoir Dynamics: A Bayesian Approach

Directory of Open Access Journals (Sweden)

Dirk Eilander

2014-01-01

Full Text Available Multipurpose small reservoirs are important for livelihoods in rural semi-arid regions. To manage and plan these reservoirs and to assess their hydrological impact at a river basin scale, it is important to monitor their water storage dynamics. This paper introduces a Bayesian approach for monitoring small reservoirs with radar satellite images. The newly developed growing Bayesian classifier has a high degree of automation, can readily be extended with auxiliary information and reduces the confusion error to the land-water boundary pixels. A case study has been performed in the Upper East Region of Ghana, based on Radarsat-2 data from November 2012 until April 2013. Results show that the growing Bayesian classifier can deal with the spatial and temporal variability in synthetic aperture radar (SAR backscatter intensities from small reservoirs. Due to its ability to incorporate auxiliary information, the algorithm is able to delineate open water from SAR imagery with a low land-water contrast in the case of wind-induced Bragg scattering or limited vegetation on the land surrounding a small reservoir.

A Bayesian approach for parameter estimation and prediction using a computationally intensive model

International Nuclear Information System (INIS)

Higdon, Dave; McDonnell, Jordan D; Schunck, Nicolas; Sarich, Jason; Wild, Stefan M

2015-01-01

Bayesian methods have been successful in quantifying uncertainty in physics-based problems in parameter estimation and prediction. In these cases, physical measurements y are modeled as the best fit of a physics-based model η(θ), where θ denotes the uncertain, best input setting. Hence the statistical model is of the form y=η(θ)+ϵ, where ϵ accounts for measurement, and possibly other, error sources. When nonlinearity is present in η(⋅), the resulting posterior distribution for the unknown parameters in the Bayesian formulation is typically complex and nonstandard, requiring computationally demanding computational approaches such as Markov chain Monte Carlo (MCMC) to produce multivariate draws from the posterior. Although generally applicable, MCMC requires thousands (or even millions) of evaluations of the physics model η(⋅). This requirement is problematic if the model takes hours or days to evaluate. To overcome this computational bottleneck, we present an approach adapted from Bayesian model calibration. This approach combines output from an ensemble of computational model runs with physical measurements, within a statistical formulation, to carry out inference. A key component of this approach is a statistical response surface, or emulator, estimated from the ensemble of model runs. We demonstrate this approach with a case study in estimating parameters for a density functional theory model, using experimental mass/binding energy measurements from a collection of atomic nuclei. We also demonstrate how this approach produces uncertainties in predictions for recent mass measurements obtained at Argonne National Laboratory. (paper)

Towards GLUE2 evolution of the computing element information model

CERN Document Server

Andreozzi, S; Field, L; Kónya, B

2008-01-01

A key advantage of Grid systems is the ability to share heterogeneous resources and services between traditional administrative and organizational domains. This ability enables virtual pools of resources to be created and assigned to groups of users. Resource awareness, the capability of users or user agents to have knowledge about the existence and state of resources, is required in order utilize the resource. This awareness requires a description of the services and resources typically defined via a community-agreed information model. One of the most popular information models, used by a number of Grid infrastructures, is the GLUE Schema, which provides a common language for describing Grid resources. Other approaches exist, however they follow different modeling strategies. The presence of different flavors of information models for Grid resources is a barrier for enabling inter-Grid interoperability. In order to solve this problem, the GLUE Working Group in the context of the Open Grid Forum was started. ...

Effectiveness of Fibrin Glue in Adherence of Skin Graft.

Science.gov (United States)

Reddy, Konda Sireesha; Chittoria, Ravi Kumar; Babu, Preethitha; Marimuthu, Senthil Kumaran; Kumar, Sudhanva Hemanth; Subbarayan, Elan Kumar; Chavan, Vinayak; Mohapatra, Devi Prasad; Sivakumar, Dinesh Kumar; Friji, M T

2017-01-01

Graft fixation is important for graft take. Fibrin glue has been proposed as an ideal material, because of its human origin and it provides firm adhesion in seconds or minutes. To evaluate the efficiency of fibrin glue, in increasing the take of skin graft. Assessment includes surgical time taken for graft fixation, haematoma/seroma formation, engraftment and wound closure by day 14. The study is an observational prospective study conducted in the Department of Plastic Surgery, Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry, from January 2016 to June 2016. Sixteen patients who underwent split skin grafting were assessed during the study period. Fibrin glue was used on the recipient bed before grafting. Better haemostasis and graft adhesion, with a significant reduction of surgical time, were noted. The safety profile of fibrin glue was excellent as indicated by the lack of any related serious adverse experiences. These findings demonstrate that it is safe and effective for attachment of skin grafts, with outcomes at least as good as conventional methods.

SHORT-TERM SOLAR FLARE LEVEL PREDICTION USING A BAYESIAN NETWORK APPROACH

International Nuclear Information System (INIS)

Yu Daren; Huang Xin; Hu Qinghua; Zhou Rui; Wang Huaning; Cui Yanmei

2010-01-01

A Bayesian network approach for short-term solar flare level prediction has been proposed based on three sequences of photospheric magnetic field parameters extracted from Solar and Heliospheric Observatory/Michelson Doppler Imager longitudinal magnetograms. The magnetic measures, the maximum horizontal gradient, the length of neutral line, and the number of singular points do not have determinate relationships with solar flares, so the solar flare level prediction is considered as an uncertainty reasoning process modeled by the Bayesian network. The qualitative network structure which describes conditional independent relationships among magnetic field parameters and the quantitative conditional probability tables which determine the probabilistic values for each variable are learned from the data set. Seven sequential features-the maximum, the mean, the root mean square, the standard deviation, the shape factor, the crest factor, and the pulse factor-are extracted to reduce the dimensions of the raw sequences. Two Bayesian network models are built using raw sequential data (BN R ) and feature extracted data (BN F ), respectively. The explanations of these models are consistent with physical analyses of experts. The performances of the BN R and the BN F appear comparable with other methods. More importantly, the comprehensibility of the Bayesian network models is better than other methods.

Bayesian benefits with JASP

NARCIS (Netherlands)

Marsman, M.; Wagenmakers, E.-J.

2017-01-01

We illustrate the Bayesian approach to data analysis using the newly developed statistical software program JASP. With JASP, researchers are able to take advantage of the benefits that the Bayesian framework has to offer in terms of parameter estimation and hypothesis testing. The Bayesian

Bayesian-based estimation of acoustic surface impedance: Finite difference frequency domain approach.

Science.gov (United States)

Bockman, Alexander; Fackler, Cameron; Xiang, Ning

2015-04-01

Acoustic performance for an interior requires an accurate description of the boundary materials' surface acoustic impedance. Analytical methods may be applied to a small class of test geometries, but inverse numerical methods provide greater flexibility. The parameter estimation problem requires minimizing prediction vice observed acoustic field pressure. The Bayesian-network sampling approach presented here mitigates other methods' susceptibility to noise inherent to the experiment, model, and numerics. A geometry agnostic method is developed here and its parameter estimation performance is demonstrated for an air-backed micro-perforated panel in an impedance tube. Good agreement is found with predictions from the ISO standard two-microphone, impedance-tube method, and a theoretical model for the material. Data by-products exclusive to a Bayesian approach are analyzed to assess sensitivity of the method to nuisance parameters.

Capturing changes in flood risk with Bayesian approaches for flood damage assessment

Science.gov (United States)

Vogel, Kristin; Schröter, Kai; Kreibich, Heidi; Thieken, Annegret; Müller, Meike; Sieg, Tobias; Laudan, Jonas; Kienzler, Sarah; Weise, Laura; Merz, Bruno; Scherbaum, Frank

2016-04-01

Flood risk is a function of hazard as well as of exposure and vulnerability. All three components are under change over space and time and have to be considered for reliable damage estimations and risk analyses, since this is the basis for an efficient, adaptable risk management. Hitherto, models for estimating flood damage are comparatively simple and cannot sufficiently account for changing conditions. The Bayesian network approach allows for a multivariate modeling of complex systems without relying on expert knowledge about physical constraints. In a Bayesian network each model component is considered to be a random variable. The way of interactions between those variables can be learned from observations or be defined by expert knowledge. Even a combination of both is possible. Moreover, the probabilistic framework captures uncertainties related to the prediction and provides a probability distribution for the damage instead of a point estimate. The graphical representation of Bayesian networks helps to study the change of probabilities for changing circumstances and may thus simplify the communication between scientists and public authorities. In the framework of the DFG-Research Training Group "NatRiskChange" we aim to develop Bayesian networks for flood damage and vulnerability assessments of residential buildings and companies under changing conditions. A Bayesian network learned from data, collected over the last 15 years in flooded regions in the Elbe and Danube catchments (Germany), reveals the impact of many variables like building characteristics, precaution and warning situation on flood damage to residential buildings. While the handling of incomplete and hybrid (discrete mixed with continuous) data are the most challenging issues in the study on residential buildings, a similar study, that focuses on the vulnerability of small to medium sized companies, bears new challenges. Relying on a much smaller data set for the determination of the model

Pedestrian fatality and natural light: Evidence from South Africa using a Bayesian approach

CSIR Research Space (South Africa)

Das, Sonali

2014-02-01

Full Text Available from South Africa using a Bayesian approach, Econ.Model. (2013), http://dx.doi.org/10.1016/j.econmod.2013.11.037 U N C O R R E C TE D P R O O F 95 countries the most vulnerable group is the economically active sector 96 namely working adults... using a Bayesian approach, Econ.Model. (2013), http://dx.doi.org/10.1016/j.econmod.2013.11.037 U N C O R R E C TE D P R O O F 157 relates the chi-square and Poisson distributions (Johnson and Kotz, 158 1969; Stuart and Ord, 1994) is given by: l ¼ χ2a 2...

Life cycle reliability assessment of new products—A Bayesian model updating approach

International Nuclear Information System (INIS)

Peng, Weiwen; Huang, Hong-Zhong; Li, Yanfeng; Zuo, Ming J.; Xie, Min

2013-01-01

The rapidly increasing pace and continuously evolving reliability requirements of new products have made life cycle reliability assessment of new products an imperative yet difficult work. While much work has been done to separately estimate reliability of new products in specific stages, a gap exists in carrying out life cycle reliability assessment throughout all life cycle stages. We present a Bayesian model updating approach (BMUA) for life cycle reliability assessment of new products. Novel features of this approach are the development of Bayesian information toolkits by separately including “reliability improvement factor” and “information fusion factor”, which allow the integration of subjective information in a specific life cycle stage and the transition of integrated information between adjacent life cycle stages. They lead to the unique characteristics of the BMUA in which information generated throughout life cycle stages are integrated coherently. To illustrate the approach, an application to the life cycle reliability assessment of a newly developed Gantry Machining Center is shown

Bayesian approaches for detecting significant deterioration

International Nuclear Information System (INIS)

Roed, Willy; Aven, Terje

2009-01-01

Risk indicators can provide useful input to risk management processes and are given increased attention in the Norwegian petroleum industry. Examples include indicators expressing the proportion of test failures of safety and barrier systems. Such indicators give valuable information about the performance of the systems and provide a basis for trend evaluations. Early warning of a possible deterioration is essential due to the importance of the systems in focus, but what should be the basis for the warning criterion? This paper presents and discusses several Bayesian approaches for the establishment of a warning criterion to disclose significant deterioration. The Norwegian petroleum industry is the starting point for this paper, but the study is relevant for other application areas as well

Observations on dimensional changes of sized canvas based on glue temperature

DEFF Research Database (Denmark)

Krarup Andersen, Cecil

2008-01-01

The aim of this study was to explore dimensional changes caused by water on sized canvas. Samples of new linen canvas were mounted on a rig for biaxial tensioning, holding a constant stress of 100 N/m in both weave directions. The samples were then sized with respectively warm fluent glue (45 °C......) or a cold gel (20 °C), both consisting of a 5 percent sheepskin glue extracted from parchment clippings. The warm glue was absorbed into the canvas structure, whereas the cold gel mostly stayed as a discrete layer on the canvas. Twice the amount of glue was therefore needed for the warm sizing. When...... of size layers during application is important to the dimensional reaction of a canvas painting that is exposed to water....

A Bayesian approach to estimate sensible and latent heat over vegetated land surface

Directory of Open Access Journals (Sweden)

C. van der Tol

2009-06-01

Full Text Available Sensible and latent heat fluxes are often calculated from bulk transfer equations combined with the energy balance. For spatial estimates of these fluxes, a combination of remotely sensed and standard meteorological data from weather stations is used. The success of this approach depends on the accuracy of the input data and on the accuracy of two variables in particular: aerodynamic and surface conductance. This paper presents a Bayesian approach to improve estimates of sensible and latent heat fluxes by using a priori estimates of aerodynamic and surface conductance alongside remote measurements of surface temperature. The method is validated for time series of half-hourly measurements in a fully grown maize field, a vineyard and a forest. It is shown that the Bayesian approach yields more accurate estimates of sensible and latent heat flux than traditional methods.

Upper limit for Poisson variable incorporating systematic uncertainties by Bayesian approach

International Nuclear Information System (INIS)

Zhu, Yongsheng

2007-01-01

To calculate the upper limit for the Poisson observable at given confidence level with inclusion of systematic uncertainties in background expectation and signal efficiency, formulations have been established along the line of Bayesian approach. A FORTRAN program, BPULE, has been developed to implement the upper limit calculation

Air kerma rate estimation by means of in-situ gamma spectrometry: A Bayesian approach

International Nuclear Information System (INIS)

Cabal, Gonzalo; Kluson, Jaroslav

2008-01-01

Full text: Bayesian inference is used to determine the Air Kerma Rate based on a set of in situ environmental gamma spectra measurements performed with a NaI(Tl) scintillation detector. A natural advantage of such approach is the possibility to quantify uncertainty not only in the Air Kerma Rate estimation but also for the gamma spectra which is unfolded within the procedure. The measurements were performed using a 3'' x 3'' NaI(Tl) scintillation detector. The response matrices of such detection system were calculated using a Monte Carlo code. For the calculations of the spectra as well as the Air Kerma Rate the WinBugs program was used. WinBugs is a dedicated software for Bayesian inference using Monte Carlo Markov chain methods (MCMC). The results of such calculations are shown and compared with other non-Bayesian approachs such as the Scofield-Gold iterative method and the Maximum Entropy Method

Bayesian Multi-Energy Computed Tomography reconstruction approaches based on decomposition models

International Nuclear Information System (INIS)

Cai, Caifang

2013-01-01

Multi-Energy Computed Tomography (MECT) makes it possible to get multiple fractions of basis materials without segmentation. In medical application, one is the soft-tissue equivalent water fraction and the other is the hard-matter equivalent bone fraction. Practical MECT measurements are usually obtained with polychromatic X-ray beams. Existing reconstruction approaches based on linear forward models without counting the beam poly-chromaticity fail to estimate the correct decomposition fractions and result in Beam-Hardening Artifacts (BHA). The existing BHA correction approaches either need to refer to calibration measurements or suffer from the noise amplification caused by the negative-log pre-processing and the water and bone separation problem. To overcome these problems, statistical DECT reconstruction approaches based on non-linear forward models counting the beam poly-chromaticity show great potential for giving accurate fraction images.This work proposes a full-spectral Bayesian reconstruction approach which allows the reconstruction of high quality fraction images from ordinary polychromatic measurements. This approach is based on a Gaussian noise model with unknown variance assigned directly to the projections without taking negative-log. Referring to Bayesian inferences, the decomposition fractions and observation variance are estimated by using the joint Maximum A Posteriori (MAP) estimation method. Subject to an adaptive prior model assigned to the variance, the joint estimation problem is then simplified into a single estimation problem. It transforms the joint MAP estimation problem into a minimization problem with a non-quadratic cost function. To solve it, the use of a monotone Conjugate Gradient (CG) algorithm with suboptimal descent steps is proposed.The performances of the proposed approach are analyzed with both simulated and experimental data. The results show that the proposed Bayesian approach is robust to noise and materials. It is also

Estimation of the order of an autoregressive time series: a Bayesian approach

International Nuclear Information System (INIS)

Robb, L.J.

1980-01-01

Finite-order autoregressive models for time series are often used for prediction and other inferences. Given the order of the model, the parameters of the models can be estimated by least-squares, maximum-likelihood, or Yule-Walker method. The basic problem is estimating the order of the model. The problem of autoregressive order estimation is placed in a Bayesian framework. This approach illustrates how the Bayesian method brings the numerous aspects of the problem together into a coherent structure. A joint prior probability density is proposed for the order, the partial autocorrelation coefficients, and the variance; and the marginal posterior probability distribution for the order, given the data, is obtained. It is noted that the value with maximum posterior probability is the Bayes estimate of the order with respect to a particular loss function. The asymptotic posterior distribution of the order is also given. In conclusion, Wolfer's sunspot data as well as simulated data corresponding to several autoregressive models are analyzed according to Akaike's method and the Bayesian method. Both methods are observed to perform quite well, although the Bayesian method was clearly superior, in most cases

Merging Digital Surface Models Implementing Bayesian Approaches

Science.gov (United States)

Sadeq, H.; Drummond, J.; Li, Z.

2016-06-01

In this research different DSMs from different sources have been merged. The merging is based on a probabilistic model using a Bayesian Approach. The implemented data have been sourced from very high resolution satellite imagery sensors (e.g. WorldView-1 and Pleiades). It is deemed preferable to use a Bayesian Approach when the data obtained from the sensors are limited and it is difficult to obtain many measurements or it would be very costly, thus the problem of the lack of data can be solved by introducing a priori estimations of data. To infer the prior data, it is assumed that the roofs of the buildings are specified as smooth, and for that purpose local entropy has been implemented. In addition to the a priori estimations, GNSS RTK measurements have been collected in the field which are used as check points to assess the quality of the DSMs and to validate the merging result. The model has been applied in the West-End of Glasgow containing different kinds of buildings, such as flat roofed and hipped roofed buildings. Both quantitative and qualitative methods have been employed to validate the merged DSM. The validation results have shown that the model was successfully able to improve the quality of the DSMs and improving some characteristics such as the roof surfaces, which consequently led to better representations. In addition to that, the developed model has been compared with the well established Maximum Likelihood model and showed similar quantitative statistical results and better qualitative results. Although the proposed model has been applied on DSMs that were derived from satellite imagery, it can be applied to any other sourced DSMs.

MERGING DIGITAL SURFACE MODELS IMPLEMENTING BAYESIAN APPROACHES

Directory of Open Access Journals (Sweden)

H. Sadeq

2016-06-01

Full Text Available In this research different DSMs from different sources have been merged. The merging is based on a probabilistic model using a Bayesian Approach. The implemented data have been sourced from very high resolution satellite imagery sensors (e.g. WorldView-1 and Pleiades. It is deemed preferable to use a Bayesian Approach when the data obtained from the sensors are limited and it is difficult to obtain many measurements or it would be very costly, thus the problem of the lack of data can be solved by introducing a priori estimations of data. To infer the prior data, it is assumed that the roofs of the buildings are specified as smooth, and for that purpose local entropy has been implemented. In addition to the a priori estimations, GNSS RTK measurements have been collected in the field which are used as check points to assess the quality of the DSMs and to validate the merging result. The model has been applied in the West-End of Glasgow containing different kinds of buildings, such as flat roofed and hipped roofed buildings. Both quantitative and qualitative methods have been employed to validate the merged DSM. The validation results have shown that the model was successfully able to improve the quality of the DSMs and improving some characteristics such as the roof surfaces, which consequently led to better representations. In addition to that, the developed model has been compared with the well established Maximum Likelihood model and showed similar quantitative statistical results and better qualitative results. Although the proposed model has been applied on DSMs that were derived from satellite imagery, it can be applied to any other sourced DSMs.

A Bayesian Approach for Sensor Optimisation in Impact Identification

Directory of Open Access Journals (Sweden)

Vincenzo Mallardo

2016-11-01

Full Text Available This paper presents a Bayesian approach for optimizing the position of sensors aimed at impact identification in composite structures under operational conditions. The uncertainty in the sensor data has been represented by statistical distributions of the recorded signals. An optimisation strategy based on the genetic algorithm is proposed to find the best sensor combination aimed at locating impacts on composite structures. A Bayesian-based objective function is adopted in the optimisation procedure as an indicator of the performance of meta-models developed for different sensor combinations to locate various impact events. To represent a real structure under operational load and to increase the reliability of the Structural Health Monitoring (SHM system, the probability of malfunctioning sensors is included in the optimisation. The reliability and the robustness of the procedure is tested with experimental and numerical examples. Finally, the proposed optimisation algorithm is applied to a composite stiffened panel for both the uniform and non-uniform probability of impact occurrence.

A Robust Bayesian Approach for Structural Equation Models with Missing Data

Science.gov (United States)

Lee, Sik-Yum; Xia, Ye-Mao

2008-01-01

In this paper, normal/independent distributions, including but not limited to the multivariate t distribution, the multivariate contaminated distribution, and the multivariate slash distribution, are used to develop a robust Bayesian approach for analyzing structural equation models with complete or missing data. In the context of a nonlinear…

A Bayesian Approach to the Overlap Analysis of Epidemiologically Linked Traits.

Science.gov (United States)

Asimit, Jennifer L; Panoutsopoulou, Kalliope; Wheeler, Eleanor; Berndt, Sonja I; Cordell, Heather J; Morris, Andrew P; Zeggini, Eleftheria; Barroso, Inês

2015-12-01

Diseases often cooccur in individuals more often than expected by chance, and may be explained by shared underlying genetic etiology. A common approach to genetic overlap analyses is to use summary genome-wide association study data to identify single-nucleotide polymorphisms (SNPs) that are associated with multiple traits at a selected P-value threshold. However, P-values do not account for differences in power, whereas Bayes' factors (BFs) do, and may be approximated using summary statistics. We use simulation studies to compare the power of frequentist and Bayesian approaches with overlap analyses, and to decide on appropriate thresholds for comparison between the two methods. It is empirically illustrated that BFs have the advantage over P-values of a decreasing type I error rate as study size increases for single-disease associations. Consequently, the overlap analysis of traits from different-sized studies encounters issues in fair P-value threshold selection, whereas BFs are adjusted automatically. Extensive simulations show that Bayesian overlap analyses tend to have higher power than those that assess association strength with P-values, particularly in low-power scenarios. Calibration tables between BFs and P-values are provided for a range of sample sizes, as well as an approximation approach for sample sizes that are not in the calibration table. Although P-values are sometimes thought more intuitive, these tables assist in removing the opaqueness of Bayesian thresholds and may also be used in the selection of a BF threshold to meet a certain type I error rate. An application of our methods is used to identify variants associated with both obesity and osteoarthritis. © 2015 The Authors. *Genetic Epidemiology published by Wiley Periodicals, Inc.

Bayesian Reliability Estimation for Deteriorating Systems with Limited Samples Using the Maximum Entropy Approach

OpenAIRE

Xiao, Ning-Cong; Li, Yan-Feng; Wang, Zhonglai; Peng, Weiwen; Huang, Hong-Zhong

2013-01-01

In this paper the combinations of maximum entropy method and Bayesian inference for reliability assessment of deteriorating system is proposed. Due to various uncertainties, less data and incomplete information, system parameters usually cannot be determined precisely. These uncertainty parameters can be modeled by fuzzy sets theory and the Bayesian inference which have been proved to be useful for deteriorating systems under small sample sizes. The maximum entropy approach can be used to cal...

The subjectivity of scientists and the Bayesian approach

CERN Document Server

Press, James S

2001-01-01

Comparing and contrasting the reality of subjectivity in the work of history's great scientists and the modern Bayesian approach to statistical analysisScientists and researchers are taught to analyze their data from an objective point of view, allowing the data to speak for themselves rather than assigning them meaning based on expectations or opinions. But scientists have never behaved fully objectively. Throughout history, some of our greatest scientific minds have relied on intuition, hunches, and personal beliefs to make sense of empirical data-and these subjective influences have often a

A new method for E-government procurement using collaborative filtering and Bayesian approach.

Science.gov (United States)

Zhang, Shuai; Xi, Chengyu; Wang, Yan; Zhang, Wenyu; Chen, Yanhong

2013-01-01

Nowadays, as the Internet services increase faster than ever before, government systems are reinvented as E-government services. Therefore, government procurement sectors have to face challenges brought by the explosion of service information. This paper presents a novel method for E-government procurement (eGP) to search for the optimal procurement scheme (OPS). Item-based collaborative filtering and Bayesian approach are used to evaluate and select the candidate services to get the top-M recommendations such that the involved computation load can be alleviated. A trapezoidal fuzzy number similarity algorithm is applied to support the item-based collaborative filtering and Bayesian approach, since some of the services' attributes can be hardly expressed as certain and static values but only be easily represented as fuzzy values. A prototype system is built and validated with an illustrative example from eGP to confirm the feasibility of our approach.

A New Method for E-Government Procurement Using Collaborative Filtering and Bayesian Approach

Directory of Open Access Journals (Sweden)

Shuai Zhang

2013-01-01

Full Text Available Nowadays, as the Internet services increase faster than ever before, government systems are reinvented as E-government services. Therefore, government procurement sectors have to face challenges brought by the explosion of service information. This paper presents a novel method for E-government procurement (eGP to search for the optimal procurement scheme (OPS. Item-based collaborative filtering and Bayesian approach are used to evaluate and select the candidate services to get the top-M recommendations such that the involved computation load can be alleviated. A trapezoidal fuzzy number similarity algorithm is applied to support the item-based collaborative filtering and Bayesian approach, since some of the services’ attributes can be hardly expressed as certain and static values but only be easily represented as fuzzy values. A prototype system is built and validated with an illustrative example from eGP to confirm the feasibility of our approach.

Introduction to Bayesian statistics

CERN Document Server

Bolstad, William M

2017-01-01

There is a strong upsurge in the use of Bayesian methods in applied statistical analysis, yet most introductory statistics texts only present frequentist methods. Bayesian statistics has many important advantages that students should learn about if they are going into fields where statistics will be used. In this Third Edition, four newly-added chapters address topics that reflect the rapid advances in the field of Bayesian staistics. The author continues to provide a Bayesian treatment of introductory statistical topics, such as scientific data gathering, discrete random variables, robust Bayesian methods, and Bayesian approaches to inferenfe cfor discrete random variables, bionomial proprotion, Poisson, normal mean, and simple linear regression. In addition, newly-developing topics in the field are presented in four new chapters: Bayesian inference with unknown mean and variance; Bayesian inference for Multivariate Normal mean vector; Bayesian inference for Multiple Linear RegressionModel; and Computati...

A nonparametric Bayesian approach for genetic evaluation in ...

African Journals Online (AJOL)

South African Journal of Animal Science ... the Bayesian and Classical models, a Bayesian procedure is provided which allows these random ... data from the Elsenburg Dormer sheep stud and data from a simulation experiment are utilized. >

Effect of MHEC on evaporation and hydration characteristics of glue mortar

NARCIS (Netherlands)

Faiyas, A.P.A.; Erich, S.J.F.; Huinink, H.P.; Adan, O.C.G; Nijland, T.G.

2016-01-01

The influence of methylhydroxyethylcellulose (MHEC) on both moisture distribution and hydration characteristics of glue mortar using nuclear magnetic resonance imaging (NMR) is investigated. MHEC is added to glue mortar in order to control the drying rate by increasing the open time. Besides drying,

Application of Bayesian approach to estimate average level spacing

International Nuclear Information System (INIS)

Huang Zhongfu; Zhao Zhixiang

1991-01-01

A method to estimate average level spacing from a set of resolved resonance parameters by using Bayesian approach is given. Using the information given in the distributions of both levels spacing and neutron width, the level missing in measured sample can be corrected more precisely so that better estimate for average level spacing can be obtained by this method. The calculation of s-wave resonance has been done and comparison with other work was carried out

Bayesian Mediation Analysis

Science.gov (United States)

Yuan, Ying; MacKinnon, David P.

2009-01-01

In this article, we propose Bayesian analysis of mediation effects. Compared with conventional frequentist mediation analysis, the Bayesian approach has several advantages. First, it allows researchers to incorporate prior information into the mediation analysis, thus potentially improving the efficiency of estimates. Second, under the Bayesian…

Fibrin glue as a protective tool for microanastomoses in limb reconstructive surgery

Directory of Open Access Journals (Sweden)

Langer, Stefan

2015-12-01

Full Text Available Aim: Fibrin glue becomes a more and more routinely used tool for stabilization of microanastomoses and nerve repair. This paper summarizes the technical properties and advantages of its use in a wide variety of microsurgical contexts, and includes an exemplary limb reconstructive case.Patients and methods: A total of 131 patients who had undergone elective and emergency microsurgery mainly of the limbs were retrospectively analyzed, as was the use of free flaps.Results: The use of fibrin glue allows for proper positioning of anastomoses and repaired nerves. No torsion of the pedicle could be seen. The flap survival rated >94%. The fibrin glue could stay in place in >99%. In the rare case of revision, the fibrin glue could easily be removed without damaging the region of the microanastomosis.Conclusion: Fibrin glue should not be used to repair insufficient, i.e., leaking anastomoses, but it does protect the site of anastomosis from tissue and fluid pressure. It prevents the pedickle from torsion and its use facilitates relocation of the microanastomoses in cases of revision surgery.

How to interpret the results of medical time series data analysis: Classical statistical approaches versus dynamic Bayesian network modeling.

Science.gov (United States)

Onisko, Agnieszka; Druzdzel, Marek J; Austin, R Marshall

2016-01-01

Classical statistics is a well-established approach in the analysis of medical data. While the medical community seems to be familiar with the concept of a statistical analysis and its interpretation, the Bayesian approach, argued by many of its proponents to be superior to the classical frequentist approach, is still not well-recognized in the analysis of medical data. The goal of this study is to encourage data analysts to use the Bayesian approach, such as modeling with graphical probabilistic networks, as an insightful alternative to classical statistical analysis of medical data. This paper offers a comparison of two approaches to analysis of medical time series data: (1) classical statistical approach, such as the Kaplan-Meier estimator and the Cox proportional hazards regression model, and (2) dynamic Bayesian network modeling. Our comparison is based on time series cervical cancer screening data collected at Magee-Womens Hospital, University of Pittsburgh Medical Center over 10 years. The main outcomes of our comparison are cervical cancer risk assessments produced by the three approaches. However, our analysis discusses also several aspects of the comparison, such as modeling assumptions, model building, dealing with incomplete data, individualized risk assessment, results interpretation, and model validation. Our study shows that the Bayesian approach is (1) much more flexible in terms of modeling effort, and (2) it offers an individualized risk assessment, which is more cumbersome for classical statistical approaches.

[Overcoming the limitations of the descriptive and categorical approaches in psychiatric diagnosis: a proposal based on Bayesian networks].

Science.gov (United States)

Sorias, Soli

2015-01-01

Efforts to overcome the problems of descriptive and categorical approaches have not yielded results. In the present article, psychiatric diagnosis using Bayesian networks is proposed. Instead of a yes/no decision, Bayesian networks give the probability of diagnostic category inclusion, thereby yielding both a graded, i.e., dimensional diagnosis, and a value of the certainty of the diagnosis. With the use of Bayesian networks in the diagnosis of mental disorders, information about etiology, associated features, treatment outcome, and laboratory results may be used in addition to clinical signs and symptoms, with each of these factors contributing proportionally to their own specificity and sensitivity. Furthermore, a diagnosis (albeit one with a lower probability) can be made even with incomplete, uncertain, or partially erroneous information, and patients whose symptoms are below the diagnostic threshold can be evaluated. Lastly, there is no need of NOS or "unspecified" categories, and comorbid disorders become different dimensions of the diagnostic evaluation. Bayesian diagnoses allow the preservation of current categories and assessment methods, and may be used concurrently with criteria-based diagnoses. Users need not put in extra effort except to collect more comprehensive information. Unlike the Research Domain Criteria (RDoC) project, the Bayesian approach neither increases the diagnostic validity of existing categories nor explains the pathophysiological mechanisms of mental disorders. It, however, can be readily integrated to present classification systems. Therefore, the Bayesian approach may be an intermediate phase between criteria-based diagnosis and the RDoC ideal.

Bayesian flood forecasting methods: A review

Science.gov (United States)

Han, Shasha; Coulibaly, Paulin

2017-08-01

Over the past few decades, floods have been seen as one of the most common and largely distributed natural disasters in the world. If floods could be accurately forecasted in advance, then their negative impacts could be greatly minimized. It is widely recognized that quantification and reduction of uncertainty associated with the hydrologic forecast is of great importance for flood estimation and rational decision making. Bayesian forecasting system (BFS) offers an ideal theoretic framework for uncertainty quantification that can be developed for probabilistic flood forecasting via any deterministic hydrologic model. It provides suitable theoretical structure, empirically validated models and reasonable analytic-numerical computation method, and can be developed into various Bayesian forecasting approaches. This paper presents a comprehensive review on Bayesian forecasting approaches applied in flood forecasting from 1999 till now. The review starts with an overview of fundamentals of BFS and recent advances in BFS, followed with BFS application in river stage forecasting and real-time flood forecasting, then move to a critical analysis by evaluating advantages and limitations of Bayesian forecasting methods and other predictive uncertainty assessment approaches in flood forecasting, and finally discusses the future research direction in Bayesian flood forecasting. Results show that the Bayesian flood forecasting approach is an effective and advanced way for flood estimation, it considers all sources of uncertainties and produces a predictive distribution of the river stage, river discharge or runoff, thus gives more accurate and reliable flood forecasts. Some emerging Bayesian forecasting methods (e.g. ensemble Bayesian forecasting system, Bayesian multi-model combination) were shown to overcome limitations of single model or fixed model weight and effectively reduce predictive uncertainty. In recent years, various Bayesian flood forecasting approaches have been

Control of DNA hybridization by photoswitchable molecular glue.

Science.gov (United States)

Dohno, Chikara; Nakatani, Kazuhiko

2011-12-01

Hybridization of DNA is one of the most intriguing events in molecular recognition and is essential for living matter to inherit life beyond generations. In addition to the function of DNA as genetic material, DNA hybridization is a key to control the function of DNA-based materials in nanoscience. Since the hybridization of two single stranded DNAs is a thermodynamically favorable process, dissociation of the once formed DNA duplex is normally unattainable under isothermal conditions. As the progress of DNA-based nanoscience, methodology to control the DNA hybridization process has become increasingly important. Besides many reports using the chemically modified DNA for the regulation of hybridization, we focused our attention on the use of a small ligand as the molecular glue for the DNA. In 2001, we reported the first designed molecule that strongly and specifically bound to the mismatched base pairs in double stranded DNA. Further studies on the mismatch binding molecules provided us a key discovery of a novel mode of the binding of a mismatch binding ligand that induced the base flipping. With these findings we proposed the concept of molecular glue for DNA for the unidirectional control of DNA hybridization and, eventually photoswitchable molecular glue for DNA, which enabled the bidirectional control of hybridization under photoirradiation. In this tutorial review, we describe in detail how we integrated the mismatch binding ligand into photoswitchable molecular glue for DNA, and the application and perspective in DNA-based nanoscience.

A Bayesian Approach to Real-Time Earthquake Phase Association

Science.gov (United States)

Benz, H.; Johnson, C. E.; Earle, P. S.; Patton, J. M.

2014-12-01

Real-time location of seismic events requires a robust and extremely efficient means of associating and identifying seismic phases with hypothetical sources. An association algorithm converts a series of phase arrival times into a catalog of earthquake hypocenters. The classical approach based on time-space stacking of the locus of possible hypocenters for each phase arrival using the principal of acoustic reciprocity has been in use now for many years. One of the most significant problems that has emerged over time with this approach is related to the extreme variations in seismic station density throughout the global seismic network. To address this problem we have developed a novel, Bayesian association algorithm, which looks at the association problem as a dynamically evolving complex system of "many to many relationships". While the end result must be an array of one to many relations (one earthquake, many phases), during the association process the situation is quite different. Both the evolving possible hypocenters and the relationships between phases and all nascent hypocenters is many to many (many earthquakes, many phases). The computational framework we are using to address this is a responsive, NoSQL graph database where the earthquake-phase associations are represented as intersecting Bayesian Learning Networks. The approach directly addresses the network inhomogeneity issue while at the same time allowing the inclusion of other kinds of data (e.g., seismic beams, station noise characteristics, priors on estimated location of the seismic source) by representing the locus of intersecting hypothetical loci for a given datum as joint probability density functions.

A Bayesian joint probability modeling approach for seasonal forecasting of streamflows at multiple sites

Science.gov (United States)

Wang, Q. J.; Robertson, D. E.; Chiew, F. H. S.

2009-05-01

Seasonal forecasting of streamflows can be highly valuable for water resources management. In this paper, a Bayesian joint probability (BJP) modeling approach for seasonal forecasting of streamflows at multiple sites is presented. A Box-Cox transformed multivariate normal distribution is proposed to model the joint distribution of future streamflows and their predictors such as antecedent streamflows and El Niño-Southern Oscillation indices and other climate indicators. Bayesian inference of model parameters and uncertainties is implemented using Markov chain Monte Carlo sampling, leading to joint probabilistic forecasts of streamflows at multiple sites. The model provides a parametric structure for quantifying relationships between variables, including intersite correlations. The Box-Cox transformed multivariate normal distribution has considerable flexibility for modeling a wide range of predictors and predictands. The Bayesian inference formulated allows the use of data that contain nonconcurrent and missing records. The model flexibility and data-handling ability means that the BJP modeling approach is potentially of wide practical application. The paper also presents a number of statistical measures and graphical methods for verification of probabilistic forecasts of continuous variables. Results for streamflows at three river gauges in the Murrumbidgee River catchment in southeast Australia show that the BJP modeling approach has good forecast quality and that the fitted model is consistent with observed data.

A non-parametric Bayesian approach to decompounding from high frequency data

NARCIS (Netherlands)

Gugushvili, Shota; van der Meulen, F.H.; Spreij, Peter

2016-01-01

Given a sample from a discretely observed compound Poisson process, we consider non-parametric estimation of the density f0 of its jump sizes, as well as of its intensity λ0. We take a Bayesian approach to the problem and specify the prior on f0 as the Dirichlet location mixture of normal densities.

Event based uncertainty assessment in urban drainage modelling, applying the GLUE methodology

DEFF Research Database (Denmark)

Thorndahl, Søren; Beven, K.J.; Jensen, Jacob Birk

2008-01-01

of combined sewer overflow. The GLUE methodology is used to test different conceptual setups in order to determine if one model setup gives a better goodness of fit conditional on the observations than the other. Moreover, different methodological investigations of GLUE are conducted in order to test......In the present paper an uncertainty analysis on an application of the commercial urban drainage model MOUSE is conducted. Applying the Generalized Likelihood Uncertainty Estimation (GLUE) methodology the model is conditioned on observation time series from two flow gauges as well as the occurrence...... if the uncertainty analysis is unambiguous. It is shown that the GLUE methodology is very applicable in uncertainty analysis of this application of an urban drainage model, although it was shown to be quite difficult of get good fits of the whole time series....

Endoluminal embolization of bilateral atherosclerotic common iliac aneurysms with fibrin tissue glue (Beriplast)

International Nuclear Information System (INIS)

Beese, Richard C.; Tomlinson, Mark A.; Buckenham, Timothy M.

2000-01-01

The standard surgical approach to nonleaking iliac aneurysms found at repair of a leaking abdominal aortic aneurysm is to minimize the operative risk by repairing the abdominal aorta only. This means that the bypassed iliac aneurysms may have to be repaired later. As this population of patients are usually elderly with coexisting medical problems, interventional radiology is being used to embolize these aneurysms, thus avoiding the morbidity and mortality associated with further general anesthesia and surgery. Various materials and stents have been reported to be effective in the treatment of iliac aneurysms. We report the successful use of endoluminal fibrin tissue glue (Beriplast) to treat two large iliac aneurysms in a patient who had had a previous abdominal aortic aneurysm repair. We discuss the technique involved and the reasons why we used tissue glue in this patient.

Learning Bayesian networks for discrete data

KAUST Repository

Liang, Faming

2009-02-01

Bayesian networks have received much attention in the recent literature. In this article, we propose an approach to learn Bayesian networks using the stochastic approximation Monte Carlo (SAMC) algorithm. Our approach has two nice features. Firstly, it possesses the self-adjusting mechanism and thus avoids essentially the local-trap problem suffered by conventional MCMC simulation-based approaches in learning Bayesian networks. Secondly, it falls into the class of dynamic importance sampling algorithms; the network features can be inferred by dynamically weighted averaging the samples generated in the learning process, and the resulting estimates can have much lower variation than the single model-based estimates. The numerical results indicate that our approach can mix much faster over the space of Bayesian networks than the conventional MCMC simulation-based approaches. © 2008 Elsevier B.V. All rights reserved.

A Bayesian Approach for Summarizing and Modeling Time-Series Exposure Data with Left Censoring.

Science.gov (United States)

Houseman, E Andres; Virji, M Abbas

2017-08-01

Direct reading instruments are valuable tools for measuring exposure as they provide real-time measurements for rapid decision making. However, their use is limited to general survey applications in part due to issues related to their performance. Moreover, statistical analysis of real-time data is complicated by autocorrelation among successive measurements, non-stationary time series, and the presence of left-censoring due to limit-of-detection (LOD). A Bayesian framework is proposed that accounts for non-stationary autocorrelation and LOD issues in exposure time-series data in order to model workplace factors that affect exposure and estimate summary statistics for tasks or other covariates of interest. A spline-based approach is used to model non-stationary autocorrelation with relatively few assumptions about autocorrelation structure. Left-censoring is addressed by integrating over the left tail of the distribution. The model is fit using Markov-Chain Monte Carlo within a Bayesian paradigm. The method can flexibly account for hierarchical relationships, random effects and fixed effects of covariates. The method is implemented using the rjags package in R, and is illustrated by applying it to real-time exposure data. Estimates for task means and covariates from the Bayesian model are compared to those from conventional frequentist models including linear regression, mixed-effects, and time-series models with different autocorrelation structures. Simulations studies are also conducted to evaluate method performance. Simulation studies with percent of measurements below the LOD ranging from 0 to 50% showed lowest root mean squared errors for task means and the least biased standard deviations from the Bayesian model compared to the frequentist models across all levels of LOD. In the application, task means from the Bayesian model were similar to means from the frequentist models, while the standard deviations were different. Parameter estimates for covariates

Overlapping community detection in weighted networks via a Bayesian approach

Science.gov (United States)

Chen, Yi; Wang, Xiaolong; Xiang, Xin; Tang, Buzhou; Chen, Qingcai; Fan, Shixi; Bu, Junzhao

2017-02-01

Complex networks as a powerful way to represent complex systems have been widely studied during the past several years. One of the most important tasks of complex network analysis is to detect communities embedded in networks. In the real world, weighted networks are very common and may contain overlapping communities where a node is allowed to belong to multiple communities. In this paper, we propose a novel Bayesian approach, called the Bayesian mixture network (BMN) model, to detect overlapping communities in weighted networks. The advantages of our method are (i) providing soft-partition solutions in weighted networks; (ii) providing soft memberships, which quantify 'how strongly' a node belongs to a community. Experiments on a large number of real and synthetic networks show that our model has the ability in detecting overlapping communities in weighted networks and is competitive with other state-of-the-art models at shedding light on community partition.

Krazy Glue® in the ear: A case report of child abuse.

Science.gov (United States)

Sorichetti, Brendan D; Fandiño, Marcela; Kozak, Frederick K

2018-06-01

Krazy Glue ® or cyanoacrylate glue is an acrylic resin that polymerizes in less than a minute when in contact with moisture or water. We present a case of a one month old referred to our tertiary pediatric otolaryngology clinic from an outside emergency department with a history of application of cyanoacrylate glue in the external ear canals. This report presents the management of this case along with the medical and legal outcomes surrounding this case of child abuse. Copyright © 2018. Published by Elsevier B.V.

Quantitative Precipitation Estimation over Ocean Using Bayesian Approach from Microwave Observations during the Typhoon Season

Directory of Open Access Journals (Sweden)

Jen-Chi Hu

2009-01-01

Full Text Available We have developed a new Bayesian approach to retrieve oceanic rain rate from the Tropical Rainfall Measuring Mission (TRMM Microwave Imager (TMI, with an emphasis on typhoon cases in the West Pacific. Retrieved rain rates are validated with measurements of rain gauges located on Japanese islands. To demonstrate improvement, retrievals are also compared with those from the TRMM/Precipitation Radar (PR, the Goddard Profiling Algorithm (GPROF, and a multi-channel linear regression statistical method (MLRS. We have found that qualitatively, all methods retrieved similar horizontal distributions in terms of locations of eyes and rain bands of typhoons. Quantitatively, our new Bayesian retrievals have the best linearity and the smallest root mean square (RMS error against rain gauge data for 16 typhoon over passes in 2004. The correlation coefficient and RMS of our retrievals are 0.95 and ~2 mm hr-1, respectively. In particular, at heavy rain rates, our Bayesian retrievals out perform those retrieved from GPROF and MLRS. Over all, the new Bayesian approach accurately retrieves surface rain rate for typhoon cases. Ac cu rate rain rate estimates from this method can be assimilated in models to improve forecast and prevent potential damages in Taiwan during typhoon seasons.

A non-linear and stochastic response surface method for Bayesian estimation of uncertainty in soil moisture simulation from a land surface model

Directory of Open Access Journals (Sweden)

F. Hossain

2004-01-01

Full Text Available This study presents a simple and efficient scheme for Bayesian estimation of uncertainty in soil moisture simulation by a Land Surface Model (LSM. The scheme is assessed within a Monte Carlo (MC simulation framework based on the Generalized Likelihood Uncertainty Estimation (GLUE methodology. A primary limitation of using the GLUE method is the prohibitive computational burden imposed by uniform random sampling of the model's parameter distributions. Sampling is improved in the proposed scheme by stochastic modeling of the parameters' response surface that recognizes the non-linear deterministic behavior between soil moisture and land surface parameters. Uncertainty in soil moisture simulation (model output is approximated through a Hermite polynomial chaos expansion of normal random variables that represent the model's parameter (model input uncertainty. The unknown coefficients of the polynomial are calculated using limited number of model simulation runs. The calibrated polynomial is then used as a fast-running proxy to the slower-running LSM to predict the degree of representativeness of a randomly sampled model parameter set. An evaluation of the scheme's efficiency in sampling is made through comparison with the fully random MC sampling (the norm for GLUE and the nearest-neighborhood sampling technique. The scheme was able to reduce computational burden of random MC sampling for GLUE in the ranges of 10%-70%. The scheme was also found to be about 10% more efficient than the nearest-neighborhood sampling method in predicting a sampled parameter set's degree of representativeness. The GLUE based on the proposed sampling scheme did not alter the essential features of the uncertainty structure in soil moisture simulation. The scheme can potentially make GLUE uncertainty estimation for any LSM more efficient as it does not impose any additional structural or distributional assumptions.

GO-Bayes: Gene Ontology-based overrepresentation analysis using a Bayesian approach.

Science.gov (United States)

Zhang, Song; Cao, Jing; Kong, Y Megan; Scheuermann, Richard H

2010-04-01

A typical approach for the interpretation of high-throughput experiments, such as gene expression microarrays, is to produce groups of genes based on certain criteria (e.g. genes that are differentially expressed). To gain more mechanistic insights into the underlying biology, overrepresentation analysis (ORA) is often conducted to investigate whether gene sets associated with particular biological functions, for example, as represented by Gene Ontology (GO) annotations, are statistically overrepresented in the identified gene groups. However, the standard ORA, which is based on the hypergeometric test, analyzes each GO term in isolation and does not take into account the dependence structure of the GO-term hierarchy. We have developed a Bayesian approach (GO-Bayes) to measure overrepresentation of GO terms that incorporates the GO dependence structure by taking into account evidence not only from individual GO terms, but also from their related terms (i.e. parents, children, siblings, etc.). The Bayesian framework borrows information across related GO terms to strengthen the detection of overrepresentation signals. As a result, this method tends to identify sets of closely related GO terms rather than individual isolated GO terms. The advantage of the GO-Bayes approach is demonstrated with a simulation study and an application example.

Information-provider scripts for GLUE2 and RAID configurations.

CERN Document Server

Kalimeris, Dimitrios

2014-01-01

This report will outline two projects that were done as part of a three months long summer internship at CERN. In the first project we dealt with Worldwide LHC Computing Grid (WLCG) and its information system. The information system currently conforms to a schema called GLUE and it is evolving towards a new version: GLUE2. The aim of the project was to develop and adapt the current information system of the WLCG, used by the Large Scale Storage Systems at CERN (CASTOR and EOS), to the new GLUE2 schema. During the second project we investigated different RAID configurations so that we can get performance boost from CERN's disk systems in the future. RAID 1 that is currently in use is not an option anymore because of limited performance and high cost. We tried to discover RAID configurations that will improve the performance and simultaneously decrease the cost.

Stroke from Delayed Embolization of Polymerized Glue Following Percutaneous Direct Injection of a Carotid Body Tumor

Energy Technology Data Exchange (ETDEWEB)

Krishnamoorthy, Thamburaj; Gupta, Arun Kumar; Rajan, Jayadevan E; Thomas, Bejoy [Sree Chitra Tirunal Institute for Medical Sciences and Technology, Trivandrum, Kerala, (India)

2007-06-15

Direct percutaneous embolization of hypervascular tumors results in more effective preoperative devascularization. Migration of glue is a well known complication of direct glue injection and it may lead to stroke or cranial nerve deficits. We report here on a case of carotid body tumor in a 52-year-old man; the tumor was mainly embolized by percutaneous injection of 50% glue and this was supported with balloon protection of the internal carotid artery. Thirteen hours later, he developed hemiparesis from delayed migration of glue. The possible mechanisms of this migration are discussed and preventive measures are suggested. Preoperative embolization of hypervascular tumors of the head and neck, including carotid body tumor, is often performed to decrease the amount of blood loss during surgery. Devascularization is mainly performed with particulate agents and by employing the transarterial route. More effective embolization may be achieved by performing percutaneous direct embolization of hypervascular tumors with liquid embolic agents. Even though there are few reports available on direct embolization, complications from glue migration have been reported, and this mainly happens during the procedure when the glue is in a liquid state. We report here on a case of delayed migration of polymerized glue (n-butyl-2-cyanoacrylate [NBCA]), many hours after the procedure, into the intracranial circulation and the final result was stroke. A 52-year-old male with right carotid body tumor underwent direct percutaneous glue (n-butylcyanoacrylate [NBCA]) embolization. Several hours later, he developed left hemiparesis from embolization of the polymerized glue cast. Migration of glue during percutaneous tumor embolization is presumed to occur only in the liquid state, which may lead to stroke or cranial nerve deficits. To the best of our knowledge, this is the first report of delayed glue embolization from a treated hypervascular tumor of the head and neck.

Learning Bayesian networks for discrete data

KAUST Repository

Liang, Faming; Zhang, Jian

2009-01-01

Bayesian networks have received much attention in the recent literature. In this article, we propose an approach to learn Bayesian networks using the stochastic approximation Monte Carlo (SAMC) algorithm. Our approach has two nice features. Firstly

Bayesian estimation of parameters in a regional hydrological model

Directory of Open Access Journals (Sweden)

K. Engeland

2002-01-01

Full Text Available This study evaluates the applicability of the distributed, process-oriented Ecomag model for prediction of daily streamflow in ungauged basins. The Ecomag model is applied as a regional model to nine catchments in the NOPEX area, using Bayesian statistics to estimate the posterior distribution of the model parameters conditioned on the observed streamflow. The distribution is calculated by Markov Chain Monte Carlo (MCMC analysis. The Bayesian method requires formulation of a likelihood function for the parameters and three alternative formulations are used. The first is a subjectively chosen objective function that describes the goodness of fit between the simulated and observed streamflow, as defined in the GLUE framework. The second and third formulations are more statistically correct likelihood models that describe the simulation errors. The full statistical likelihood model describes the simulation errors as an AR(1 process, whereas the simple model excludes the auto-regressive part. The statistical parameters depend on the catchments and the hydrological processes and the statistical and the hydrological parameters are estimated simultaneously. The results show that the simple likelihood model gives the most robust parameter estimates. The simulation error may be explained to a large extent by the catchment characteristics and climatic conditions, so it is possible to transfer knowledge about them to ungauged catchments. The statistical models for the simulation errors indicate that structural errors in the model are more important than parameter uncertainties. Keywords: regional hydrological model, model uncertainty, Bayesian analysis, Markov Chain Monte Carlo analysis

On the glue content in heavy quarkonia

International Nuclear Information System (INIS)

Gromes, D.

2003-01-01

Starting with two coupled Bethe-Salpeter equations for the quark-antiquark, and for the quark-glue-antiquark component of the quarkonium, we solve the bound state equations perturbatively. The resulting admixture of glue can be partially understood in a semiclassical way; one has, however, to take care of the different use of time ordered versus retarded Green functions. Subtle questions concerning the precise definition of the equal time wave function arise, because the wave function for the Coulomb gluon is discontinuous with respect to the relative time of the gluon. A striking feature is that a one loop non-abelian graph contributes to the same order as tree graphs, because the couplings of transverse gluons in the tree graphs are suppressed in the non-relativistic bound state, while the higher order loop graph can couple to quarks via non-suppressed Coulomb gluons. We also calculate the amplitude for quark and antiquark at zero distance in the quark-glue-antiquark component of the P-state. This quantity is of importance for annihilation decays of P-states. It shows a remarkable compensation between the tree graph and the non-abelian loop graph contribution. An extension of our results to include non-perturbative effects is possible. (orig.)

Understanding Computational Bayesian Statistics

CERN Document Server

Bolstad, William M

2011-01-01

A hands-on introduction to computational statistics from a Bayesian point of view Providing a solid grounding in statistics while uniquely covering the topics from a Bayesian perspective, Understanding Computational Bayesian Statistics successfully guides readers through this new, cutting-edge approach. With its hands-on treatment of the topic, the book shows how samples can be drawn from the posterior distribution when the formula giving its shape is all that is known, and how Bayesian inferences can be based on these samples from the posterior. These ideas are illustrated on common statistic

Bayesian statistics an introduction

CERN Document Server

Lee, Peter M

2012-01-01

Bayesian Statistics is the school of thought that combines prior beliefs with the likelihood of a hypothesis to arrive at posterior beliefs. The first edition of Peter Lee’s book appeared in 1989, but the subject has moved ever onwards, with increasing emphasis on Monte Carlo based techniques. This new fourth edition looks at recent techniques such as variational methods, Bayesian importance sampling, approximate Bayesian computation and Reversible Jump Markov Chain Monte Carlo (RJMCMC), providing a concise account of the way in which the Bayesian approach to statistics develops as wel

Evaluating impacts using a BACI design, ratios, and a Bayesian approach with a focus on restoration.

Science.gov (United States)

Conner, Mary M; Saunders, W Carl; Bouwes, Nicolaas; Jordan, Chris

2015-10-01

Before-after-control-impact (BACI) designs are an effective method to evaluate natural and human-induced perturbations on ecological variables when treatment sites cannot be randomly chosen. While effect sizes of interest can be tested with frequentist methods, using Bayesian Markov chain Monte Carlo (MCMC) sampling methods, probabilities of effect sizes, such as a ≥20 % increase in density after restoration, can be directly estimated. Although BACI and Bayesian methods are used widely for assessing natural and human-induced impacts for field experiments, the application of hierarchal Bayesian modeling with MCMC sampling to BACI designs is less common. Here, we combine these approaches and extend the typical presentation of results with an easy to interpret ratio, which provides an answer to the main study question-"How much impact did a management action or natural perturbation have?" As an example of this approach, we evaluate the impact of a restoration project, which implemented beaver dam analogs, on survival and density of juvenile steelhead. Results indicated the probabilities of a ≥30 % increase were high for survival and density after the dams were installed, 0.88 and 0.99, respectively, while probabilities for a higher increase of ≥50 % were variable, 0.17 and 0.82, respectively. This approach demonstrates a useful extension of Bayesian methods that can easily be generalized to other study designs from simple (e.g., single factor ANOVA, paired t test) to more complicated block designs (e.g., crossover, split-plot). This approach is valuable for estimating the probabilities of restoration impacts or other management actions.

A Bayesian inference approach to unveil supply curves in electricity markets

DEFF Research Database (Denmark)

Mitridati, Lesia Marie-Jeanne Mariane; Pinson, Pierre

2017-01-01

in the literature on modeling this uncertainty. In this study we introduce a Bayesian inference approach to reveal the aggregate supply curve in a day-ahead electricity market. The proposed algorithm relies on Markov Chain Monte Carlo and Sequential Monte Carlo methods. The major appeal of this approach......With increased competition in wholesale electricity markets, the need for new decision-making tools for strategic producers has arisen. Optimal bidding strategies have traditionally been modeled as stochastic profit maximization problems. However, for producers with non-negligible market power...

Glue versus suture for mesh fixation in inguinal hernia repair.

Science.gov (United States)

Chandrasekar, Shruthi; Jeyakumar, S; Ganapathy, Tharun

2018-03-22

Inguinal hernia is one of the most common surgical problem presenting to the surgical OPD. Surgery is the mainstay of treatment for inguinal hernia today. Surgery for inguinal hernia has undergone a great evolution over a period of several centuries. Lichenstein's tension free hernioplasty is the one of the first surgeries taught to a surgical resident. The main aim of surgeries in this era is to give the best possible results with the least possible pain, scar and time. This has given rise to so many modifications to the classical Lichenstein's procedure and also to laparoscopic hernioplasty. Pain after inguinal hernia surgery is found to be debilitating and altering the quality of life in several patients, which has been attributed to the traumatic fixation of the mesh with sutures. This has paved way to the development of various atraumatic methods of fixation, tissue glue is one such development. Hence this study, to compare traumatic and atraumatic methods of mesh fixation in inguinal hernia repair. The aim of this study was to compare suture fixation versus tissue glue fixation of the mesh in inguinal hernia repair. Primary objective was to compare the immediate and chronic post-operative pain. Secondary objective was to compare the time taken for the procedure by the two methods in use and also to compare the presence of any complications. and methodology: This study was done in the General Surgery department of XXX hospital, medical college and research centre, kattangulathur after Ethics committee clearance. It is a single blinded study. The study was done on 51 patients consenting for the study and meeting the inclusion criterias from the period of March 2016 to August 2017 out of which 26 were selected for glue mesh fixation and 25 for suture mesh fixation according to simple randomization. The suture group patients underwent classical Lichenstein's tension free hernioplasty and the glue group underwent Lichenstein's hernioplasty with glue where dots of

A Bayesian approach for quantification of model uncertainty

International Nuclear Information System (INIS)

Park, Inseok; Amarchinta, Hemanth K.; Grandhi, Ramana V.

2010-01-01

In most engineering problems, more than one model can be created to represent an engineering system's behavior. Uncertainty is inevitably involved in selecting the best model from among the models that are possible. Uncertainty in model selection cannot be ignored, especially when the differences between the predictions of competing models are significant. In this research, a methodology is proposed to quantify model uncertainty using measured differences between experimental data and model outcomes under a Bayesian statistical framework. The adjustment factor approach is used to propagate model uncertainty into prediction of a system response. A nonlinear vibration system is used to demonstrate the processes for implementing the adjustment factor approach. Finally, the methodology is applied on the engineering benefits of a laser peening process, and a confidence band for residual stresses is established to indicate the reliability of model prediction.

Glue detection based on teaching points constraint and tracking model of pixel convolution

Science.gov (United States)

Geng, Lei; Ma, Xiao; Xiao, Zhitao; Wang, Wen

2018-01-01

On-line glue detection based on machine version is significant for rust protection and strengthening in car production. Shadow stripes caused by reflect light and unevenness of inside front cover of car reduce the accuracy of glue detection. In this paper, we propose an effective algorithm to distinguish the edges of the glue and shadow stripes. Teaching points are utilized to calculate slope between the two adjacent points. Then a tracking model based on pixel convolution along motion direction is designed to segment several local rectangular regions using distance. The distance is the height of rectangular region. The pixel convolution along the motion direction is proposed to extract edges of gules in local rectangular region. A dataset with different illumination and complexity shape stripes are used to evaluate proposed method, which include 500 thousand images captured from the camera of glue gun machine. Experimental results demonstrate that the proposed method can detect the edges of glue accurately. The shadow stripes are distinguished and removed effectively. Our method achieves the 99.9% accuracies for the image dataset.

Bayesian networks improve causal environmental ...

Science.gov (United States)

Rule-based weight of evidence approaches to ecological risk assessment may not account for uncertainties and generally lack probabilistic integration of lines of evidence. Bayesian networks allow causal inferences to be made from evidence by including causal knowledge about the problem, using this knowledge with probabilistic calculus to combine multiple lines of evidence, and minimizing biases in predicting or diagnosing causal relationships. Too often, sources of uncertainty in conventional weight of evidence approaches are ignored that can be accounted for with Bayesian networks. Specifying and propagating uncertainties improve the ability of models to incorporate strength of the evidence in the risk management phase of an assessment. Probabilistic inference from a Bayesian network allows evaluation of changes in uncertainty for variables from the evidence. The network structure and probabilistic framework of a Bayesian approach provide advantages over qualitative approaches in weight of evidence for capturing the impacts of multiple sources of quantifiable uncertainty on predictions of ecological risk. Bayesian networks can facilitate the development of evidence-based policy under conditions of uncertainty by incorporating analytical inaccuracies or the implications of imperfect information, structuring and communicating causal issues through qualitative directed graph formulations, and quantitatively comparing the causal power of multiple stressors on value

Bayesian inference with ecological applications

CERN Document Server

Link, William A

2009-01-01

This text is written to provide a mathematically sound but accessible and engaging introduction to Bayesian inference specifically for environmental scientists, ecologists and wildlife biologists. It emphasizes the power and usefulness of Bayesian methods in an ecological context. The advent of fast personal computers and easily available software has simplified the use of Bayesian and hierarchical models . One obstacle remains for ecologists and wildlife biologists, namely the near absence of Bayesian texts written specifically for them. The book includes many relevant examples, is supported by software and examples on a companion website and will become an essential grounding in this approach for students and research ecologists. Engagingly written text specifically designed to demystify a complex subject Examples drawn from ecology and wildlife research An essential grounding for graduate and research ecologists in the increasingly prevalent Bayesian approach to inference Companion website with analyt...

Bayesian data analysis for newcomers.

Science.gov (United States)

Kruschke, John K; Liddell, Torrin M

2018-02-01

This article explains the foundational concepts of Bayesian data analysis using virtually no mathematical notation. Bayesian ideas already match your intuitions from everyday reasoning and from traditional data analysis. Simple examples of Bayesian data analysis are presented that illustrate how the information delivered by a Bayesian analysis can be directly interpreted. Bayesian approaches to null-value assessment are discussed. The article clarifies misconceptions about Bayesian methods that newcomers might have acquired elsewhere. We discuss prior distributions and explain how they are not a liability but an important asset. We discuss the relation of Bayesian data analysis to Bayesian models of mind, and we briefly discuss what methodological problems Bayesian data analysis is not meant to solve. After you have read this article, you should have a clear sense of how Bayesian data analysis works and the sort of information it delivers, and why that information is so intuitive and useful for drawing conclusions from data.

An empirical Bayesian approach for model-based inference of cellular signaling networks

Directory of Open Access Journals (Sweden)

Klinke David J

2009-11-01

Full Text Available Abstract Background A common challenge in systems biology is to infer mechanistic descriptions of biological process given limited observations of a biological system. Mathematical models are frequently used to represent a belief about the causal relationships among proteins within a signaling network. Bayesian methods provide an attractive framework for inferring the validity of those beliefs in the context of the available data. However, efficient sampling of high-dimensional parameter space and appropriate convergence criteria provide barriers for implementing an empirical Bayesian approach. The objective of this study was to apply an Adaptive Markov chain Monte Carlo technique to a typical study of cellular signaling pathways. Results As an illustrative example, a kinetic model for the early signaling events associated with the epidermal growth factor (EGF signaling network was calibrated against dynamic measurements observed in primary rat hepatocytes. A convergence criterion, based upon the Gelman-Rubin potential scale reduction factor, was applied to the model predictions. The posterior distributions of the parameters exhibited complicated structure, including significant covariance between specific parameters and a broad range of variance among the parameters. The model predictions, in contrast, were narrowly distributed and were used to identify areas of agreement among a collection of experimental studies. Conclusion In summary, an empirical Bayesian approach was developed for inferring the confidence that one can place in a particular model that describes signal transduction mechanisms and for inferring inconsistencies in experimental measurements.

Glue therapy in hemoptysis: A new technique

Directory of Open Access Journals (Sweden)

Rakesh K Chawla

2012-01-01

Full Text Available Hemoptysis is defined as the spitting of blood derived from the lungs or bronchial tubes as a result of pulmonary or bronchial hemorrhage. There is a large chunk of patients with hemoptysis who do not respond to conservative treatment including use of cough suppressants, antibiotics, vitamin C, hemostatics, and anxiolytics. The advanced management of such a situation is bronchial artery embolization (BAE or open thoracic surgery, which is often not possible. We have attempted a cheap, effective, and safe alternative in the form of intrabronchial instillation of glue (n-butyl cyanoacrylate under vision with the help of a therapeutic video bronchoscope (OLYMPUS T-180. The glue is instilled through a polyethylene catheter placed through the working channel of the video bronchoscope.

Bayesian ensemble approach to error estimation of interatomic potentials

DEFF Research Database (Denmark)

Frederiksen, Søren Lund; Jacobsen, Karsten Wedel; Brown, K.S.

2004-01-01

Using a Bayesian approach a general method is developed to assess error bars on predictions made by models fitted to data. The error bars are estimated from fluctuations in ensembles of models sampling the model-parameter space with a probability density set by the minimum cost. The method...... is applied to the development of interatomic potentials for molybdenum using various potential forms and databases based on atomic forces. The calculated error bars on elastic constants, gamma-surface energies, structural energies, and dislocation properties are shown to provide realistic estimates...

Photoinduced Bioorthogonal 1,3-Dipolar Poly-cycloaddition Promoted by Oxyanionic Substrates for Spatiotemporal Operation of Molecular Glues.

Science.gov (United States)

Hatano, Junichi; Okuro, Kou; Aida, Takuzo

2016-01-04

PGlue(PZ), a pyrazoline (PZ)-based fluorescent adhesive which can be generated spatiotemporally in living systems, was developed. Since PGlue(PZ) carries many guanidinium ion (Gu(+)) pendants, it strongly adheres to various oxyanionic substrates through a multivalent salt-bridge interaction. PGlue(PZ) is given by bioorthogonal photopolymerization of a Gu(+)-appended monomer (Glue(TZ)), bearing tetrazole (TZ) and olefinic termini. Upon exposure to UV light, Glue(TZ) transforms into a nitrileimine (NI) intermediate (Glue(NI)), which is eligible for 1,3-dipolar polycycloaddition. However, Glue(NI) in aqueous media can concomitantly be deactivated into Glue(WA) by the addition of water, and the polymerization hardly occurs unless Glue(NI) is concentrated. We found that, even under high dilution, Glue(NI) is concentrated on oxyanionic substrates to a sufficient level for the polymerization, so that their surfaces can be point-specifically functionalized with PGlue(PZ) by the use of a focused beam of UV light. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Measurement of radiation damage on an epoxy-based optical glue

International Nuclear Information System (INIS)

Huang, H.C.; Peng, K.C.; Sahu, S.K.; Ueno, K.; Chang, Y.H.; Wang, C.H.; Hou, W.S.

1997-01-01

We measured the radiation damage on an optical glue called Eccobond-24, which is a candidate for CsI and BGO crystal calorimeters of the BELLE detector of the KEK B-factory. Absorption spectrophotometry in the range 300-800 nm was used to monitor the radiation damage. The maximum equivalent dose was 1.64 Mrad. The glue shows effects of damage, but is acceptable for the radiation level in the above-mentioned experiment. (orig.)

Drug-target interaction prediction: A Bayesian ranking approach.

Science.gov (United States)

Peska, Ladislav; Buza, Krisztian; Koller, Júlia

2017-12-01

In silico prediction of drug-target interactions (DTI) could provide valuable information and speed-up the process of drug repositioning - finding novel usage for existing drugs. In our work, we focus on machine learning algorithms supporting drug-centric repositioning approach, which aims to find novel usage for existing or abandoned drugs. We aim at proposing a per-drug ranking-based method, which reflects the needs of drug-centric repositioning research better than conventional drug-target prediction approaches. We propose Bayesian Ranking Prediction of Drug-Target Interactions (BRDTI). The method is based on Bayesian Personalized Ranking matrix factorization (BPR) which has been shown to be an excellent approach for various preference learning tasks, however, it has not been used for DTI prediction previously. In order to successfully deal with DTI challenges, we extended BPR by proposing: (i) the incorporation of target bias, (ii) a technique to handle new drugs and (iii) content alignment to take structural similarities of drugs and targets into account. Evaluation on five benchmark datasets shows that BRDTI outperforms several state-of-the-art approaches in terms of per-drug nDCG and AUC. BRDTI results w.r.t. nDCG are 0.929, 0.953, 0.948, 0.897 and 0.690 for G-Protein Coupled Receptors (GPCR), Ion Channels (IC), Nuclear Receptors (NR), Enzymes (E) and Kinase (K) datasets respectively. Additionally, BRDTI significantly outperformed other methods (BLM-NII, WNN-GIP, NetLapRLS and CMF) w.r.t. nDCG in 17 out of 20 cases. Furthermore, BRDTI was also shown to be able to predict novel drug-target interactions not contained in the original datasets. The average recall at top-10 predicted targets for each drug was 0.762, 0.560, 1.000 and 0.404 for GPCR, IC, NR, and E datasets respectively. Based on the evaluation, we can conclude that BRDTI is an appropriate choice for researchers looking for an in silico DTI prediction technique to be used in drug

An efficient Bayesian inference approach to inverse problems based on an adaptive sparse grid collocation method

International Nuclear Information System (INIS)

Ma Xiang; Zabaras, Nicholas

2009-01-01

A new approach to modeling inverse problems using a Bayesian inference method is introduced. The Bayesian approach considers the unknown parameters as random variables and seeks the probabilistic distribution of the unknowns. By introducing the concept of the stochastic prior state space to the Bayesian formulation, we reformulate the deterministic forward problem as a stochastic one. The adaptive hierarchical sparse grid collocation (ASGC) method is used for constructing an interpolant to the solution of the forward model in this prior space which is large enough to capture all the variability/uncertainty in the posterior distribution of the unknown parameters. This solution can be considered as a function of the random unknowns and serves as a stochastic surrogate model for the likelihood calculation. Hierarchical Bayesian formulation is used to derive the posterior probability density function (PPDF). The spatial model is represented as a convolution of a smooth kernel and a Markov random field. The state space of the PPDF is explored using Markov chain Monte Carlo algorithms to obtain statistics of the unknowns. The likelihood calculation is performed by directly sampling the approximate stochastic solution obtained through the ASGC method. The technique is assessed on two nonlinear inverse problems: source inversion and permeability estimation in flow through porous media

The Bayesian New Statistics: Hypothesis testing, estimation, meta-analysis, and power analysis from a Bayesian perspective.

Science.gov (United States)

Kruschke, John K; Liddell, Torrin M

2018-02-01

In the practice of data analysis, there is a conceptual distinction between hypothesis testing, on the one hand, and estimation with quantified uncertainty on the other. Among frequentists in psychology, a shift of emphasis from hypothesis testing to estimation has been dubbed "the New Statistics" (Cumming 2014). A second conceptual distinction is between frequentist methods and Bayesian methods. Our main goal in this article is to explain how Bayesian methods achieve the goals of the New Statistics better than frequentist methods. The article reviews frequentist and Bayesian approaches to hypothesis testing and to estimation with confidence or credible intervals. The article also describes Bayesian approaches to meta-analysis, randomized controlled trials, and power analysis.

Bayesian approach to estimate AUC, partition coefficient and drug targeting index for studies with serial sacrifice design.

Science.gov (United States)

Wang, Tianli; Baron, Kyle; Zhong, Wei; Brundage, Richard; Elmquist, William

2014-03-01

The current study presents a Bayesian approach to non-compartmental analysis (NCA), which provides the accurate and precise estimate of AUC 0 (∞) and any AUC 0 (∞) -based NCA parameter or derivation. In order to assess the performance of the proposed method, 1,000 simulated datasets were generated in different scenarios. A Bayesian method was used to estimate the tissue and plasma AUC 0 (∞) s and the tissue-to-plasma AUC 0 (∞) ratio. The posterior medians and the coverage of 95% credible intervals for the true parameter values were examined. The method was applied to laboratory data from a mice brain distribution study with serial sacrifice design for illustration. Bayesian NCA approach is accurate and precise in point estimation of the AUC 0 (∞) and the partition coefficient under a serial sacrifice design. It also provides a consistently good variance estimate, even considering the variability of the data and the physiological structure of the pharmacokinetic model. The application in the case study obtained a physiologically reasonable posterior distribution of AUC, with a posterior median close to the value estimated by classic Bailer-type methods. This Bayesian NCA approach for sparse data analysis provides statistical inference on the variability of AUC 0 (∞) -based parameters such as partition coefficient and drug targeting index, so that the comparison of these parameters following destructive sampling becomes statistically feasible.

Advances in Bayesian Modeling in Educational Research

Science.gov (United States)

Levy, Roy

2016-01-01

In this article, I provide a conceptually oriented overview of Bayesian approaches to statistical inference and contrast them with frequentist approaches that currently dominate conventional practice in educational research. The features and advantages of Bayesian approaches are illustrated with examples spanning several statistical modeling…

A Bayesian approach to spectral quantitative photoacoustic tomography

International Nuclear Information System (INIS)

Pulkkinen, A; Kaipio, J P; Tarvainen, T; Cox, B T; Arridge, S R

2014-01-01

A Bayesian approach to the optical reconstruction problem associated with spectral quantitative photoacoustic tomography is presented. The approach is derived for commonly used spectral tissue models of optical absorption and scattering: the absorption is described as a weighted sum of absorption spectra of known chromophores (spatially dependent chromophore concentrations), while the scattering is described using Mie scattering theory, with the proportionality constant and spectral power law parameter both spatially-dependent. It is validated using two-dimensional test problems composed of three biologically relevant chromophores: fat, oxygenated blood and deoxygenated blood. Using this approach it is possible to estimate the Grüneisen parameter, the absolute chromophore concentrations, and the Mie scattering parameters associated with spectral photoacoustic tomography problems. In addition, the direct estimation of the spectral parameters is compared to estimates obtained by fitting the spectral parameters to estimates of absorption, scattering and Grüneisen parameter at the investigated wavelengths. It is shown with numerical examples that the direct estimation results in better accuracy of the estimated parameters. (papers)

bayesQR: A Bayesian Approach to Quantile Regression

Directory of Open Access Journals (Sweden)

Dries F. Benoit

2017-01-01

Full Text Available After its introduction by Koenker and Basset (1978, quantile regression has become an important and popular tool to investigate the conditional response distribution in regression. The R package bayesQR contains a number of routines to estimate quantile regression parameters using a Bayesian approach based on the asymmetric Laplace distribution. The package contains functions for the typical quantile regression with continuous dependent variable, but also supports quantile regression for binary dependent variables. For both types of dependent variables, an approach to variable selection using the adaptive lasso approach is provided. For the binary quantile regression model, the package also contains a routine that calculates the fitted probabilities for each vector of predictors. In addition, functions for summarizing the results, creating traceplots, posterior histograms and drawing quantile plots are included. This paper starts with a brief overview of the theoretical background of the models used in the bayesQR package. The main part of this paper discusses the computational problems that arise in the implementation of the procedure and illustrates the usefulness of the package through selected examples.

Glue from the Sea:Biomedical Adhesives Inspired by Algal Polymers

Institute of Scientific and Technical Information of China (English)

H.Bianco-Peled; R.Bitton; P.Potin

2007-01-01

1 Introduction Tissue repair following surgery or trauma has been dominated by sutures,staples and wiring.Although these techniques are well established and widely used,their application often involves pain,unaesthetic results,or bleeding. These limitations emphasize the need for adhesive products to be available to surgeons.2 ResultsA challenging aspect of developing new tissue adhesive is to create a material that can glue wet surfaces.The success of synthetic glues under such an environment is very ...

A dynamic Bayesian network based approach to safety decision support in tunnel construction

International Nuclear Information System (INIS)

Wu, Xianguo; Liu, Huitao; Zhang, Limao; Skibniewski, Miroslaw J.; Deng, Qianli; Teng, Jiaying

2015-01-01

This paper presents a systemic decision approach with step-by-step procedures based on dynamic Bayesian network (DBN), aiming to provide guidelines for dynamic safety analysis of the tunnel-induced road surface damage over time. The proposed DBN-based approach can accurately illustrate the dynamic and updated feature of geological, design and mechanical variables as the construction progress evolves, in order to overcome deficiencies of traditional fault analysis methods. Adopting the predictive, sensitivity and diagnostic analysis techniques in the DBN inference, this approach is able to perform feed-forward, concurrent and back-forward control respectively on a quantitative basis, and provide real-time support before and after an accident. A case study in relating to dynamic safety analysis in the construction of Wuhan Yangtze Metro Tunnel in China is used to verify the feasibility of the proposed approach, as well as its application potential. The relationships between the DBN-based and BN-based approaches are further discussed according to analysis results. The proposed approach can be used as a decision tool to provide support for safety analysis in tunnel construction, and thus increase the likelihood of a successful project in a dynamic project environment. - Highlights: • A dynamic Bayesian network (DBN) based approach for safety decision support is developed. • This approach is able to perform feed-forward, concurrent and back-forward analysis and control. • A case concerning dynamic safety analysis in Wuhan Yangtze Metro Tunnel in China is presented. • DBN-based approach can perform a higher accuracy than traditional static BN-based approach

Use of Glubran 2 acrylic glue in interventional neuroradiology

International Nuclear Information System (INIS)

Raffi, L.; Simonetti, L.; Cenni, P.; Leonardi, M.

2007-01-01

Glubran 2 is a cyanoacrylate-based synthetic glue modified by the addition of a monomer synthesized by the manufacturer. With this material it is possible to obtain the stability of endovascular embolization that is needed to treat tumours and vascular disease. We report our 3-year experience of the use of Glubran for treating extracerebral tumours, spinal tumours, spinal arteriovenous malformations, and brain and spine dural fistulae. Glubran 2 was diluted with Lipiodol and injected in a continuous column with the flow rate monitored by seriography. The injection was stopped when retrograde flow was displayed in the afferent vessel. There were no periprocedural or subsequent clinical complications and the glue resulted in successful selective permanent occlusion with intralesional penetration similar to the angiographic features of microcatheterization. The embolization procedure was technically straightforward and relatively safe. However, Glubran 2 can be difficult to use and the procedure does carry major risks for patients. Glue injection requires in-depth study of the lesion, its circulation and the collateral circulation to avoid severe complications due to inappropriate use. (orig.)

Preliminary report of a sutureless onlay technique for incisional hernia repair using fibrin glue alone for mesh fixation.

Science.gov (United States)

Stoikes, Nathaniel; Webb, David; Powell, Ben; Voeller, Guy

2013-11-01

The Rives repair for ventral/incisional (V/I) hernias involves sublay mesh placement requiring retrorectus dissection and transfascial stitches. Chevrel described a repair by onlaying mesh after a unique primary fascial closure. Although Chevrel fixated mesh to the anterior fascia with sutures, he used fibrin glue for fascial closure reinforcement. We describe an onlay technique with mesh fixated to the anterior fascia solely with fibrin glue without suture fixation. From January 2010 to January 2012, 50 patients underwent a V/I hernia onlay technique with fibrin glue mesh fixation. Records were reviewed for technical details, demographics, mesh characteristics, and postoperative outcomes. Primary fascial closure with interrupted permanent suture was done with or without myofascial advancement flaps. Onlay polypropylene mesh was placed providing 8 cm of overlap. Fibrin glue was applied over the prosthesis and subcutaneous drains were placed. Mean age was 62.4 years. Mean body mass index was 30.1 kg/m(2). Average mesh size was 14.5 cm × 19.1 cm. Mean operative time was 144.4 minutes (range, 38 to 316 minutes). Mean discharge was postoperative Day 2.9 (range, 0 to 15 days). Morbidity included eight seromas, one hematoma, and three wound infections. Seventeen patients required components separation. Mean follow-up was 19.5 months with no recurrences. This is the first series describing fibrin glue alone for mesh fixation for V/I hernia repair. It allows for immediate prosthesis fixation to the anterior fascia. Early results are promising. Potential advantages include less operative time, less technical difficulty, and less long-term pain. A prospective trial is needed to evaluate this approach.

Preoperative embolization of nasopharyngeal angiofibromas: The role of direct percutaneous injection of cyanoacrylate glue in conjunction with particulate endovascular approach

Directory of Open Access Journals (Sweden)

Mohamed Abdel Hakim Osman Kasem

2016-12-01

Conclusion: The embolization of nasopharyngeal angiofibromas before surgery using percutaneous cyanoacrylate glue with endovascular particulate material proved to efficiently devascularize these tumours with lower blood loss during surgery and no major procedural complications.

Bulk physicochemical, interconnectivity, and mechanical properties of calcium phosphate cements-fibrin glue composites for bone substitute applications

NARCIS (Netherlands)

Lopez-Heredia, M.A.; Pattipeilohy, J.; Hsu, S.; Grykien, M.; Weijden, B. van der; Leeuwenburgh, S.C.G.; Salmon, P.; Wolke, J.G.C.; Jansen, J.A.

2013-01-01

Calcium phosphate cements (CPCs) and fibrin glue (FG) are used for surgical applications. Their combination is promising to create bone substitutes able to promote cell attachment and bone remodeling. This study proposes a novel approach to create CPC-FG composites by simultaneous CPC setting and FG

Determination of fibrin glue with antibiotics on collagen production in colon anastomosis

Directory of Open Access Journals (Sweden)

Stanojković Zoran

2008-01-01

Full Text Available Background/Aim. Fibrin glue is used as a matrix for local application of antibiotics. The aim of this study was to determine whether application of fibrin glue in combination with antibiotics can strengthen collagen production, prevent dehiscence of colon anastomoses due to infection, and reduce frequency of mortality and morbidity comparing to the control group and the group with fibrin glue application. Methods. The adult male Wistar rats divided into three groups were used in the experiment. The group 1 was the control one (after partial colon resection, colonic anastomoses performed were not treated, while to the group 2 and the group 3 were applied fibrin glue and fibrin glue with antibiotics (clindamycin and ceftriaxon on the site of anastomoses, respectively. Quality of colonic anastomoses were estimated by means of determination of collagen (L-hydroxyproline amount in the collon wall with anastomoses and histological analysis of this colon segment using light and electronic microscope on the days 5, 7 and 13 postoperatively. Results. The highest morbidity rate was registered in the group 1 (30%, then in the group 2 (13.3% and the lowest one in the group 3 (3.33%; p < 0,05 vs group 1. Mortality rate was significantly higher in the group 1 than in the group 3 (20% and 0%, respectively; p < 0,05. In the postoperative course, the highest concentrations of collagen in the colon wall on the site of anastomoses, which was confirmed by both light and electronic microscopy, were found in the group 3. Conclusion. The application of fibrin glue with antibiotics on colon anastomoses reduces the number of dehiscence, provides good mechanical protection and shorten the time of anastomoses healing.

A bayesian approach to classification criteria for spectacled eiders

Science.gov (United States)

Taylor, B.L.; Wade, P.R.; Stehn, R.A.; Cochrane, J.F.

1996-01-01

To facilitate decisions to classify species according to risk of extinction, we used Bayesian methods to analyze trend data for the Spectacled Eider, an arctic sea duck. Trend data from three independent surveys of the Yukon-Kuskokwim Delta were analyzed individually and in combination to yield posterior distributions for population growth rates. We used classification criteria developed by the recovery team for Spectacled Eiders that seek to equalize errors of under- or overprotecting the species. We conducted both a Bayesian decision analysis and a frequentist (classical statistical inference) decision analysis. Bayesian decision analyses are computationally easier, yield basically the same results, and yield results that are easier to explain to nonscientists. With the exception of the aerial survey analysis of the 10 most recent years, both Bayesian and frequentist methods indicated that an endangered classification is warranted. The discrepancy between surveys warrants further research. Although the trend data are abundance indices, we used a preliminary estimate of absolute abundance to demonstrate how to calculate extinction distributions using the joint probability distributions for population growth rate and variance in growth rate generated by the Bayesian analysis. Recent apparent increases in abundance highlight the need for models that apply to declining and then recovering species.

An Application of Bayesian Approach in Modeling Risk of Death in an Intensive Care Unit.

Science.gov (United States)

Wong, Rowena Syn Yin; Ismail, Noor Azina

2016-01-01

There are not many studies that attempt to model intensive care unit (ICU) risk of death in developing countries, especially in South East Asia. The aim of this study was to propose and describe application of a Bayesian approach in modeling in-ICU deaths in a Malaysian ICU. This was a prospective study in a mixed medical-surgery ICU in a multidisciplinary tertiary referral hospital in Malaysia. Data collection included variables that were defined in Acute Physiology and Chronic Health Evaluation IV (APACHE IV) model. Bayesian Markov Chain Monte Carlo (MCMC) simulation approach was applied in the development of four multivariate logistic regression predictive models for the ICU, where the main outcome measure was in-ICU mortality risk. The performance of the models were assessed through overall model fit, discrimination and calibration measures. Results from the Bayesian models were also compared against results obtained using frequentist maximum likelihood method. The study involved 1,286 consecutive ICU admissions between January 1, 2009 and June 30, 2010, of which 1,111 met the inclusion criteria. Patients who were admitted to the ICU were generally younger, predominantly male, with low co-morbidity load and mostly under mechanical ventilation. The overall in-ICU mortality rate was 18.5% and the overall mean Acute Physiology Score (APS) was 68.5. All four models exhibited good discrimination, with area under receiver operating characteristic curve (AUC) values approximately 0.8. Calibration was acceptable (Hosmer-Lemeshow p-values > 0.05) for all models, except for model M3. Model M1 was identified as the model with the best overall performance in this study. Four prediction models were proposed, where the best model was chosen based on its overall performance in this study. This study has also demonstrated the promising potential of the Bayesian MCMC approach as an alternative in the analysis and modeling of in-ICU mortality outcomes.

Sealing of Gastrointestinal Anastomoses with a Fibrin Glue-Coated Collagen Patch: A Safety Study

DEFF Research Database (Denmark)

Nordentoft, Tyge; Rømer, John; Sørensen, Michael

2007-01-01

gastrointestinal anastomoses with a collagen patch coated with fibrin glue. The study is a prospective, experimental animal study comparing sealed and unsealed gastrointestinal anastomoses. Laparotomy was performed in 11 pigs under general anesthesia. In each pig two anastomoses were performed on the small......Sealing of anastomoses has previously been tested with several methods, including sealing with liquid fibrin glue. Sealing with a collagen patch coated with fibrin glue components has never been systematically examined. The aim of the present study was to determine the safety of sealing...... intestine. One of the anastomoses was sealed with a collagen patch coated with fibrin glue components (TachoSil). The other anastomosis contained no sealing. The pigs were observed for 1 to 6 weeks. The observation period was followed by in vivo examination under general anesthesia and included observation...

Hearing loss in a glue sniffer.

Science.gov (United States)

Williams, D M

1988-10-01

A case is presented of a 27-year-old glue sniffing woman with sensorineural hearing loss, optic atrophy and global brain damage. This form of addiction has not received much attention as a cause of otologic catastrophes, and should be borne in mind where similar cases come to the otolaryngologist.

Interactive Instruction in Bayesian Inference

DEFF Research Database (Denmark)

Khan, Azam; Breslav, Simon; Hornbæk, Kasper

2018-01-01

An instructional approach is presented to improve human performance in solving Bayesian inference problems. Starting from the original text of the classic Mammography Problem, the textual expression is modified and visualizations are added according to Mayer’s principles of instruction. These pri......An instructional approach is presented to improve human performance in solving Bayesian inference problems. Starting from the original text of the classic Mammography Problem, the textual expression is modified and visualizations are added according to Mayer’s principles of instruction....... These principles concern coherence, personalization, signaling, segmenting, multimedia, spatial contiguity, and pretraining. Principles of self-explanation and interactivity are also applied. Four experiments on the Mammography Problem showed that these principles help participants answer the questions...... that an instructional approach to improving human performance in Bayesian inference is a promising direction....

A comparison of the Bayesian and frequentist approaches to estimation

CERN Document Server

Samaniego, Francisco J

2010-01-01

This monograph contributes to the area of comparative statistical inference. Attention is restricted to the important subfield of statistical estimation. The book is intended for an audience having a solid grounding in probability and statistics at the level of the year-long undergraduate course taken by statistics and mathematics majors. The necessary background on Decision Theory and the frequentist and Bayesian approaches to estimation is presented and carefully discussed in Chapters 1-3. The 'threshold problem' - identifying the boundary between Bayes estimators which tend to outperform st

Bayesian Network Induction via Local Neighborhoods

National Research Council Canada - National Science Library

Margaritis, Dimitris

1999-01-01

.... We present an efficient algorithm for learning Bayesian networks from data. Our approach constructs Bayesian networks by first identifying each node's Markov blankets, then connecting nodes in a consistent way...

A Bayesian Approach to Multistage Fitting of the Variation of the Skeletal Age Features

Directory of Open Access Journals (Sweden)

Dong Hua

2009-01-01

Full Text Available Accurate assessment of skeletal maturity is important clinically. Skeletal age assessment is usually based on features encoded in ossification centers. Therefore, it is critical to design a mechanism to capture as much as possible characteristics of features. We have observed that given a feature, there exist stages of the skeletal age such that the variation pattern of the feature differs in these stages. Based on this observation, we propose a Bayesian cut fitting to describe features in response to the skeletal age. With our approach, appropriate positions for stage separation are determined automatically by a Bayesian approach, and a model is used to fit the variation of a feature within each stage. Our experimental results show that the proposed method surpasses the traditional fitting using only one line or one curve not only in the efficiency and accuracy of fitting but also in global and local feature characterization.

Assessing high reliability via Bayesian approach and accelerated tests

International Nuclear Information System (INIS)

Erto, Pasquale; Giorgio, Massimiliano

2002-01-01

Sometimes the assessment of very high reliability levels is difficult for the following main reasons: - the high reliability level of each item makes it impossible to obtain, in a reasonably short time, a sufficient number of failures; - the high cost of the high reliability items to submit to life tests makes it unfeasible to collect enough data for 'classical' statistical analyses. In the above context, this paper presents a Bayesian solution to the problem of estimation of the parameters of the Weibull-inverse power law model, on the basis of a limited number (say six) of life tests, carried out at different stress levels, all higher than the normal one. The over-stressed (i.e. accelerated) tests allow the use of experimental data obtained in a reasonably short time. The Bayesian approach enables one to reduce the required number of failures adding to the failure information the available a priori engineers' knowledge. This engineers' involvement conforms to the most advanced management policy that aims at involving everyone's commitment in order to obtain total quality. A Monte Carlo study of the non-asymptotic properties of the proposed estimators and a comparison with the properties of maximum likelihood estimators closes the work

Bayesian approach in MN low dose of radiation counting

International Nuclear Information System (INIS)

Serna Berna, A.; Alcaraz, M.; Acevedo, C.; Navarro, J. L.; Alcanzar, M. D.; Canteras, M.

2006-01-01

The Micronucleus assay in lymphocytes is a well established technique for the assessment of genetic damage induced by ionizing radiation. Due to the presence of a natural background of MN the net MN is obtained by subtracting this value to the gross value. When very low doses of radiation are given the induced MN is close even lower than the predetermined background value. Furthermore, the damage distribution induced by the radiation follows a Poisson probability distribution. These two facts pose a difficult task to obtain the net counting rate in the exposed situations. It is possible to overcome this problem using a bayesian approach, in which the selection of a priori distributions for the background and net counting rate plays an important role. In the present work we make a detailed analysed using bayesian theory to infer the net counting rate in two different situations: a) when the background is known for an individual sample, using exact value value for the background and Jeffreys prior for the net counting rate, and b) when the background is not known and we make use of a population background distribution as background prior function and constant prior for the net counting rate. (Author)

Bayesian approach for peak detection in two-dimensional chromatography.

Science.gov (United States)

Vivó-Truyols, Gabriel

2012-03-20

A new method for peak detection in two-dimensional chromatography is presented. In a first step, the method starts with a conventional one-dimensional peak detection algorithm to detect modulated peaks. In a second step, a sophisticated algorithm is constructed to decide which of the individual one-dimensional peaks have been originated from the same compound and should then be arranged in a two-dimensional peak. The merging algorithm is based on Bayesian inference. The user sets prior information about certain parameters (e.g., second-dimension retention time variability, first-dimension band broadening, chromatographic noise). On the basis of these priors, the algorithm calculates the probability of myriads of peak arrangements (i.e., ways of merging one-dimensional peaks), finding which of them holds the highest value. Uncertainty in each parameter can be accounted by adapting conveniently its probability distribution function, which in turn may change the final decision of the most probable peak arrangement. It has been demonstrated that the Bayesian approach presented in this paper follows the chromatographers' intuition. The algorithm has been applied and tested with LC × LC and GC × GC data and takes around 1 min to process chromatograms with several thousands of peaks.

Photoclickable dendritic molecular glue: noncovalent-to-covalent photochemical transformation of protein hybrids.

Science.gov (United States)

Uchida, Noriyuki; Okuro, Kou; Niitani, Yamato; Ling, Xiao; Ariga, Takayuki; Tomishige, Michio; Aida, Takuzo

2013-03-27

A water-soluble dendron with a fluorescein isothiocyanate (FITC) fluorescent label and bearing nine pendant guanidinium ion (Gu(+))/benzophenone (BP) pairs at its periphery (Glue(BP)-FITC) serves as a "photoclickable molecular glue". By multivalent salt-bridge formation between Gu(+) ions and oxyanions, Glue(BP)-FITC temporarily adheres to a kinesin/microtubule hybrid. Upon subsequent exposure to UV light, this noncovalent binding is made permanent via a cross-linking reaction mediated by carbon radicals derived from the photoexcited BP units. This temporal-to-permanent transformation by light occurs quickly and efficiently in this preorganized state, allowing the movements of microtubules on a kinesin-coated glass plate to be photochemically controlled. A fundamental difference between such temporal and permanent bindings was visualized by the use of "optical tweezers".

Bayesian Approaches to Imputation, Hypothesis Testing, and Parameter Estimation

Science.gov (United States)

Ross, Steven J.; Mackey, Beth

2015-01-01

This chapter introduces three applications of Bayesian inference to common and novel issues in second language research. After a review of the critiques of conventional hypothesis testing, our focus centers on ways Bayesian inference can be used for dealing with missing data, for testing theory-driven substantive hypotheses without a default null…

Effect of BioGlue on the incidence of pancreatic fistula following pancreas resection.

Science.gov (United States)

Fisher, William E; Chai, Christy; Hodges, Sally E; Wu, Meng-Fen; Hilsenbeck, Susan G; Brunicardi, F Charles

2008-05-01

Despite numerous modifications of surgical technique, pancreatic fistula remains a serious problem and occurs in about 10% of patients following pancreas resection. BioGlue is a new sealant that creates a flexible mechanical seal within minutes independent of the body's clotting mechanism. Application of BioGlue sealant will reduce the incidence of pancreatic fistula following pancreas resection. A retrospective cohort study was performed with 64 patients undergoing pancreas resection. BioGlue sealant was applied to the pancreatic anastomosis (Whipple) or resection margin (distal pancreatectomy) in 32 cases. Factors that could affect the rate of postoperative pancreatic fistula were recorded. Pancreatic fistula was defined as greater than 50 ml of drain output with an amylase content greater than three times normal serum value after postoperative day 10. To improve the sensitivity of our study, we also examined pancreatic fistula with a strict definition of any drain output on or after postoperative day 3 with a high amylase content and graded the fistulas in terms of clinical severity. Grade A leaks were defined as subclinical. Grade B leaks required some response such as making the patient nil per os, parenteral nutrition, octreotide, antibiotics, or a prolonged hospital stay. Grade C leaks were defined as serious and life threatening. They were associated with hemorrhage, sepsis, resulted in deterioration of other organ systems, and mandated intensive care. Comparisons between the two groups were made using the chi-square test or Fisher's exact test for categorical variables and by the Wilcoxon rank-sum test for continuous variables. P values of 0.05 or less were deemed statistically significant. There were no differences between the patients who received BioGlue and the control cohort in terms of comorbid conditions, tumor location, texture of the pancreas, size of the pancreatic duct, or surgical technique. By the common definition, pancreatic fistula occurred

Optimization of performance parameters for large area silicon photomultipliers for use in the GlueX experiment

Science.gov (United States)

Janzen, Kathryn Louise

Largely because of their resistance to magnetic fields, silicon photomultipliers (SiPMs) are being considered as the readout for the GlueX Barrel Calorimeter, a key component of the GlueX detector located immediately inside a 2.2 T superconducting solenoid. SiPMs with active area 1 x 1 mm2 have been investigated for use in other experiments, but detectors with larger active areas are required for the GlueX BCAL. This puts the GlueX collaboration in the unique position of being pioneers in the use of this frontend detection revolution by driving the technology for larger area sensors. SensL, a photonics research and development company in Ireland, has been collaborating with the University of Regina GlueX group to develop prototype large area SiPMs comprising 16 - 3x3 mm2 cells assembled in a close-packed matrix. Performance parameters of individual SensL 1x1 mm2 and 3x3 mm2 SiPMs along with prototype SensL SiPM arrays are tested, including current versus voltage characteristics, photon detection efficiency, and gain uniformity, in an effort to determine the suitability of these detectors to the GlueX BCAL readout.

[Chemical hazards when working with solvent glues].

Science.gov (United States)

Domański, Wojciech; Makles, Zbigniew

2012-01-01

Solvent glues are used in a wide variety of industries, e.g., textile, footwear and rubber. The problem of workers' exposure to solvent vapors is rarely tackled within the area of occupational safety and health in small and medium-sized enterprises. In order to assess exposure to solvents, organic solvents emitted by glues were identified in the samples of workplace air. The concentration of acetone, benzene, cyclohexane, ethylbenzene, n-hexane, methylcyclohexane, butyl acetate and toluene were determined. The obtained results evidenced the presence of cyclohexane, ethylbenzene, ethylcyclohexane, heptane, n-hexane, o-xylene, methylcyclohexane, methylcyclopentane, butyl acetate and toluene in workplace air. The concentration of those compounds in workplace air was low, usually below 0.15 of MAC. At some workstations the presence of benzene was also observed. Occupational risk was assessed at workstations where gluing took place. It showed that the risk at those workstations was medium or low.

Characterization of a Saccharomyces cerevisiae fermentation process for production of a therapeutic recombinant protein using a multivariate Bayesian approach.

Science.gov (United States)

Fu, Zhibiao; Baker, Daniel; Cheng, Aili; Leighton, Julie; Appelbaum, Edward; Aon, Juan

2016-05-01

The principle of quality by design (QbD) has been widely applied to biopharmaceutical manufacturing processes. Process characterization is an essential step to implement the QbD concept to establish the design space and to define the proven acceptable ranges (PAR) for critical process parameters (CPPs). In this study, we present characterization of a Saccharomyces cerevisiae fermentation process using risk assessment analysis, statistical design of experiments (DoE), and the multivariate Bayesian predictive approach. The critical quality attributes (CQAs) and CPPs were identified with a risk assessment. The statistical model for each attribute was established using the results from the DoE study with consideration given to interactions between CPPs. Both the conventional overlapping contour plot and the multivariate Bayesian predictive approaches were used to establish the region of process operating conditions where all attributes met their specifications simultaneously. The quantitative Bayesian predictive approach was chosen to define the PARs for the CPPs, which apply to the manufacturing control strategy. Experience from the 10,000 L manufacturing scale process validation, including 64 continued process verification batches, indicates that the CPPs remain under a state of control and within the established PARs. The end product quality attributes were within their drug substance specifications. The probability generated with the Bayesian approach was also used as a tool to assess CPP deviations. This approach can be extended to develop other production process characterization and quantify a reliable operating region. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:799-812, 2016. © 2016 American Institute of Chemical Engineers.

Bayesian Inference on Gravitational Waves

Directory of Open Access Journals (Sweden)

Asad Ali

2015-12-01

Full Text Available The Bayesian approach is increasingly becoming popular among the astrophysics data analysis communities. However, the Pakistan statistics communities are unaware of this fertile interaction between the two disciplines. Bayesian methods have been in use to address astronomical problems since the very birth of the Bayes probability in eighteenth century. Today the Bayesian methods for the detection and parameter estimation of gravitational waves have solid theoretical grounds with a strong promise for the realistic applications. This article aims to introduce the Pakistan statistics communities to the applications of Bayesian Monte Carlo methods in the analysis of gravitational wave data with an  overview of the Bayesian signal detection and estimation methods and demonstration by a couple of simplified examples.

2-octyl-cyanoacrylate glue for fixation of STSG in genitourinary tissue defects due to Fournier gangrene: a preliminary trial.

Science.gov (United States)

Sivrioğlu, Nazan; Irkören, Saime; Ceylan, Ender; Sonel, Ali Murat; Copçu, Eray

2013-05-01

In these reported cases, we observed the outcomes of skin take and wound healing using 2-octyl-cyanoacrylate glue, which was used as tissue glue in the reconstruction of complex genital skin loss due to fournier gangrene. Fifteen patients with Fournier's gangrene were treated in this study. After initial surgical debridement, all defects were repaired using STSG. In this method a thin layer of 2-octyl-cyanoacrylate was dripped on the recipient site immediately before graft application. All wounds were followed up postoperatively and observed for evidence of graft take, seroma or hematoma formation, drainage, and infection. Patient and physician satisfaction were also determined. Grafts were completely accepted in all fifteen patients. None of the patients had wound infection, seroma, hematoma, or other complications. Use of 2-octyl-cyanoacrylate glue (Glueseal) for STSG fixation in complex genital skin defects after Fournier gangrene may be an acceptable alternative to conventional surgical closure with a good cosmetic outcome. Further studies are needed to confirm our initial success with this approach.

Sediment Curve Uncertainty Estimation Using GLUE and Bootstrap Methods

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aboalhasan fathabadi

2017-02-01

Full Text Available Introduction: In order to implement watershed practices to decrease soil erosion effects it needs to estimate output sediment of watershed. Sediment rating curve is used as the most conventional tool to estimate sediment. Regarding to sampling errors and short data, there are some uncertainties in estimating sediment using sediment curve. In this research, bootstrap and the Generalized Likelihood Uncertainty Estimation (GLUE resampling techniques were used to calculate suspended sediment loads by using sediment rating curves. Materials and Methods: The total drainage area of the Sefidrood watershed is about 560000 km2. In this study uncertainty in suspended sediment rating curves was estimated in four stations including Motorkhane, Miyane Tonel Shomare 7, Stor and Glinak constructed on Ayghdamosh, Ghrangho, GHezelOzan and Shahrod rivers, respectively. Data were randomly divided into a training data set (80 percent and a test set (20 percent by Latin hypercube random sampling.Different suspended sediment rating curves equations were fitted to log-transformed values of sediment concentration and discharge and the best fit models were selected based on the lowest root mean square error (RMSE and the highest correlation of coefficient (R2. In the GLUE methodology, different parameter sets were sampled randomly from priori probability distribution. For each station using sampled parameter sets and selected suspended sediment rating curves equation suspended sediment concentration values were estimated several times (100000 to 400000 times. With respect to likelihood function and certain subjective threshold, parameter sets were divided into behavioral and non-behavioral parameter sets. Finally using behavioral parameter sets the 95% confidence intervals for suspended sediment concentration due to parameter uncertainty were estimated. In bootstrap methodology observed suspended sediment and discharge vectors were resampled with replacement B (set to

The Effect of Seaweed Glue in the Separation of Copper–Molybdenum Sulphide Ore by Flotation

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Zhixiang Chen

2018-01-01

Full Text Available Flotation separation of chalcopyrite from molybdenite was studied using seaweed glue (SEG as a depressant. Flotation process and mechanism were examined by response surface methodology, flotation tests, adsorption tests, zeta potential measurements and fourier transform infrared (FT-IR spectra. Response surface methodology with a Box–Behnken design suggested the optimal reagent schedule: pH 4, depressant seaweed glue 197 mg/L, collector amyl xanthate 16 mg/L and frother (methyl isobutyl carbinol 20 mg/L, and selective separation of chalcopyrite and molybdenite was achieved by flotation. Comparison of SEG and traditional depressants indicated that the SEG could achieve a similar separation efficiency, and exhibited the advantages of environmental compatibility and economic adaptability. Co-adsorption of seaweed glue and amyl xanthate occurred on the surface of molybdenite, and is explained to happen through distinct mechanisms due to the heterogeneous nature of the surface. It is likely that seaweed glue depresses molybdenite by covering the dixanthogen resulting from adsorption of xanthate ions. It is shown that seaweed glue is as effective a depressant of Cu/Mo separation as cyanide.

Photocurable surgical tissue adhesive glues composed of photoreactive gelatin and poly(ethylene glycol) diacrylate.

Science.gov (United States)

Nakayama, Y; Matsuda, T

1999-01-01

This article presents a novel photochemically driven surgical tissue adhesive technology using photoreactive gelatins and a water-soluble difunctional macromer (poly(ethylene glycol) diacrylate: PEGDA).The gelatins were partially derivatized with photoreactive groups, such as ultraviolet light (UV)-reactive benzophenone and visible light-reactive xanthene dye (e.g., fluorescein sodium salt, eosin Y, and rose bengal). A series of the prepared photocurable tissue adhesive glues, consisting of the photoreactive gelatin, PEGDA, and a saline solution with or without ascorbic acid as a reducing agent, were viscous solutions under warming, and their effectiveness was evaluated as hemostasis- and anastomosis-aid in cardiovascular surgery. Regardless of the type of photoreactive groups, the irradiation of the photocurable tissue adhesive glues by UV or visible light within 1 min produced water-swollen gels, which had a high adhesive strength to wet collagen film. These were due to the synergistic action of photoreactive group-initiated photo-cross-linking and photograft polymerization. An increase in the irradiation time resulted in increased gel yield and reduced water swellability. A decrease in the molecular weight of PEGDA and an increase in concentration of both gelatin and PEGDA resulted in reduced water swellability and increased tensile and burst strengths of the resultant gels. In rats whose livers were injured with a trephine in laparotomy, the bleeding spots were coated with the photocurable adhesive glue and irradiated through an optical fiber. The coated solution was immediately converted to a swollen gel. The gel was tightly adhered to the liver tissue presumably by interpenetration, and concomitantly hemostasis was completed. The anastomosis treatment with the photocurable glue in the canine abdominal or thoracic aortas incised with a knife resulted in little bleeding under pulsatile flow after declamping. Histological examination showed that the glues

Topics in Bayesian statistics and maximum entropy

International Nuclear Information System (INIS)

Mutihac, R.; Cicuttin, A.; Cerdeira, A.; Stanciulescu, C.

1998-12-01

Notions of Bayesian decision theory and maximum entropy methods are reviewed with particular emphasis on probabilistic inference and Bayesian modeling. The axiomatic approach is considered as the best justification of Bayesian analysis and maximum entropy principle applied in natural sciences. Particular emphasis is put on solving the inverse problem in digital image restoration and Bayesian modeling of neural networks. Further topics addressed briefly include language modeling, neutron scattering, multiuser detection and channel equalization in digital communications, genetic information, and Bayesian court decision-making. (author)

Sparse Event Modeling with Hierarchical Bayesian Kernel Methods

Science.gov (United States)

2016-01-05

SECURITY CLASSIFICATION OF: The research objective of this proposal was to develop a predictive Bayesian kernel approach to model count data based on...several predictive variables. Such an approach, which we refer to as the Poisson Bayesian kernel model, is able to model the rate of occurrence of... kernel methods made use of: (i) the Bayesian property of improving predictive accuracy as data are dynamically obtained, and (ii) the kernel function

Magnetic Retraction of Bowel by Intraluminal Injectable Cyanoacrylate-Based Magnetic Glue

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Zhigang Wang

2013-01-01

Full Text Available Magnetic retraction offers advantages over physical retraction by graspers because of reduced tissue trauma. The objectives of this study are to investigate a novel method of magnetisation of bowel segments by intraluminal injection of magnetic glue and to demonstrate the feasibility of magnetic retraction of bowel with sufficient force during minimal access surgery. Following an initial materials characterisation study, selected microparticles of stainless steel (SS410-μPs were mixed with chosen cyanoacrylate glue (Loctite 4014. During intraluminal injection of the magnetic glue using ex vivo porcine colonic segments, a magnetic probe placed at the injected site ensured that the SS410-μPs aggregated during glue polymerisation to form an intraluminal mucosally adherent coagulum. The magnetised colonic segments were retracted by magnetic probes (5 and 10 mm placed external to the bowel wall. A tensiometer was used to record the retraction force. With an injected volume of 2 mL in a particle concentration of 1 g/mL, this technique produced maximal magnetic retraction forces of 2.24 ± 0.23 N and 5.11 ± 0.34 N (, with use of 5 and 10 mm probes, respectively. The results indicate that the formation of an intraluminal coagulum based on SS410-μPs and Loctite 4014 produces sufficient magnetic retraction for bowel retraction.

An evaluation of the Bayesian approach to fitting the N-mixture model for use with pseudo-replicated count data

Science.gov (United States)

Toribo, S.G.; Gray, B.R.; Liang, S.

2011-01-01

The N-mixture model proposed by Royle in 2004 may be used to approximate the abundance and detection probability of animal species in a given region. In 2006, Royle and Dorazio discussed the advantages of using a Bayesian approach in modelling animal abundance and occurrence using a hierarchical N-mixture model. N-mixture models assume replication on sampling sites, an assumption that may be violated when the site is not closed to changes in abundance during the survey period or when nominal replicates are defined spatially. In this paper, we studied the robustness of a Bayesian approach to fitting the N-mixture model for pseudo-replicated count data. Our simulation results showed that the Bayesian estimates for abundance and detection probability are slightly biased when the actual detection probability is small and are sensitive to the presence of extra variability within local sites.

Bayesian Data Analysis (lecture 2)

CERN Multimedia

CERN. Geneva

2018-01-01

framework but we will also go into more detail and discuss for example the role of the prior. The second part of the lecture will cover further examples and applications that heavily rely on the bayesian approach, as well as some computational tools needed to perform a bayesian analysis.

Bayesian Data Analysis (lecture 1)

CERN Multimedia

CERN. Geneva

2018-01-01

framework but we will also go into more detail and discuss for example the role of the prior. The second part of the lecture will cover further examples and applications that heavily rely on the bayesian approach, as well as some computational tools needed to perform a bayesian analysis.

Alternative glues for the production of ATLAS silicon strip modules for the Phase-II upgrade of the ATLAS Inner Detector

Science.gov (United States)

Poley, L.; Bloch, I.; Edwards, S.; Friedrich, C.; Gregor, I.-M.; Jones, T.; Lacker, H.; Pyatt, S.; Rehnisch, L.; Sperlich, D.; Wilson, J.

2016-05-01

The Phase-II upgrade of the ATLAS detector for the High Luminosity Large Hadron Collider (HL-LHC) includes the replacement of the current Inner Detector with an all-silicon tracker consisting of pixel and strip detectors. The current Phase-II detector layout requires the construction of 20,000 strip detector modules consisting of sensor, circuit boards and readout chips, which are connected mechanically using adhesives. The adhesive used initially between readout chips and circuit board is a silver epoxy glue as was used in the current ATLAS SemiConductor Tracker (SCT). However, this glue has several disadvantages, which motivated the search for an alternative. This paper presents a study of six ultra-violet (UV) cure glues and a glue pad for possible use in the assembly of silicon strip detector modules for the ATLAS upgrade. Trials were carried out to determine the ease of use, thermal conduction and shear strength. Samples were thermally cycled, radiation hardness and corrosion resistance were also determined. These investigations led to the exclusion of three UV cure glues as well as the glue pad. Three UV cure glues were found to be possible better alternatives than silver loaded glue. Results from electrical tests of first prototype modules constructed using these glues are presented.

Alternative glues for the production of ATLAS silicon strip modules for the Phase-II upgrade of the ATLAS Inner Detector

International Nuclear Information System (INIS)

Poley, L.; Bloch, I.; Friedrich, C.; Gregor, I.-M.; Edwards, S.; Pyatt, S.; Wilson, J.; Jones, T.; Lacker, H.; Rehnisch, L.; Sperlich, D.

2016-01-01

The Phase-II upgrade of the ATLAS detector for the High Luminosity Large Hadron Collider (HL-LHC) includes the replacement of the current Inner Detector with an all-silicon tracker consisting of pixel and strip detectors. The current Phase-II detector layout requires the construction of 20,000 strip detector modules consisting of sensor, circuit boards and readout chips, which are connected mechanically using adhesives. The adhesive used initially between readout chips and circuit board is a silver epoxy glue as was used in the current ATLAS SemiConductor Tracker (SCT). However, this glue has several disadvantages, which motivated the search for an alternative. This paper presents a study of six ultra-violet (UV) cure glues and a glue pad for possible use in the assembly of silicon strip detector modules for the ATLAS upgrade. Trials were carried out to determine the ease of use, thermal conduction and shear strength. Samples were thermally cycled, radiation hardness and corrosion resistance were also determined. These investigations led to the exclusion of three UV cure glues as well as the glue pad. Three UV cure glues were found to be possible better alternatives than silver loaded glue. Results from electrical tests of first prototype modules constructed using these glues are presented.

Bayesian Statistics: Concepts and Applications in Animal Breeding – A Review

Directory of Open Access Journals (Sweden)

Lsxmikant-Sambhaji Kokate

2011-07-01

Full Text Available Statistics uses two major approaches- conventional (or frequentist and Bayesian approach. Bayesian approach provides a complete paradigm for both statistical inference and decision making under uncertainty. Bayesian methods solve many of the difficulties faced by conventional statistical methods, and extend the applicability of statistical methods. It exploits the use of probabilistic models to formulate scientific problems. To use Bayesian statistics, there is computational difficulty and secondly, Bayesian methods require specifying prior probability distributions. Markov Chain Monte-Carlo (MCMC methods were applied to overcome the computational difficulty, and interest in Bayesian methods was renewed. In Bayesian statistics, Bayesian structural equation model (SEM is used. It provides a powerful and flexible approach for studying quantitative traits for wide spectrum problems and thus it has no operational difficulties, with the exception of some complex cases. In this method, the problems are solved at ease, and the statisticians feel it comfortable with the particular way of expressing the results and employing the software available to analyze a large variety of problems.

Comparative Study of Inference Methods for Bayesian Nonnegative Matrix Factorisation

DEFF Research Database (Denmark)

Brouwer, Thomas; Frellsen, Jes; Liò, Pietro

2017-01-01

In this paper, we study the trade-offs of different inference approaches for Bayesian matrix factorisation methods, which are commonly used for predicting missing values, and for finding patterns in the data. In particular, we consider Bayesian nonnegative variants of matrix factorisation and tri......-factorisation, and compare non-probabilistic inference, Gibbs sampling, variational Bayesian inference, and a maximum-a-posteriori approach. The variational approach is new for the Bayesian nonnegative models. We compare their convergence, and robustness to noise and sparsity of the data, on both synthetic and real...

Linkage and mapping analyses of the no glue egg gene Ng in the ...

African Journals Online (AJOL)

In the silkworm, Bombyx mori, no glue egg is mainly controlled by Ng (No glue) gene, which is located on the 12th chromosome. Owning to a lack of crossing over in females, reciprocal backcrossed F1 (BC1) progenies were used for linkage analysis and mapping of the Ng gene based on the simple sequence repeats ...

A Bayesian Approach to Interactive Retrieval

Science.gov (United States)

Tague, Jean M.

1973-01-01

A probabilistic model for interactive retrieval is presented. Bayesian statistical decision theory principles are applied: use of prior and sample information about the relationship of document descriptions to query relevance; maximization of expected value of a utility function, to the problem of optimally restructuring search strategies in an…

Banking Crisis Early Warning Model based on a Bayesian Model Averaging Approach

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Taha Zaghdoudi

2016-08-01

Full Text Available The succession of banking crises in which most have resulted in huge economic and financial losses, prompted several authors to study their determinants. These authors constructed early warning models to prevent their occurring. It is in this same vein as our study takes its inspiration. In particular, we have developed a warning model of banking crises based on a Bayesian approach. The results of this approach have allowed us to identify the involvement of the decline in bank profitability, deterioration of the competitiveness of the traditional intermediation, banking concentration and higher real interest rates in triggering bank crisis.

SHEAR STRENGTH IN THE GLUE LINE OF Eucalyptus sp. AND Pinus sp.WOOD

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Juliana Jerásio Bianche

Full Text Available ABSTRACT To evaluate the adhesive efficiency on the union of glued joints in a particular temperature and humidity conditions for a specified time the adhesive must be submitted to specific load tests, such as shear in the glue line. The objective of this study was to evaluate the shear strength in the glue line of Eucalyptus sp and Pinus sp.woods. Five adhesives (castor oil, sodium silicate, modified silicate, , PVA and resorcinol-formaldehyde, three weights (150 g/m2, 200 g/m2, and 250 g/m2 and two species (Eucalyptus sp. and Pinus sp. of wood were used. Twelve specimens were obtained from each repetition per treatment, corresponding to 108 specimens that were conditioned at a temperature of 23 ± 1°C and relative humidity of 50 ± 2%. The interaction between the weight and type of adhesive was significant for the shear strength in the glue line of eucalyptus wood. However, no interaction between the weight and the adhesive was found for pinus, only the isolated from the adhesive effect. Chemical bonds originated in the polymerization of resorcinol-formaldehyde adhesives and castor bi-component conferred upon these adhesives the greatest resistance in the glue line. Castor and resorcinol-formaldehyde adhesives showed the highest shear strength values in the line of glue and wood failure. Castor adhesive presented satisfactory performance for bonding of eucalyptus and pine woods.

Predicting Graduation Rates at 4-Year Broad Access Institutions Using a Bayesian Modeling Approach

Science.gov (United States)

Crisp, Gloria; Doran, Erin; Salis Reyes, Nicole A.

2018-01-01

This study models graduation rates at 4-year broad access institutions (BAIs). We examine the student body, structural-demographic, and financial characteristics that best predict 6-year graduation rates across two time periods (2008-2009 and 2014-2015). A Bayesian model averaging approach is utilized to account for uncertainty in variable…

An Application of Bayesian Approach in Modeling Risk of Death in an Intensive Care Unit.

Directory of Open Access Journals (Sweden)

Rowena Syn Yin Wong

Full Text Available There are not many studies that attempt to model intensive care unit (ICU risk of death in developing countries, especially in South East Asia. The aim of this study was to propose and describe application of a Bayesian approach in modeling in-ICU deaths in a Malaysian ICU.This was a prospective study in a mixed medical-surgery ICU in a multidisciplinary tertiary referral hospital in Malaysia. Data collection included variables that were defined in Acute Physiology and Chronic Health Evaluation IV (APACHE IV model. Bayesian Markov Chain Monte Carlo (MCMC simulation approach was applied in the development of four multivariate logistic regression predictive models for the ICU, where the main outcome measure was in-ICU mortality risk. The performance of the models were assessed through overall model fit, discrimination and calibration measures. Results from the Bayesian models were also compared against results obtained using frequentist maximum likelihood method.The study involved 1,286 consecutive ICU admissions between January 1, 2009 and June 30, 2010, of which 1,111 met the inclusion criteria. Patients who were admitted to the ICU were generally younger, predominantly male, with low co-morbidity load and mostly under mechanical ventilation. The overall in-ICU mortality rate was 18.5% and the overall mean Acute Physiology Score (APS was 68.5. All four models exhibited good discrimination, with area under receiver operating characteristic curve (AUC values approximately 0.8. Calibration was acceptable (Hosmer-Lemeshow p-values > 0.05 for all models, except for model M3. Model M1 was identified as the model with the best overall performance in this study.Four prediction models were proposed, where the best model was chosen based on its overall performance in this study. This study has also demonstrated the promising potential of the Bayesian MCMC approach as an alternative in the analysis and modeling of in-ICU mortality outcomes.

Identification and quantification of glue-like off-odors in elastic therapeutic tapes.

Science.gov (United States)

Denk, Philipp; Buettner, Andrea

2018-05-01

Elastic therapeutic tapes are an important tool in the field of physical therapy and medicine. These tapes contain types of adhesive. However, sensory evaluations revealed the release of pronounced and irritating odors of the tapes. Negative odors were, amongst others, reported in elastic therapeutic tapes containing acrylic adhesives. In this study, the odor of four different tape samples was evaluated applying a descriptive analysis approach carried out by a trained sensory panel. Afterwards, the volatile compounds were recovered from the samples by solvent extraction and isolated by solvent-assisted flavor evaporation (SAFE). The obtained distillates were subsequently analyzed by gas chromatography-olfactometry (GC-O) and two-dimensional GC-O coupled with mass spectrometry (2D-GC-MS/O). To determine the most potent odorants in the distillates, odor extract dilution analyses (OEDA) were carried out. Thirty-one odorants were successfully identified using this approach, which were all described for the first time as odorants in tapes. Amongst the set of volatiles, unsaturated and saturated aldehydes were present, eliciting fatty, soapy, and citrus-like odor impressions, as well as a range of glue-like, moldy, and fruity smelling odor-active volatiles, such as 2-ethyl-1-hexanol, butyl benzoate, and 3-phenyltoluene. Based on their relative intensities, the concentrations of the glue-like smelling substances were determined: 2-ethyl-1-hexanol, present in all samples, was determined with concentrations ranging from 10 to 200 mg/kg in the investigated tapes.

A novel critical infrastructure resilience assessment approach using dynamic Bayesian networks

Science.gov (United States)

Cai, Baoping; Xie, Min; Liu, Yonghong; Liu, Yiliu; Ji, Renjie; Feng, Qiang

2017-10-01

The word resilience originally originates from the Latin word "resiliere", which means to "bounce back". The concept has been used in various fields, such as ecology, economics, psychology, and society, with different definitions. In the field of critical infrastructure, although some resilience metrics are proposed, they are totally different from each other, which are determined by the performances of the objects of evaluation. Here we bridge the gap by developing a universal critical infrastructure resilience metric from the perspective of reliability engineering. A dynamic Bayesian networks-based assessment approach is proposed to calculate the resilience value. A series, parallel and voting system is used to demonstrate the application of the developed resilience metric and assessment approach.

Bayesian approach to MSD-based analysis of particle motion in live cells.

Science.gov (United States)

Monnier, Nilah; Guo, Syuan-Ming; Mori, Masashi; He, Jun; Lénárt, Péter; Bathe, Mark

2012-08-08

Quantitative tracking of particle motion using live-cell imaging is a powerful approach to understanding the mechanism of transport of biological molecules, organelles, and cells. However, inferring complex stochastic motion models from single-particle trajectories in an objective manner is nontrivial due to noise from sampling limitations and biological heterogeneity. Here, we present a systematic Bayesian approach to multiple-hypothesis testing of a general set of competing motion models based on particle mean-square displacements that automatically classifies particle motion, properly accounting for sampling limitations and correlated noise while appropriately penalizing model complexity according to Occam's Razor to avoid over-fitting. We test the procedure rigorously using simulated trajectories for which the underlying physical process is known, demonstrating that it chooses the simplest physical model that explains the observed data. Further, we show that computed model probabilities provide a reliability test for the downstream biological interpretation of associated parameter values. We subsequently illustrate the broad utility of the approach by applying it to disparate biological systems including experimental particle trajectories from chromosomes, kinetochores, and membrane receptors undergoing a variety of complex motions. This automated and objective Bayesian framework easily scales to large numbers of particle trajectories, making it ideal for classifying the complex motion of large numbers of single molecules and cells from high-throughput screens, as well as single-cell-, tissue-, and organism-level studies. Copyright © 2012 Biophysical Society. Published by Elsevier Inc. All rights reserved.

BioGlue iceball stabilization to minimize the risk of hemorrhage during laparoscopic renal cryoablation.

Science.gov (United States)

Mues, Adam C; Graversen, Joseph A; Truesdale, Matthew D; Casazza, Cristin; Landman, Jaime

2011-08-01

To evaluate the application of a BioGlue adhesive shell to minimize iceball fracture. Iceball fracture and hemorrhage is common with laparoscopic cryoablation (LCA) of larger (>4 cm) renal tumors. Twenty large iceballs were created in porcine kidneys using 3 cryoablation probes in a nonsurvival study. Each kidney underwent an upper and lower pole ablation. One pole in each kidney was covered with 5 mL of BioGlue and the opposite pole served as a control. A double freeze-thaw cycle was performed (10 minutes freeze and 5 minutes active thaw) in both renal poles simultaneously. The probes were removed and the sites were monitored for 20 minutes under direct vision. Fracture length (mm), severity of fracture depth, severity of bleeding (absent, mild, moderate, severe), and estimated blood loss (EBL) (mL) were recorded. In the control group, the mean fracture length was 1.9 mm (range, 0-3 mm). Blood loss was absent in 10%, mild in 60%, and moderate in 30% of ablations. The mean EBL was 20.5 mL (range, 0-50 mL). For the BioGlue ablations, there were no parenchymal fractures. Blood loss was mild in 30% and absent in 70% of sites with an average EBL of 5 mL (range, 0-20). Two bleeding sites occurred as a result of subcapsular hematomas caused by initial probe placement. BioGlue application minimized the frequency and magnitude of renal fracture. EBL was lower with BioGlue application and most sites demonstrated no postablation bleeding. Further clinical study of the BioGlue shell should be performed to confirm these results. Copyright © 2011 Elsevier Inc. All rights reserved.

Fibrin glue on an aortic cusp detected by transesophageal echocardiography after valve-sparing aortic valve replacement: a case report.

Science.gov (United States)

Nakahira, Junko; Ishii, Hisanari; Sawai, Toshiyuki; Minami, Toshiaki

2015-03-07

Fibrin glue is used commonly during cardiac surgery but can behave as an intracardiac abnormal foreign body following surgery. There have been few such cases reported, and they were typically noticed only because of the resulting catastrophic cardiac conditions, such as valvular malfunction. We report a case where, for the first time, transesophageal echocardiography was used to detected fibrin glue that was adherent to the ventricular side of a patient's aortic valve immediately after aortic declamping. A 45-year-old Japanese man with Marfan syndrome underwent an aortic valve-sparing operation to treat moderate aortic valve regurgitation resulting from enlargement of his right coronary cusp. Fibrin glue was lightly applied to the suture line between the previous and new grafts. Transesophageal echocardiography performed prior to weaning from the cardiopulmonary bypass revealed mild aortic valve regurgitation in addition to a mobile membranous structure attached to the ventricular side of his aortic valve. It was identified as fibrin glue. We resolved the regurgitation by removing the fibrin glue and repeating the aortic cusp plication. The patient had no complications during recovery. Fibrin glue can act as an intracardiac foreign body and lead to a potentially fatal embolism. We demonstrated the use of transesophageal echocardiography to detect a fibrin glue-derived intracardiac abnormal foreign body and to confirm its removal. To the best of our knowledge, this is the first case where fibrin glue adherent to the aortic valve was detected by transesophageal echocardiography. These findings demonstrate the importance of using transesophageal echocardiography during cardiac surgery that involves using biological glues.

Bayesian approach to peak deconvolution and library search for high resolution gas chromatography - Mass spectrometry

NARCIS (Netherlands)

Barcaru, A.; Mol, H.G.J.; Tienstra, M.; Vivó-Truyols, G.

2017-01-01

A novel probabilistic Bayesian strategy is proposed to resolve highly coeluting peaks in high-resolution GC-MS (Orbitrap) data. Opposed to a deterministic approach, we propose to solve the problem probabilistically, using a complete pipeline. First, the retention time(s) for a (probabilistic) number

Variations on Bayesian Prediction and Inference

Science.gov (United States)

2016-05-09

inference 2.2.1 Background There are a number of statistical inference problems that are not generally formulated via a full probability model...problem of inference about an unknown parameter, the Bayesian approach requires a full probability 1. REPORT DATE (DD-MM-YYYY) 4. TITLE AND...the problem of inference about an unknown parameter, the Bayesian approach requires a full probability model/likelihood which can be an obstacle

Kernel Bayesian ART and ARTMAP.

Science.gov (United States)

Masuyama, Naoki; Loo, Chu Kiong; Dawood, Farhan

2018-02-01

Adaptive Resonance Theory (ART) is one of the successful approaches to resolving "the plasticity-stability dilemma" in neural networks, and its supervised learning model called ARTMAP is a powerful tool for classification. Among several improvements, such as Fuzzy or Gaussian based models, the state of art model is Bayesian based one, while solving the drawbacks of others. However, it is known that the Bayesian approach for the high dimensional and a large number of data requires high computational cost, and the covariance matrix in likelihood becomes unstable. This paper introduces Kernel Bayesian ART (KBA) and ARTMAP (KBAM) by integrating Kernel Bayes' Rule (KBR) and Correntropy Induced Metric (CIM) to Bayesian ART (BA) and ARTMAP (BAM), respectively, while maintaining the properties of BA and BAM. The kernel frameworks in KBA and KBAM are able to avoid the curse of dimensionality. In addition, the covariance-free Bayesian computation by KBR provides the efficient and stable computational capability to KBA and KBAM. Furthermore, Correntropy-based similarity measurement allows improving the noise reduction ability even in the high dimensional space. The simulation experiments show that KBA performs an outstanding self-organizing capability than BA, and KBAM provides the superior classification ability than BAM, respectively. Copyright © 2017 Elsevier Ltd. All rights reserved.

Bayesian non- and semi-parametric methods and applications

CERN Document Server

Rossi, Peter

2014-01-01

This book reviews and develops Bayesian non-parametric and semi-parametric methods for applications in microeconometrics and quantitative marketing. Most econometric models used in microeconomics and marketing applications involve arbitrary distributional assumptions. As more data becomes available, a natural desire to provide methods that relax these assumptions arises. Peter Rossi advocates a Bayesian approach in which specific distributional assumptions are replaced with more flexible distributions based on mixtures of normals. The Bayesian approach can use either a large but fixed number

Bandwidth-Tunable Fiber Bragg Gratings Based on UV Glue Technique

Science.gov (United States)

Fu, Ming-Yue; Liu, Wen-Feng; Chen, Hsin-Tsang; Chuang, Chia-Wei; Bor, Sheau-Shong; Tien, Chuen-Lin

2007-07-01

In this study, we have demonstrated that a uniform fiber Bragg grating (FBG) can be transformed into a chirped fiber grating by a simple UV glue adhesive technique without shifting the reflection band with respect to the center wavelength of the FBG. The technique is based on the induced strain of an FBG due to the UV glue adhesive force on the fiber surface that causes a grating period variation and an effective index change. This technique can provide a fast and simple method of obtaining the required chirp value of a grating for applications in the dispersion compensators, gain flattening in erbium-doped fiber amplifiers (EDFAs) or optical filters.

[Propolis. The bee glue as presented by the Graeco-Roman literature].

Science.gov (United States)

Golder, Werner

2004-01-01

The bee glue, commonly known as propolis, has been employed for medical purposes already in teh ancient world. More than 15 Greek and Roman authors report on the preparation and application of the so-called third natural product of the bees (besides honey and wax). Aristoteles described the fundamental issues of its biology in his 'Historia Animalium' correctly. The bulk of propolis is obtained from the barks of poplars. Once carried in the hives, the glue is used to stabilize the cells and honeycombs and to protect the bees against invaders and cold weather. Propolis has been chiefly employed for the preparation of ointment and plasters. For this purpose, the viscous raw material was purified, moulded and boiled. In most preparations, the bee glue was only one of many (up to 20) pharmacologically active constituents and came to five to 20% of the mixture. Only rarely, a single drug therapy was using propolis was carried out. The application of the glue was most successful in general surgery and casualties. In that respect, the ancient physicians took advantage of the anti-edematous and anti-infectious properties of the substance. Thus, it was used to treat bumps, indurations, and slow-healing wounds. Moreover, cataplasms against swollen cervical nodes and indurations of the female breast often contained propolis. Finally, bee glue proved successful for the treatment of chronic backache and pain in the hip as well as fresh injuries of muscles and tendons. In the sector of skin diseases, lichens and condylomata were found to respond well to propolis. ALl this indications have been a matter of several records. However, the successful use of propolis in diseases of the stomach and liver has ben reported solely by Alexander of Tralles (6th century AD). Not counting the internal diseases, the spectrum of indications for propolis has not substantially changed as compared to the classical antiquity. Interestingly, radiation therapists have adopted the ancient remedy and

Experimental closure of gunshot wounds by fibrin glue with antibiotics in pigs

OpenAIRE

Đenić Nebojša; Višnjić Milan; Dragović Saša; Bojanić Vladmila; Bojanić Zoran; Đurđević Dragan; Đinđić Boris; Kostov Miloš

2015-01-01

Background/Aim. Gunshot wounds caused by the automatic rifle M70AB2 (AK-47) 7.62 mm, after the primary surgical management, were closed with delayed primary suture during the next four to seven days. This period coincides with the fibroblastic phase of wound healing. Fibrin glue is used as a local hemostatic and as a matrix for the local dosed release of antibiotics. Antibiotics addition to fibrin glue resulted in continuous diffusion into the surrounding n...

Alternative glues for the production of ATLAS silicon strip modules for the Phase-II upgrade of the ATLAS inner detector

International Nuclear Information System (INIS)

Poley, Luise; Bloch, Ingo; Edwards, Sam

2016-04-01

The Phase-II upgrade of the ATLAS detector for the High Luminosity Large Hadron Collider (HL-LHC) includes the replacement of the current Inner Detector with an all-silicon tracker consisting of pixel and strip detectors. The current Phase-II detector layout requires the construction of 20,000 strip detector modules consisting of sensor, circuit boards and readout chips, which are connected mechanically using adhesives. The adhesive between readout chips and circuit board is a silver epoxy glue as was used in the current ATLAS SemiConductor Tracker (SCT). This glue has several disadvantages, which motivated the search for an alternative. This paper presents a study concerning the use of six ultra-violet (UV) cure glues and a glue pad for use in the assembly of silicon strip detector modules for the ATLAS upgrade. Trials were carried out to determine the ease of use, the thermal conduction and shear strength, thermal cycling, radiation hardness, corrosion resistance and shear strength tests. These investigations led to the exclusion of three UV cure glues as well as the glue pad. Three UV cure glues were found to be possible better alternatives. Results from electrical tests of first prototype modules constructed using these glues are presented.

Alternative glues for the production of ATLAS silicon strip modules for the Phase-II upgrade of the ATLAS inner detector

Energy Technology Data Exchange (ETDEWEB)

Poley, Luise [DESY, Zeuthen (Germany); Humboldt Univ. Berlin (Germany); Bloch, Ingo [DESY, Zeuthen (Germany); Edwards, Sam [Birmingham Univ. (United Kingdom); and others

2016-04-15

The Phase-II upgrade of the ATLAS detector for the High Luminosity Large Hadron Collider (HL-LHC) includes the replacement of the current Inner Detector with an all-silicon tracker consisting of pixel and strip detectors. The current Phase-II detector layout requires the construction of 20,000 strip detector modules consisting of sensor, circuit boards and readout chips, which are connected mechanically using adhesives. The adhesive between readout chips and circuit board is a silver epoxy glue as was used in the current ATLAS SemiConductor Tracker (SCT). This glue has several disadvantages, which motivated the search for an alternative. This paper presents a study concerning the use of six ultra-violet (UV) cure glues and a glue pad for use in the assembly of silicon strip detector modules for the ATLAS upgrade. Trials were carried out to determine the ease of use, the thermal conduction and shear strength, thermal cycling, radiation hardness, corrosion resistance and shear strength tests. These investigations led to the exclusion of three UV cure glues as well as the glue pad. Three UV cure glues were found to be possible better alternatives. Results from electrical tests of first prototype modules constructed using these glues are presented.

Evolution of Subjective Hurricane Risk Perceptions: A Bayesian Approach

OpenAIRE

David Kelly; David Letson; Forest Nelson; David S. Nolan; Daniel Solis

2009-01-01

This paper studies how individuals update subjective risk perceptions in response to hurricane track forecast information, using a unique data set from an event market, the Hurricane Futures Market (HFM). We derive a theoretical Bayesian framework which predicts how traders update their perceptions of the probability of a hurricane making landfall in a certain range of coastline. Our results suggest that traders behave in a way consistent with Bayesian updating but this behavior is based on t...

Inverse Problems in a Bayesian Setting

KAUST Repository

Matthies, Hermann G.

2016-02-13

In a Bayesian setting, inverse problems and uncertainty quantification (UQ)—the propagation of uncertainty through a computational (forward) model—are strongly connected. In the form of conditional expectation the Bayesian update becomes computationally attractive. We give a detailed account of this approach via conditional approximation, various approximations, and the construction of filters. Together with a functional or spectral approach for the forward UQ there is no need for time-consuming and slowly convergent Monte Carlo sampling. The developed sampling-free non-linear Bayesian update in form of a filter is derived from the variational problem associated with conditional expectation. This formulation in general calls for further discretisation to make the computation possible, and we choose a polynomial approximation. After giving details on the actual computation in the framework of functional or spectral approximations, we demonstrate the workings of the algorithm on a number of examples of increasing complexity. At last, we compare the linear and nonlinear Bayesian update in form of a filter on some examples.

Inverse Problems in a Bayesian Setting

KAUST Repository

Matthies, Hermann G.; Zander, Elmar; Rosić, Bojana V.; Litvinenko, Alexander; Pajonk, Oliver

2016-01-01

In a Bayesian setting, inverse problems and uncertainty quantification (UQ)—the propagation of uncertainty through a computational (forward) model—are strongly connected. In the form of conditional expectation the Bayesian update becomes computationally attractive. We give a detailed account of this approach via conditional approximation, various approximations, and the construction of filters. Together with a functional or spectral approach for the forward UQ there is no need for time-consuming and slowly convergent Monte Carlo sampling. The developed sampling-free non-linear Bayesian update in form of a filter is derived from the variational problem associated with conditional expectation. This formulation in general calls for further discretisation to make the computation possible, and we choose a polynomial approximation. After giving details on the actual computation in the framework of functional or spectral approximations, we demonstrate the workings of the algorithm on a number of examples of increasing complexity. At last, we compare the linear and nonlinear Bayesian update in form of a filter on some examples.

CRAFT (complete reduction to amplitude frequency table)--robust and time-efficient Bayesian approach for quantitative mixture analysis by NMR.

Science.gov (United States)

Krishnamurthy, Krish

2013-12-01

The intrinsic quantitative nature of NMR is increasingly exploited in areas ranging from complex mixture analysis (as in metabolomics and reaction monitoring) to quality assurance/control. Complex NMR spectra are more common than not, and therefore, extraction of quantitative information generally involves significant prior knowledge and/or operator interaction to characterize resonances of interest. Moreover, in most NMR-based metabolomic experiments, the signals from metabolites are normally present as a mixture of overlapping resonances, making quantification difficult. Time-domain Bayesian approaches have been reported to be better than conventional frequency-domain analysis at identifying subtle changes in signal amplitude. We discuss an approach that exploits Bayesian analysis to achieve a complete reduction to amplitude frequency table (CRAFT) in an automated and time-efficient fashion - thus converting the time-domain FID to a frequency-amplitude table. CRAFT uses a two-step approach to FID analysis. First, the FID is digitally filtered and downsampled to several sub FIDs, and secondly, these sub FIDs are then modeled as sums of decaying sinusoids using the Bayesian approach. CRAFT tables can be used for further data mining of quantitative information using fingerprint chemical shifts of compounds of interest and/or statistical analysis of modulation of chemical quantity in a biological study (metabolomics) or process study (reaction monitoring) or quality assurance/control. The basic principles behind this approach as well as results to evaluate the effectiveness of this approach in mixture analysis are presented. Copyright © 2013 John Wiley & Sons, Ltd.

An efficient Bayesian meta-analysis approach for studying cross-phenotype genetic associations.

Directory of Open Access Journals (Sweden)

Arunabha Majumdar

2018-02-01

Full Text Available Simultaneous analysis of genetic associations with multiple phenotypes may reveal shared genetic susceptibility across traits (pleiotropy. For a locus exhibiting overall pleiotropy, it is important to identify which specific traits underlie this association. We propose a Bayesian meta-analysis approach (termed CPBayes that uses summary-level data across multiple phenotypes to simultaneously measure the evidence of aggregate-level pleiotropic association and estimate an optimal subset of traits associated with the risk locus. This method uses a unified Bayesian statistical framework based on a spike and slab prior. CPBayes performs a fully Bayesian analysis by employing the Markov Chain Monte Carlo (MCMC technique Gibbs sampling. It takes into account heterogeneity in the size and direction of the genetic effects across traits. It can be applied to both cohort data and separate studies of multiple traits having overlapping or non-overlapping subjects. Simulations show that CPBayes can produce higher accuracy in the selection of associated traits underlying a pleiotropic signal than the subset-based meta-analysis ASSET. We used CPBayes to undertake a genome-wide pleiotropic association study of 22 traits in the large Kaiser GERA cohort and detected six independent pleiotropic loci associated with at least two phenotypes. This includes a locus at chromosomal region 1q24.2 which exhibits an association simultaneously with the risk of five different diseases: Dermatophytosis, Hemorrhoids, Iron Deficiency, Osteoporosis and Peripheral Vascular Disease. We provide an R-package 'CPBayes' implementing the proposed method.

Reliability assessment using degradation models: bayesian and classical approaches

Directory of Open Access Journals (Sweden)

Marta Afonso Freitas

2010-04-01

Full Text Available Traditionally, reliability assessment of devices has been based on (accelerated life tests. However, for highly reliable products, little information about reliability is provided by life tests in which few or no failures are typically observed. Since most failures arise from a degradation mechanism at work for which there are characteristics that degrade over time, one alternative is monitor the device for a period of time and assess its reliability from the changes in performance (degradation observed during that period. The goal of this article is to illustrate how degradation data can be modeled and analyzed by using "classical" and Bayesian approaches. Four methods of data analysis based on classical inference are presented. Next we show how Bayesian methods can also be used to provide a natural approach to analyzing degradation data. The approaches are applied to a real data set regarding train wheels degradation.Tradicionalmente, o acesso à confiabilidade de dispositivos tem sido baseado em testes de vida (acelerados. Entretanto, para produtos altamente confiáveis, pouca informação a respeito de sua confiabilidade é fornecida por testes de vida no quais poucas ou nenhumas falhas são observadas. Uma vez que boa parte das falhas é induzida por mecanismos de degradação, uma alternativa é monitorar o dispositivo por um período de tempo e acessar sua confiabilidade através das mudanças em desempenho (degradação observadas durante aquele período. O objetivo deste artigo é ilustrar como dados de degradação podem ser modelados e analisados utilizando-se abordagens "clássicas" e Bayesiana. Quatro métodos de análise de dados baseados em inferência clássica são apresentados. A seguir, mostramos como os métodos Bayesianos podem também ser aplicados para proporcionar uma abordagem natural à análise de dados de degradação. As abordagens são aplicadas a um banco de dados real relacionado à degradação de rodas de trens.

Bayesian approach to errors-in-variables in regression models

Science.gov (United States)

Rozliman, Nur Aainaa; Ibrahim, Adriana Irawati Nur; Yunus, Rossita Mohammad

2017-05-01

In many applications and experiments, data sets are often contaminated with error or mismeasured covariates. When at least one of the covariates in a model is measured with error, Errors-in-Variables (EIV) model can be used. Measurement error, when not corrected, would cause misleading statistical inferences and analysis. Therefore, our goal is to examine the relationship of the outcome variable and the unobserved exposure variable given the observed mismeasured surrogate by applying the Bayesian formulation to the EIV model. We shall extend the flexible parametric method proposed by Hossain and Gustafson (2009) to another nonlinear regression model which is the Poisson regression model. We shall then illustrate the application of this approach via a simulation study using Markov chain Monte Carlo sampling methods.

Bayesian Reliability Estimation for Deteriorating Systems with Limited Samples Using the Maximum Entropy Approach

Directory of Open Access Journals (Sweden)

Ning-Cong Xiao

2013-12-01

Full Text Available In this paper the combinations of maximum entropy method and Bayesian inference for reliability assessment of deteriorating system is proposed. Due to various uncertainties, less data and incomplete information, system parameters usually cannot be determined precisely. These uncertainty parameters can be modeled by fuzzy sets theory and the Bayesian inference which have been proved to be useful for deteriorating systems under small sample sizes. The maximum entropy approach can be used to calculate the maximum entropy density function of uncertainty parameters more accurately for it does not need any additional information and assumptions. Finally, two optimization models are presented which can be used to determine the lower and upper bounds of systems probability of failure under vague environment conditions. Two numerical examples are investigated to demonstrate the proposed method.

Fibrinogen and thrombin concentrations are critical for fibrin glue adherence in rat high-risk colon anastomoses

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Eliseo Portilla-de Buen

2014-04-01

Full Text Available OBJECTIVE: Fibrin glues have not been consistently successful in preventing the dehiscence of high-risk colonic anastomoses. Fibrinogen and thrombin concentrations in glues determine their ability to function as sealants, healers, and/or adhesives. The objective of the current study was to compare the effects of different concentrations of fibrinogen and thrombin on bursting pressure, leaks, dehiscence, and morphology of high-risk ischemic colonic anastomoses using fibrin glue in rats. METHODS: Colonic anastomoses in adult female Sprague-Dawley rats (weight, 250-350 g treated with fibrin glue containing different concentrations of fibrinogen and thrombin were evaluated at post-operative day 5. The interventions were low-risk (normal or high-risk (ischemic end-to-end colonic anastomoses using polypropylene sutures and topical application of fibrinogen at high (120 mg/mL or low (40 mg/mL concentrations and thrombin at high (1000 IU/mL or low (500 IU/mL concentrations. RESULTS: Ischemia alone, anastomosis alone, or both together reduced the bursting pressure. Glues containing a low fibrinogen concentration improved this parameter in all cases. High thrombin in combination with low fibrinogen also improved adherence exclusively in low-risk anastomoses. No differences were detected with respect to macroscopic parameters, histopathology, or hydroxyproline content at 5 days post-anastomosis. CONCLUSIONS: Fibrin glue with a low fibrinogen content normalizes the bursting pressure of high-risk ischemic left-colon anastomoses in rats at day 5 after surgery.

Incorporating historical information in biosimilar trials: Challenges and a hybrid Bayesian-frequentist approach.

Science.gov (United States)

Mielke, Johanna; Schmidli, Heinz; Jones, Byron

2018-05-01

For the approval of biosimilars, it is, in most cases, necessary to conduct large Phase III clinical trials in patients to convince the regulatory authorities that the product is comparable in terms of efficacy and safety to the originator product. As the originator product has already been studied in several trials beforehand, it seems natural to include this historical information into the showing of equivalent efficacy. Since all studies for the regulatory approval of biosimilars are confirmatory studies, it is required that the statistical approach has reasonable frequentist properties, most importantly, that the Type I error rate is controlled-at least in all scenarios that are realistic in practice. However, it is well known that the incorporation of historical information can lead to an inflation of the Type I error rate in the case of a conflict between the distribution of the historical data and the distribution of the trial data. We illustrate this issue and confirm, using the Bayesian robustified meta-analytic-predictive (MAP) approach as an example, that simultaneously controlling the Type I error rate over the complete parameter space and gaining power in comparison to a standard frequentist approach that only considers the data in the new study, is not possible. We propose a hybrid Bayesian-frequentist approach for binary endpoints that controls the Type I error rate in the neighborhood of the center of the prior distribution, while improving the power. We study the properties of this approach in an extensive simulation study and provide a real-world example. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Use of tissue glue for punch grafting in vitiligo - A preliminary report

Directory of Open Access Journals (Sweden)

Ghorpade Ashok

2004-05-01

Full Text Available BACKGROUND: Minipunch grafting has been successfully used for the treatment of stable vitiligo since several years. Post-operative immobilization at certain sites such as lips, areola & infralabial folds and joints is not easy to achieve. Putting stay sutures on the lips and areolae is difficult. Grafting over the joints may require hospitalization to ensure proper immobilization, and may discourage some patients. AIM: To study the efficacy of a tissue glue for immobilization of donor grafts at the above sites during minipunch grafting. METHODS: Ten cases with stable vitiligo over the lips, areolae, below the lower lip, and over different joints had tissue glue applied to the edges between the donor grafts and the recipient wells, after the grafting. RESULTS: The grafts adhered firmly within a minute and there was no need for a cumbersome dressing. Over the lips, the patients could resume talking and drinking fluids immediately. CONCLUSION: The glue was very helpful for immobilizing the grafts at these difficult sites.

Discriminative Bayesian Dictionary Learning for Classification.

Science.gov (United States)

Akhtar, Naveed; Shafait, Faisal; Mian, Ajmal

2016-12-01

We propose a Bayesian approach to learn discriminative dictionaries for sparse representation of data. The proposed approach infers probability distributions over the atoms of a discriminative dictionary using a finite approximation of Beta Process. It also computes sets of Bernoulli distributions that associate class labels to the learned dictionary atoms. This association signifies the selection probabilities of the dictionary atoms in the expansion of class-specific data. Furthermore, the non-parametric character of the proposed approach allows it to infer the correct size of the dictionary. We exploit the aforementioned Bernoulli distributions in separately learning a linear classifier. The classifier uses the same hierarchical Bayesian model as the dictionary, which we present along the analytical inference solution for Gibbs sampling. For classification, a test instance is first sparsely encoded over the learned dictionary and the codes are fed to the classifier. We performed experiments for face and action recognition; and object and scene-category classification using five public datasets and compared the results with state-of-the-art discriminative sparse representation approaches. Experiments show that the proposed Bayesian approach consistently outperforms the existing approaches.

Refining mass formulas for astrophysical applications: A Bayesian neural network approach

Science.gov (United States)

Utama, R.; Piekarewicz, J.

2017-10-01

Background: Exotic nuclei, particularly those near the drip lines, are at the core of one of the fundamental questions driving nuclear structure and astrophysics today: What are the limits of nuclear binding? Exotic nuclei play a critical role in both informing theoretical models as well as in our understanding of the origin of the heavy elements. Purpose: Our aim is to refine existing mass models through the training of an artificial neural network that will mitigate the large model discrepancies far away from stability. Methods: The basic paradigm of our two-pronged approach is an existing mass model that captures as much as possible of the underlying physics followed by the implementation of a Bayesian neural network (BNN) refinement to account for the missing physics. Bayesian inference is employed to determine the parameters of the neural network so that model predictions may be accompanied by theoretical uncertainties. Results: Despite the undeniable quality of the mass models adopted in this work, we observe a significant improvement (of about 40%) after the BNN refinement is implemented. Indeed, in the specific case of the Duflo-Zuker mass formula, we find that the rms deviation relative to experiment is reduced from σrms=0.503 MeV to σrms=0.286 MeV. These newly refined mass tables are used to map the neutron drip lines (or rather "drip bands") and to study a few critical r -process nuclei. Conclusions: The BNN approach is highly successful in refining the predictions of existing mass models. In particular, the large discrepancy displayed by the original "bare" models in regions where experimental data are unavailable is considerably quenched after the BNN refinement. This lends credence to our approach and has motivated us to publish refined mass tables that we trust will be helpful for future astrophysical applications.

Empirical Bayesian inference and model uncertainty

International Nuclear Information System (INIS)

Poern, K.

1994-01-01

This paper presents a hierarchical or multistage empirical Bayesian approach for the estimation of uncertainty concerning the intensity of a homogeneous Poisson process. A class of contaminated gamma distributions is considered to describe the uncertainty concerning the intensity. These distributions in turn are defined through a set of secondary parameters, the knowledge of which is also described and updated via Bayes formula. This two-stage Bayesian approach is an example where the modeling uncertainty is treated in a comprehensive way. Each contaminated gamma distributions, represented by a point in the 3D space of secondary parameters, can be considered as a specific model of the uncertainty about the Poisson intensity. Then, by the empirical Bayesian method each individual model is assigned a posterior probability

An approach based on Hierarchical Bayesian Graphical Models for measurement interpretation under uncertainty

Science.gov (United States)

Skataric, Maja; Bose, Sandip; Zeroug, Smaine; Tilke, Peter

2017-02-01

It is not uncommon in the field of non-destructive evaluation that multiple measurements encompassing a variety of modalities are available for analysis and interpretation for determining the underlying states of nature of the materials or parts being tested. Despite and sometimes due to the richness of data, significant challenges arise in the interpretation manifested as ambiguities and inconsistencies due to various uncertain factors in the physical properties (inputs), environment, measurement device properties, human errors, and the measurement data (outputs). Most of these uncertainties cannot be described by any rigorous mathematical means, and modeling of all possibilities is usually infeasible for many real time applications. In this work, we will discuss an approach based on Hierarchical Bayesian Graphical Models (HBGM) for the improved interpretation of complex (multi-dimensional) problems with parametric uncertainties that lack usable physical models. In this setting, the input space of the physical properties is specified through prior distributions based on domain knowledge and expertise, which are represented as Gaussian mixtures to model the various possible scenarios of interest for non-destructive testing applications. Forward models are then used offline to generate the expected distribution of the proposed measurements which are used to train a hierarchical Bayesian network. In Bayesian analysis, all model parameters are treated as random variables, and inference of the parameters is made on the basis of posterior distribution given the observed data. Learned parameters of the posterior distribution obtained after the training can therefore be used to build an efficient classifier for differentiating new observed data in real time on the basis of pre-trained models. We will illustrate the implementation of the HBGM approach to ultrasonic measurements used for cement evaluation of cased wells in the oil industry.

Joint analysis of input and parametric uncertainties in watershed water quality modeling: A formal Bayesian approach

Science.gov (United States)

Han, Feng; Zheng, Yi

2018-06-01

Significant Input uncertainty is a major source of error in watershed water quality (WWQ) modeling. It remains challenging to address the input uncertainty in a rigorous Bayesian framework. This study develops the Bayesian Analysis of Input and Parametric Uncertainties (BAIPU), an approach for the joint analysis of input and parametric uncertainties through a tight coupling of Markov Chain Monte Carlo (MCMC) analysis and Bayesian Model Averaging (BMA). The formal likelihood function for this approach is derived considering a lag-1 autocorrelated, heteroscedastic, and Skew Exponential Power (SEP) distributed error model. A series of numerical experiments were performed based on a synthetic nitrate pollution case and on a real study case in the Newport Bay Watershed, California. The Soil and Water Assessment Tool (SWAT) and Differential Evolution Adaptive Metropolis (DREAM(ZS)) were used as the representative WWQ model and MCMC algorithm, respectively. The major findings include the following: (1) the BAIPU can be implemented and used to appropriately identify the uncertain parameters and characterize the predictive uncertainty; (2) the compensation effect between the input and parametric uncertainties can seriously mislead the modeling based management decisions, if the input uncertainty is not explicitly accounted for; (3) the BAIPU accounts for the interaction between the input and parametric uncertainties and therefore provides more accurate calibration and uncertainty results than a sequential analysis of the uncertainties; and (4) the BAIPU quantifies the credibility of different input assumptions on a statistical basis and can be implemented as an effective inverse modeling approach to the joint inference of parameters and inputs.

Integrated survival analysis using an event-time approach in a Bayesian framework.

Science.gov (United States)

Walsh, Daniel P; Dreitz, Victoria J; Heisey, Dennis M

2015-02-01

Event-time or continuous-time statistical approaches have been applied throughout the biostatistical literature and have led to numerous scientific advances. However, these techniques have traditionally relied on knowing failure times. This has limited application of these analyses, particularly, within the ecological field where fates of marked animals may be unknown. To address these limitations, we developed an integrated approach within a Bayesian framework to estimate hazard rates in the face of unknown fates. We combine failure/survival times from individuals whose fates are known and times of which are interval-censored with information from those whose fates are unknown, and model the process of detecting animals with unknown fates. This provides the foundation for our integrated model and permits necessary parameter estimation. We provide the Bayesian model, its derivation, and use simulation techniques to investigate the properties and performance of our approach under several scenarios. Lastly, we apply our estimation technique using a piece-wise constant hazard function to investigate the effects of year, age, chick size and sex, sex of the tending adult, and nesting habitat on mortality hazard rates of the endangered mountain plover (Charadrius montanus) chicks. Traditional models were inappropriate for this analysis because fates of some individual chicks were unknown due to failed radio transmitters. Simulations revealed biases of posterior mean estimates were minimal (≤ 4.95%), and posterior distributions behaved as expected with RMSE of the estimates decreasing as sample sizes, detection probability, and survival increased. We determined mortality hazard rates for plover chicks were highest at birth weights and/or whose nest was within agricultural habitats. Based on its performance, our approach greatly expands the range of problems for which event-time analyses can be used by eliminating the need for having completely known fate data.

Integrated survival analysis using an event-time approach in a Bayesian framework

Science.gov (United States)

Walsh, Daniel P.; Dreitz, VJ; Heisey, Dennis M.

2015-01-01

Event-time or continuous-time statistical approaches have been applied throughout the biostatistical literature and have led to numerous scientific advances. However, these techniques have traditionally relied on knowing failure times. This has limited application of these analyses, particularly, within the ecological field where fates of marked animals may be unknown. To address these limitations, we developed an integrated approach within a Bayesian framework to estimate hazard rates in the face of unknown fates. We combine failure/survival times from individuals whose fates are known and times of which are interval-censored with information from those whose fates are unknown, and model the process of detecting animals with unknown fates. This provides the foundation for our integrated model and permits necessary parameter estimation. We provide the Bayesian model, its derivation, and use simulation techniques to investigate the properties and performance of our approach under several scenarios. Lastly, we apply our estimation technique using a piece-wise constant hazard function to investigate the effects of year, age, chick size and sex, sex of the tending adult, and nesting habitat on mortality hazard rates of the endangered mountain plover (Charadrius montanus) chicks. Traditional models were inappropriate for this analysis because fates of some individual chicks were unknown due to failed radio transmitters. Simulations revealed biases of posterior mean estimates were minimal (≤ 4.95%), and posterior distributions behaved as expected with RMSE of the estimates decreasing as sample sizes, detection probability, and survival increased. We determined mortality hazard rates for plover chicks were highest at birth weights and/or whose nest was within agricultural habitats. Based on its performance, our approach greatly expands the range of problems for which event-time analyses can be used by eliminating the need for having completely known fate data.

MACROECONOMIC FORECASTING USING BAYESIAN VECTOR AUTOREGRESSIVE APPROACH

Directory of Open Access Journals (Sweden)

D. Tutberidze

2017-04-01

Full Text Available There are many arguments that can be advanced to support the forecasting activities of business entities. The underlying argument in favor of forecasting is that managerial decisions are significantly dependent on proper evaluation of future trends as market conditions are constantly changing and require a detailed analysis of future dynamics. The article discusses the importance of using reasonable macro-econometric tool by suggesting the idea of conditional forecasting through a Vector Autoregressive (VAR modeling framework. Under this framework, a macroeconomic model for Georgian economy is constructed with the few variables believed to be shaping business environment. Based on the model, forecasts of macroeconomic variables are produced, and three types of scenarios are analyzed - a baseline and two alternative ones. The results of the study provide confirmatory evidence that suggested methodology is adequately addressing the research phenomenon and can be used widely by business entities in responding their strategic and operational planning challenges. Given this set-up, it is shown empirically that Bayesian Vector Autoregressive approach provides reasonable forecasts for the variables of interest.

Bayesian-based localization in inhomogeneous transmission media

DEFF Research Database (Denmark)

Nadimi, E. S.; Blanes-Vidal, V.; Johansen, P. M.

2013-01-01

In this paper, we propose a novel robust probabilistic approach based on the Bayesian inference using received-signal-strength (RSS) measurements with varying path-loss exponent. We derived the probability density function (pdf) of the distance between any two sensors in the network with heteroge......In this paper, we propose a novel robust probabilistic approach based on the Bayesian inference using received-signal-strength (RSS) measurements with varying path-loss exponent. We derived the probability density function (pdf) of the distance between any two sensors in the network...... with heterogeneous transmission medium as a function of the given RSS measurements and the characteristics of the heterogeneous medium. The results of this study show that the localization mean square error (MSE) of the Bayesian-based method outperformed all other existing localization approaches. © 2013 ACM....

Photoproduction of the Cascade Baryons at GlueX

Science.gov (United States)

Ernst, Ashley; GlueX Collaboration

2017-09-01

Multi-strange baryons play an important role in understanding the strong interaction and despite their importance, little is known about such hyperons. Almost all knowledge of the Cascades today stems from Kaon-nucleon interactions in bubble chamber experiments performed in the 1960s and 1970s, of which only the octet and decuplet ground states, Ξ (1320) and Ξ (1530) respectively, are well established. This research uses the GlueX experiment at Jefferson Laboratory to map out the spectrum of doubly-strange Cascade resonances, as well as to measure the spin-parity for each of the detected resonances. The first physics run for GlueX has recently been completed and a clear signature of the Ξ (1320) is observed. The systematics of the Cascade spectrum will be presented motivated by prior discoveries in the N* program. This work was supported by the U.S. Department of Energy Grant DE-FG02-92ER40735 and National Science Foundation Grant 1449440.

A Dynamic BI–Orthogonal Field Equation Approach to Efficient Bayesian Inversion

Directory of Open Access Journals (Sweden)

Tagade Piyush M.

2017-06-01

Full Text Available This paper proposes a novel computationally efficient stochastic spectral projection based approach to Bayesian inversion of a computer simulator with high dimensional parametric and model structure uncertainty. The proposed method is based on the decomposition of the solution into its mean and a random field using a generic Karhunen-Loève expansion. The random field is represented as a convolution of separable Hilbert spaces in stochastic and spatial dimensions that are spectrally represented using respective orthogonal bases. In particular, the present paper investigates generalized polynomial chaos bases for the stochastic dimension and eigenfunction bases for the spatial dimension. Dynamic orthogonality is used to derive closed-form equations for the time evolution of mean, spatial and the stochastic fields. The resultant system of equations consists of a partial differential equation (PDE that defines the dynamic evolution of the mean, a set of PDEs to define the time evolution of eigenfunction bases, while a set of ordinary differential equations (ODEs define dynamics of the stochastic field. This system of dynamic evolution equations efficiently propagates the prior parametric uncertainty to the system response. The resulting bi-orthogonal expansion of the system response is used to reformulate the Bayesian inference for efficient exploration of the posterior distribution. The efficacy of the proposed method is investigated for calibration of a 2D transient diffusion simulator with an uncertain source location and diffusivity. The computational efficiency of the method is demonstrated against a Monte Carlo method and a generalized polynomial chaos approach.

Inference of reactive transport model parameters using a Bayesian multivariate approach

Science.gov (United States)

Carniato, Luca; Schoups, Gerrit; van de Giesen, Nick

2014-08-01

Parameter estimation of subsurface transport models from multispecies data requires the definition of an objective function that includes different types of measurements. Common approaches are weighted least squares (WLS), where weights are specified a priori for each measurement, and weighted least squares with weight estimation (WLS(we)) where weights are estimated from the data together with the parameters. In this study, we formulate the parameter estimation task as a multivariate Bayesian inference problem. The WLS and WLS(we) methods are special cases in this framework, corresponding to specific prior assumptions about the residual covariance matrix. The Bayesian perspective allows for generalizations to cases where residual correlation is important and for efficient inference by analytically integrating out the variances (weights) and selected covariances from the joint posterior. Specifically, the WLS and WLS(we) methods are compared to a multivariate (MV) approach that accounts for specific residual correlations without the need for explicit estimation of the error parameters. When applied to inference of reactive transport model parameters from column-scale data on dissolved species concentrations, the following results were obtained: (1) accounting for residual correlation between species provides more accurate parameter estimation for high residual correlation levels whereas its influence for predictive uncertainty is negligible, (2) integrating out the (co)variances leads to an efficient estimation of the full joint posterior with a reduced computational effort compared to the WLS(we) method, and (3) in the presence of model structural errors, none of the methods is able to identify the correct parameter values.

Prior approval: the growth of Bayesian methods in psychology.

Science.gov (United States)

Andrews, Mark; Baguley, Thom

2013-02-01

Within the last few years, Bayesian methods of data analysis in psychology have proliferated. In this paper, we briefly review the history or the Bayesian approach to statistics, and consider the implications that Bayesian methods have for the theory and practice of data analysis in psychology.

A Bayesian approach to degradation-based burn-in optimization for display products exhibiting two-phase degradation patterns

International Nuclear Information System (INIS)

Yuan, Tao; Bae, Suk Joo; Zhu, Xiaoyan

2016-01-01

Motivated by the two-phase degradation phenomena observed in light displays (e.g., plasma display panels (PDPs), organic light emitting diodes (OLEDs)), this study proposes a new degradation-based burn-in testing plan for display products exhibiting two-phase degradation patterns. The primary focus of the burn-in test in this study is to eliminate the initial rapid degradation phase, while the major purpose of traditional burn-in tests is to detect and eliminate early failures from weak units. A hierarchical Bayesian bi-exponential model is used to capture two-phase degradation patterns of the burn-in population. Mission reliability and total cost are introduced as planning criteria. The proposed burn-in approach accounts for unit-to-unit variability within the burn-in population, and uncertainty concerning the model parameters, mainly in the hierarchical Bayesian framework. Available pre-burn-in data is conveniently incorporated into the burn-in decision-making procedure. A practical example of PDP degradation data is used to illustrate the proposed methodology. The proposed method is compared to other approaches such as the maximum likelihood method or the change-point regression. - Highlights: • We propose a degradation-based burn-in test for products with two-phase degradation. • Mission reliability and total cost are used as planning criteria. • The proposed burn-in approach is built within the hierarchical Bayesian framework. • A practical example was used to illustrate the proposed methodology.

Bayesian estimation of the discrete coefficient of determination.

Science.gov (United States)

Chen, Ting; Braga-Neto, Ulisses M

2016-12-01

The discrete coefficient of determination (CoD) measures the nonlinear interaction between discrete predictor and target variables and has had far-reaching applications in Genomic Signal Processing. Previous work has addressed the inference of the discrete CoD using classical parametric and nonparametric approaches. In this paper, we introduce a Bayesian framework for the inference of the discrete CoD. We derive analytically the optimal minimum mean-square error (MMSE) CoD estimator, as well as a CoD estimator based on the Optimal Bayesian Predictor (OBP). For the latter estimator, exact expressions for its bias, variance, and root-mean-square (RMS) are given. The accuracy of both Bayesian CoD estimators with non-informative and informative priors, under fixed or random parameters, is studied via analytical and numerical approaches. We also demonstrate the application of the proposed Bayesian approach in the inference of gene regulatory networks, using gene-expression data from a previously published study on metastatic melanoma.

Cross-linking by protein oxidation in the rapidly setting gel-based glues of slugs

Science.gov (United States)

Bradshaw, Andrew; Salt, Michael; Bell, Ashley; Zeitler, Matt; Litra, Noelle; Smith, Andrew M.

2011-01-01

SUMMARY The terrestrial slug Arion subfuscus secretes a glue that is a dilute gel with remarkable adhesive and cohesive strength. The function of this glue depends on metals, raising the possibility that metal-catalyzed oxidation plays a role. The extent and time course of protein oxidation was measured by immunoblotting to detect the resulting carbonyl groups. Several proteins, particularly one with a relative molecular mass (Mr) of 165×103, were heavily oxidized. Of the proteins known to distinguish the glue from non-adhesive mucus, only specific size variants were oxidized. The oxidation appears to occur within the first few seconds of secretion. Although carbonyls were detected by 2,4-dinitrophenylhydrazine (DNPH) in denatured proteins, they were not easily detected in the native state. The presence of reversible cross-links derived from carbonyls was tested for by treatment with sodium borohydride, which would reduce uncross-linked carbonyls to alcohols, but stabilize imine bonds formed by carbonyls and thus lead to less soluble complexes. Consistent with imine bond formation, sodium borohydride led to a 20–35% decrease in the amount of soluble protein with a Mr of 40–165 (×103) without changing the carbonyl content per protein. In contrast, the nucleophile hydroxylamine, which would competitively disrupt imine bonds, increased protein solubility in the glue. Finally, the primary amine groups on a protein with a Mr of 15×103 were not accessible to acid anhydrides. The results suggest that cross-links between aldehydes and primary amines contribute to the cohesive strength of the glue. PMID:21525316

A hierarchical bayesian approach to ecological count data: a flexible tool for ecologists.

Directory of Open Access Journals (Sweden)

James A Fordyce

Full Text Available Many ecological studies use the analysis of count data to arrive at biologically meaningful inferences. Here, we introduce a hierarchical bayesian approach to count data. This approach has the advantage over traditional approaches in that it directly estimates the parameters of interest at both the individual-level and population-level, appropriately models uncertainty, and allows for comparisons among models, including those that exceed the complexity of many traditional approaches, such as ANOVA or non-parametric analogs. As an example, we apply this method to oviposition preference data for butterflies in the genus Lycaeides. Using this method, we estimate the parameters that describe preference for each population, compare the preference hierarchies among populations, and explore various models that group populations that share the same preference hierarchy.

A bayesian approach for learning and tracking switching, non-stationary opponents

CSIR Research Space (South Africa)

Hernandez-Leal, P

2016-02-01

Full Text Available of interactions. We propose using a Bayesian framework to address this problem. Bayesian policy reuse (BPR) has been empirically shown to be efficient at correctly detecting the best policy to use from a library in sequential decision tasks. In this paper we...

Uncertainty analysis of pollutant build-up modelling based on a Bayesian weighted least squares approach

International Nuclear Information System (INIS)

Haddad, Khaled; Egodawatta, Prasanna; Rahman, Ataur; Goonetilleke, Ashantha

2013-01-01

Reliable pollutant build-up prediction plays a critical role in the accuracy of urban stormwater quality modelling outcomes. However, water quality data collection is resource demanding compared to streamflow data monitoring, where a greater quantity of data is generally available. Consequently, available water quality datasets span only relatively short time scales unlike water quantity data. Therefore, the ability to take due consideration of the variability associated with pollutant processes and natural phenomena is constrained. This in turn gives rise to uncertainty in the modelling outcomes as research has shown that pollutant loadings on catchment surfaces and rainfall within an area can vary considerably over space and time scales. Therefore, the assessment of model uncertainty is an essential element of informed decision making in urban stormwater management. This paper presents the application of a range of regression approaches such as ordinary least squares regression, weighted least squares regression and Bayesian weighted least squares regression for the estimation of uncertainty associated with pollutant build-up prediction using limited datasets. The study outcomes confirmed that the use of ordinary least squares regression with fixed model inputs and limited observational data may not provide realistic estimates. The stochastic nature of the dependent and independent variables need to be taken into consideration in pollutant build-up prediction. It was found that the use of the Bayesian approach along with the Monte Carlo simulation technique provides a powerful tool, which attempts to make the best use of the available knowledge in prediction and thereby presents a practical solution to counteract the limitations which are otherwise imposed on water quality modelling. - Highlights: ► Water quality data spans short time scales leading to significant model uncertainty. ► Assessment of uncertainty essential for informed decision making in water

Précis of bayesian rationality: The probabilistic approach to human reasoning.

Science.gov (United States)

Oaksford, Mike; Chater, Nick

2009-02-01

According to Aristotle, humans are the rational animal. The borderline between rationality and irrationality is fundamental to many aspects of human life including the law, mental health, and language interpretation. But what is it to be rational? One answer, deeply embedded in the Western intellectual tradition since ancient Greece, is that rationality concerns reasoning according to the rules of logic--the formal theory that specifies the inferential connections that hold with certainty between propositions. Piaget viewed logical reasoning as defining the end-point of cognitive development; and contemporary psychology of reasoning has focussed on comparing human reasoning against logical standards. Bayesian Rationality argues that rationality is defined instead by the ability to reason about uncertainty. Although people are typically poor at numerical reasoning about probability, human thought is sensitive to subtle patterns of qualitative Bayesian, probabilistic reasoning. In Chapters 1-4 of Bayesian Rationality (Oaksford & Chater 2007), the case is made that cognition in general, and human everyday reasoning in particular, is best viewed as solving probabilistic, rather than logical, inference problems. In Chapters 5-7 the psychology of "deductive" reasoning is tackled head-on: It is argued that purportedly "logical" reasoning problems, revealing apparently irrational behaviour, are better understood from a probabilistic point of view. Data from conditional reasoning, Wason's selection task, and syllogistic inference are captured by recasting these problems probabilistically. The probabilistic approach makes a variety of novel predictions which have been experimentally confirmed. The book considers the implications of this work, and the wider "probabilistic turn" in cognitive science and artificial intelligence, for understanding human rationality.

Bayesian Recovery of Clipped OFDM Signals: A Receiver-based Approach

KAUST Repository

Al-Rabah, Abdullatif R.

2013-05-01

Recently, orthogonal frequency-division multiplexing (OFDM) has been adopted for high-speed wireless communications due to its robustness against multipath fading. However, one of the main fundamental drawbacks of OFDM systems is the high peak-to-average-power ratio (PAPR). Several techniques have been proposed for PAPR reduction. Most of these techniques require transmitter-based (pre-compensated) processing. On the other hand, receiver-based alternatives would save the power and reduce the transmitter complexity. By keeping this in mind, a possible approach is to limit the amplitude of the OFDM signal to a predetermined threshold and equivalently a sparse clipping signal is added. Then, estimating this clipping signal at the receiver to recover the original signal. In this work, we propose a Bayesian receiver-based low-complexity clipping signal recovery method for PAPR reduction. The method is able to i) effectively reduce the PAPR via simple clipping scheme at the transmitter side, ii) use Bayesian recovery algorithm to reconstruct the clipping signal at the receiver side by measuring part of subcarriers, iii) perform well in the absence of statistical information about the signal (e.g. clipping level) and the noise (e.g. noise variance), and at the same time iv is energy efficient due to its low complexity. Specifically, the proposed recovery technique is implemented in data-aided based. The data-aided method collects clipping information by measuring reliable 
data subcarriers, thus makes full use of spectrum for data transmission without the need for tone reservation. The study is extended further to discuss how to improve the recovery of the clipping signal utilizing some features of practical OFDM systems i.e., the oversampling and the presence of multiple receivers. Simulation results demonstrate the superiority of the proposed technique over other recovery algorithms. The overall objective is to show that the receiver-based Bayesian technique is highly

A study of finite mixture model: Bayesian approach on financial time series data

Science.gov (United States)

Phoong, Seuk-Yen; Ismail, Mohd Tahir

2014-07-01

Recently, statistician have emphasized on the fitting finite mixture model by using Bayesian method. Finite mixture model is a mixture of distributions in modeling a statistical distribution meanwhile Bayesian method is a statistical method that use to fit the mixture model. Bayesian method is being used widely because it has asymptotic properties which provide remarkable result. In addition, Bayesian method also shows consistency characteristic which means the parameter estimates are close to the predictive distributions. In the present paper, the number of components for mixture model is studied by using Bayesian Information Criterion. Identify the number of component is important because it may lead to an invalid result. Later, the Bayesian method is utilized to fit the k-component mixture model in order to explore the relationship between rubber price and stock market price for Malaysia, Thailand, Philippines and Indonesia. Lastly, the results showed that there is a negative effect among rubber price and stock market price for all selected countries.

Assessing compositional variability through graphical analysis and Bayesian statistical approaches: case studies on transgenic crops.

Science.gov (United States)

Harrigan, George G; Harrison, Jay M

2012-01-01

New transgenic (GM) crops are subjected to extensive safety assessments that include compositional comparisons with conventional counterparts as a cornerstone of the process. The influence of germplasm, location, environment, and agronomic treatments on compositional variability is, however, often obscured in these pair-wise comparisons. Furthermore, classical statistical significance testing can often provide an incomplete and over-simplified summary of highly responsive variables such as crop composition. In order to more clearly describe the influence of the numerous sources of compositional variation we present an introduction to two alternative but complementary approaches to data analysis and interpretation. These include i) exploratory data analysis (EDA) with its emphasis on visualization and graphics-based approaches and ii) Bayesian statistical methodology that provides easily interpretable and meaningful evaluations of data in terms of probability distributions. The EDA case-studies include analyses of herbicide-tolerant GM soybean and insect-protected GM maize and soybean. Bayesian approaches are presented in an analysis of herbicide-tolerant GM soybean. Advantages of these approaches over classical frequentist significance testing include the more direct interpretation of results in terms of probabilities pertaining to quantities of interest and no confusion over the application of corrections for multiple comparisons. It is concluded that a standardized framework for these methodologies could provide specific advantages through enhanced clarity of presentation and interpretation in comparative assessments of crop composition.

GLUE 2 deployment: Ensuring quality in the EGI/WLCG information system

International Nuclear Information System (INIS)

Burke, Stephen; Pradillo, Maria Alandes; Field, Laurence; Keeble, Oliver

2014-01-01

The GLUE 2 information model is now fully supported in the production EGI/WLCG information system. However, to make it usable and allow clients to rely on the published information it is important that the meaning is clearly defined, and that information providers and site configurations are validated to ensure as far as possible that what they publish is correct. In this paper we describe the definition of a detailed schema usage profile, the implementation of a software tool to validate published information according to the profile and the use of the tool in the production Grid, and also summarise the overall state of GLUE 2 deployment.

A Bayesian trans-dimensional approach for the fusion of multiple geophysical datasets

Science.gov (United States)

JafarGandomi, Arash; Binley, Andrew

2013-09-01

We propose a Bayesian fusion approach to integrate multiple geophysical datasets with different coverage and sensitivity. The fusion strategy is based on the capability of various geophysical methods to provide enough resolution to identify either subsurface material parameters or subsurface structure, or both. We focus on electrical resistivity as the target material parameter and electrical resistivity tomography (ERT), electromagnetic induction (EMI), and ground penetrating radar (GPR) as the set of geophysical methods. However, extending the approach to different sets of geophysical parameters and methods is straightforward. Different geophysical datasets are entered into a trans-dimensional Markov chain Monte Carlo (McMC) search-based joint inversion algorithm. The trans-dimensional property of the McMC algorithm allows dynamic parameterisation of the model space, which in turn helps to avoid bias of the post-inversion results towards a particular model. Given that we are attempting to develop an approach that has practical potential, we discretize the subsurface into an array of one-dimensional earth-models. Accordingly, the ERT data that are collected by using two-dimensional acquisition geometry are re-casted to a set of equivalent vertical electric soundings. Different data are inverted either individually or jointly to estimate one-dimensional subsurface models at discrete locations. We use Shannon's information measure to quantify the information obtained from the inversion of different combinations of geophysical datasets. Information from multiple methods is brought together via introducing joint likelihood function and/or constraining the prior information. A Bayesian maximum entropy approach is used for spatial fusion of spatially dispersed estimated one-dimensional models and mapping of the target parameter. We illustrate the approach with a synthetic dataset and then apply it to a field dataset. We show that the proposed fusion strategy is

Physicochemical and functional properties, microstructure, and storage stability of whey protein/polyvinylpyrrolidone based glue sticks

Directory of Open Access Journals (Sweden)

Guorong Wang

2012-11-01

Full Text Available A glue stick is comprised of solidified adhesive mounted in a lipstick-like push-up tube. Whey is a byproduct of cheese making. Direct disposal of whey can cause environmental pollution. The objective of this study was to use whey protein isolate (WPI as a natural polymer along with polyvinylpyrrolidone (PVP to develop safe glue sticks. Pre-dissolved WPI solution, PVP, sucrose, 1,2-propanediol (PG, sodium stearate, defoamer, and preservative were mixed and dissolved in water at 90 oC and then molded in push-up tubes. Chemical composition, functional properties (bonding strength, glue setting time, gel hardness, extension/retraction, and spreading properties, microstructure, and storage stability of the prototypes were evaluated in comparison with a commercial control. Results showed that all WPI/PVP prototypes had desirable bonding strength and exhibited faster setting than PVP prototypes and control. WPI could reduce gel hardness and form less compact and rougher structures than that of PVP, but there was no difference in bonding strength. PVP and sucrose could increase the hygroscopicity of glue sticks, thus increasing storage stability. Finally, the optimized prototype GS3 (major components: WPI 8.0%, PVP 12.0%, 1,2-propanediol 10.0%, sucrose 10.0%, and stearic sodium 7.0% had a comparable functionality to the commercial control. Results indicated that whey protein could be used as an adhesive polymer for glue stick formulations, which could be used to bond fiber or cellulose derived substrates such as paper.

A Bayesian Approach to Excess Volatility, Short-term Underreaction and Long-term Overreaction during Financial Crises

NARCIS (Netherlands)

X. Guo (Xu); M.J. McAleer (Michael); W.-K. Wong (Wing-Keung); L. Zhu (Lixing)

2016-01-01

textabstractIn this paper, we introduce a new Bayesian approach to explain some market anomalies during financial crises and subsequent recovery. We assume that the earnings shock of an asset follows a random walk model with and without drift to incorporate the impact of financial crises. We further

Applying a Bayesian Approach to Identification of Orthotropic Elastic Constants from Full Field Displacement Measurements

Directory of Open Access Journals (Sweden)

Le Riche R.

2010-06-01

Full Text Available A major challenge in the identification of material properties is handling different sources of uncertainty in the experiment and the modelling of the experiment for estimating the resulting uncertainty in the identified properties. Numerous improvements in identification methods have provided increasingly accurate estimates of various material properties. However, characterizing the uncertainty in the identified properties is still relatively crude. Different material properties obtained from a single test are not obtained with the same confidence. Typically the highest uncertainty is associated with respect to properties to which the experiment is the most insensitive. In addition, the uncertainty in different properties can be strongly correlated, so that obtaining only variance estimates may be misleading. A possible approach for handling the different sources of uncertainty and estimating the uncertainty in the identified properties is the Bayesian method. This method was introduced in the late 1970s in the context of identification [1] and has been applied since to different problems, notably identification of elastic constants from plate vibration experiments [2]-[4]. The applications of the method to these classical pointwise tests involved only a small number of measurements (typically ten natural frequencies in the previously cited vibration test which facilitated the application of the Bayesian approach. For identifying elastic constants, full field strain or displacement measurements provide a high number of measured quantities (one measurement per image pixel and hence a promise of smaller uncertainties in the properties. However, the high number of measurements represents also a major computational challenge in applying the Bayesian approach to full field measurements. To address this challenge we propose an approach based on the proper orthogonal decomposition (POD of the full fields in order to drastically reduce their

Applying a Bayesian Approach to Identification of Orthotropic Elastic Constants from Full Field Displacement Measurements

Science.gov (United States)

Gogu, C.; Yin, W.; Haftka, R.; Ifju, P.; Molimard, J.; Le Riche, R.; Vautrin, A.

2010-06-01

A major challenge in the identification of material properties is handling different sources of uncertainty in the experiment and the modelling of the experiment for estimating the resulting uncertainty in the identified properties. Numerous improvements in identification methods have provided increasingly accurate estimates of various material properties. However, characterizing the uncertainty in the identified properties is still relatively crude. Different material properties obtained from a single test are not obtained with the same confidence. Typically the highest uncertainty is associated with respect to properties to which the experiment is the most insensitive. In addition, the uncertainty in different properties can be strongly correlated, so that obtaining only variance estimates may be misleading. A possible approach for handling the different sources of uncertainty and estimating the uncertainty in the identified properties is the Bayesian method. This method was introduced in the late 1970s in the context of identification [1] and has been applied since to different problems, notably identification of elastic constants from plate vibration experiments [2]-[4]. The applications of the method to these classical pointwise tests involved only a small number of measurements (typically ten natural frequencies in the previously cited vibration test) which facilitated the application of the Bayesian approach. For identifying elastic constants, full field strain or displacement measurements provide a high number of measured quantities (one measurement per image pixel) and hence a promise of smaller uncertainties in the properties. However, the high number of measurements represents also a major computational challenge in applying the Bayesian approach to full field measurements. To address this challenge we propose an approach based on the proper orthogonal decomposition (POD) of the full fields in order to drastically reduce their dimensionality. POD is

Quantifying uncertainty and resilience on coral reefs using a Bayesian approach

International Nuclear Information System (INIS)

Van Woesik, R

2013-01-01

Coral reefs are rapidly deteriorating globally. The contemporary management option favors managing for resilience to provide reefs with the capacity to tolerate human-induced disturbances. Yet resilience is most commonly defined as the capacity of a system to absorb disturbances without changing fundamental processes or functionality. Quantifying no change, or the uncertainty of a null hypothesis, is nonsensical using frequentist statistics, but is achievable using a Bayesian approach. This study outlines a practical Bayesian framework that quantifies the resilience of coral reefs using two inter-related models. The first model examines the functionality of coral reefs in the context of their reef-building capacity, whereas the second model examines the recovery rates of coral cover after disturbances. Quantifying intrinsic rates of increase in coral cover and habitat-specific, steady-state equilibria are useful proxies of resilience. A reduction in the intrinsic rate of increase following a disturbance, or the slowing of recovery over time, can be useful indicators of stress; a change in the steady-state equilibrium suggests a phase shift. Quantifying the uncertainty of key reef-building processes and recovery parameters, and comparing these parameters against benchmarks, facilitates the detection of loss of resilience and provides signals of imminent change. (letter)

Quantifying uncertainty and resilience on coral reefs using a Bayesian approach

Science.gov (United States)

van Woesik, R.

2013-12-01

Coral reefs are rapidly deteriorating globally. The contemporary management option favors managing for resilience to provide reefs with the capacity to tolerate human-induced disturbances. Yet resilience is most commonly defined as the capacity of a system to absorb disturbances without changing fundamental processes or functionality. Quantifying no change, or the uncertainty of a null hypothesis, is nonsensical using frequentist statistics, but is achievable using a Bayesian approach. This study outlines a practical Bayesian framework that quantifies the resilience of coral reefs using two inter-related models. The first model examines the functionality of coral reefs in the context of their reef-building capacity, whereas the second model examines the recovery rates of coral cover after disturbances. Quantifying intrinsic rates of increase in coral cover and habitat-specific, steady-state equilibria are useful proxies of resilience. A reduction in the intrinsic rate of increase following a disturbance, or the slowing of recovery over time, can be useful indicators of stress; a change in the steady-state equilibrium suggests a phase shift. Quantifying the uncertainty of key reef-building processes and recovery parameters, and comparing these parameters against benchmarks, facilitates the detection of loss of resilience and provides signals of imminent change.

Bayesian natural language semantics and pragmatics

CERN Document Server

Zeevat, Henk

2015-01-01

The contributions in this volume focus on the Bayesian interpretation of natural languages, which is widely used in areas of artificial intelligence, cognitive science, and computational linguistics. This is the first volume to take up topics in Bayesian Natural Language Interpretation and make proposals based on information theory, probability theory, and related fields. The methodologies offered here extend to the target semantic and pragmatic analyses of computational natural language interpretation. Bayesian approaches to natural language semantics and pragmatics are based on methods from signal processing and the causal Bayesian models pioneered by especially Pearl. In signal processing, the Bayesian method finds the most probable interpretation by finding the one that maximizes the product of the prior probability and the likelihood of the interpretation. It thus stresses the importance of a production model for interpretation as in Grice's contributions to pragmatics or in interpretation by abduction.

A Semiparametric Bayesian Approach for Analyzing Longitudinal Data from Multiple Related Groups.

Science.gov (United States)

Das, Kiranmoy; Afriyie, Prince; Spirko, Lauren

2015-11-01

Often the biological and/or clinical experiments result in longitudinal data from multiple related groups. The analysis of such data is quite challenging due to the fact that groups might have shared information on the mean and/or covariance functions. In this article, we consider a Bayesian semiparametric approach of modeling the mean trajectories for longitudinal response coming from multiple related groups. We consider matrix stick-breaking process priors on the group mean parameters which allows information sharing on the mean trajectories across the groups. Simulation studies are performed to demonstrate the effectiveness of the proposed approach compared to the more traditional approaches. We analyze data from a one-year follow-up of nutrition education for hypercholesterolemic children with three different treatments where the children are from different age-groups. Our analysis provides more clinically useful information than the previous analysis of the same dataset. The proposed approach will be a very powerful tool for analyzing data from clinical trials and other medical experiments.

Acute nonlymphocytic leukemia in a glue sniffer.

Science.gov (United States)

Caligiuri, M A; Early, A P; Marinello, M J; Preisler, H D

1985-09-01

A 17-year-old white male with a past history of chronic inhalational abuse of plastic glue was referred to our institution for sore throat, cervical adenopathy, and an abnormal peripheral blood smear. A diagnosis of acute myelomonocytic leukemia was made and abnormalities in cytogenetic studies were demonstrated. Specific inquiry regarding this form of drug exposure should be pursued when searching for possible etiologies of malignant disease.

Effect of fibrin glue on the biomechanical properties of human Descemet's membrane.

Directory of Open Access Journals (Sweden)

Shyam S Chaurasia

Full Text Available BACKGROUND: Corneal transplantation has rapidly evolved from full-thickness penetrating keratoplasty (PK to selective tissue corneal transplantation, where only the diseased portions of the patient's corneal tissue are replaced with healthy donor tissue. Descemet's membrane endothelial keratoplasty (DMEK performed in patients with corneal endothelial dysfunction is one such example where only a single layer of endothelial cells with its basement membrane (10-15 µm in thickness, Descemet's membrane (DM is replaced. It is challenging to replace this membrane due to its intrinsic property to roll in an aqueous environment. The main objective of this study was to determine the effects of fibrin glue (FG on the biomechanical properties of DM using atomic force microscopy (AFM and relates these properties to membrane folding propensity. METHODOLOGY/PRINCIPAL FINDINGS: Fibrin glue was sprayed using the EasySpray applicator system, and the biomechanical properties of human DM were determined by AFM. We studied the changes in the "rolling up" tendency of DM by examining the changes in the elasticity and flexural rigidity after the application of FG. Surface topography was assessed using scanning electron microscopy (SEM and AFM imaging. Treatment with FG not only stabilized and stiffened DM but also led to a significant increase in hysteresis of the glue-treated membrane. In addition, flexural or bending rigidity values also increased in FG-treated membranes. CONCLUSIONS/SIGNIFICANCE: Our results suggest that fibrin glue provides rigidity to the DM/endothelial cell complex that may aid in subsequent manipulation by maintaining tissue integrity.

Effect of Fibrin Glue on the Biomechanical Properties of Human Descemet's Membrane

Science.gov (United States)

Chaurasia, Shyam S.; Champakalakshmi, Ravi; Li, Ang; Poh, Rebekah; Tan, Xiao Wei; Lakshminarayanan, Rajamani; Lim, Chwee T.; Tan, Donald T.; Mehta, Jodhbir S.

2012-01-01

Background Corneal transplantation has rapidly evolved from full-thickness penetrating keratoplasty (PK) to selective tissue corneal transplantation, where only the diseased portions of the patient's corneal tissue are replaced with healthy donor tissue. Descemet's membrane endothelial keratoplasty (DMEK) performed in patients with corneal endothelial dysfunction is one such example where only a single layer of endothelial cells with its basement membrane (10–15 µm in thickness), Descemet's membrane (DM) is replaced. It is challenging to replace this membrane due to its intrinsic property to roll in an aqueous environment. The main objective of this study was to determine the effects of fibrin glue (FG) on the biomechanical properties of DM using atomic force microscopy (AFM) and relates these properties to membrane folding propensity. Methodology/Principal Findings Fibrin glue was sprayed using the EasySpray applicator system, and the biomechanical properties of human DM were determined by AFM. We studied the changes in the “rolling up” tendency of DM by examining the changes in the elasticity and flexural rigidity after the application of FG. Surface topography was assessed using scanning electron microscopy (SEM) and AFM imaging. Treatment with FG not only stabilized and stiffened DM but also led to a significant increase in hysteresis of the glue-treated membrane. In addition, flexural or bending rigidity values also increased in FG-treated membranes. Conclusions/Significance Our results suggest that fibrin glue provides rigidity to the DM/endothelial cell complex that may aid in subsequent manipulation by maintaining tissue integrity. PMID:22662156

Prediction of road accidents: A Bayesian hierarchical approach

DEFF Research Database (Denmark)

Deublein, Markus; Schubert, Matthias; Adey, Bryan T.

2013-01-01

the expected number of accidents in which an injury has occurred and the expected number of light, severe and fatally injured road users. Additionally, the methodology is used for geo-referenced identification of road sections with increased occurrence probabilities of injury accident events on a road link......In this paper a novel methodology for the prediction of the occurrence of road accidents is presented. The methodology utilizes a combination of three statistical methods: (1) gamma-updating of the occurrence rates of injury accidents and injured road users, (2) hierarchical multivariate Poisson......-lognormal regression analysis taking into account correlations amongst multiple dependent model response variables and effects of discrete accident count data e.g. over-dispersion, and (3) Bayesian inference algorithms, which are applied by means of data mining techniques supported by Bayesian Probabilistic Networks...

Bayesian estimation of dose rate effectiveness

International Nuclear Information System (INIS)

Arnish, J.J.; Groer, P.G.

2000-01-01

A Bayesian statistical method was used to quantify the effectiveness of high dose rate 137 Cs gamma radiation at inducing fatal mammary tumours and increasing the overall mortality rate in BALB/c female mice. The Bayesian approach considers both the temporal and dose dependence of radiation carcinogenesis and total mortality. This paper provides the first direct estimation of dose rate effectiveness using Bayesian statistics. This statistical approach provides a quantitative description of the uncertainty of the factor characterising the dose rate in terms of a probability density function. The results show that a fixed dose from 137 Cs gamma radiation delivered at a high dose rate is more effective at inducing fatal mammary tumours and increasing the overall mortality rate in BALB/c female mice than the same dose delivered at a low dose rate. (author)

Psychological Needs, Engagement, and Work Intentions: A Bayesian Multi-Measurement Mediation Approach and Implications for HRD

Science.gov (United States)

Shuck, Brad; Zigarmi, Drea; Owen, Jesse

2015-01-01

Purpose: The purpose of this study was to empirically examine the utility of self-determination theory (SDT) within the engagement-performance linkage. Design/methodology/approach: Bayesian multi-measurement mediation modeling was used to estimate the relation between SDT, engagement and a proxy measure of performance (e.g. work intentions) (N =…

A Bayesian network approach for modeling local failure in lung cancer

International Nuclear Information System (INIS)

Oh, Jung Hun; Craft, Jeffrey; Al Lozi, Rawan; Vaidya, Manushka; Meng, Yifan; Deasy, Joseph O; Bradley, Jeffrey D; El Naqa, Issam

2011-01-01

Locally advanced non-small cell lung cancer (NSCLC) patients suffer from a high local failure rate following radiotherapy. Despite many efforts to develop new dose-volume models for early detection of tumor local failure, there was no reported significant improvement in their application prospectively. Based on recent studies of biomarker proteins' role in hypoxia and inflammation in predicting tumor response to radiotherapy, we hypothesize that combining physical and biological factors with a suitable framework could improve the overall prediction. To test this hypothesis, we propose a graphical Bayesian network framework for predicting local failure in lung cancer. The proposed approach was tested using two different datasets of locally advanced NSCLC patients treated with radiotherapy. The first dataset was collected retrospectively, which comprises clinical and dosimetric variables only. The second dataset was collected prospectively in which in addition to clinical and dosimetric information, blood was drawn from the patients at various time points to extract candidate biomarkers as well. Our preliminary results show that the proposed method can be used as an efficient method to develop predictive models of local failure in these patients and to interpret relationships among the different variables in the models. We also demonstrate the potential use of heterogeneous physical and biological variables to improve the model prediction. With the first dataset, we achieved better performance compared with competing Bayesian-based classifiers. With the second dataset, the combined model had a slightly higher performance compared to individual physical and biological models, with the biological variables making the largest contribution. Our preliminary results highlight the potential of the proposed integrated approach for predicting post-radiotherapy local failure in NSCLC patients.

Basics of Bayesian methods.

Science.gov (United States)

Ghosh, Sujit K

2010-01-01

Bayesian methods are rapidly becoming popular tools for making statistical inference in various fields of science including biology, engineering, finance, and genetics. One of the key aspects of Bayesian inferential method is its logical foundation that provides a coherent framework to utilize not only empirical but also scientific information available to a researcher. Prior knowledge arising from scientific background, expert judgment, or previously collected data is used to build a prior distribution which is then combined with current data via the likelihood function to characterize the current state of knowledge using the so-called posterior distribution. Bayesian methods allow the use of models of complex physical phenomena that were previously too difficult to estimate (e.g., using asymptotic approximations). Bayesian methods offer a means of more fully understanding issues that are central to many practical problems by allowing researchers to build integrated models based on hierarchical conditional distributions that can be estimated even with limited amounts of data. Furthermore, advances in numerical integration methods, particularly those based on Monte Carlo methods, have made it possible to compute the optimal Bayes estimators. However, there is a reasonably wide gap between the background of the empirically trained scientists and the full weight of Bayesian statistical inference. Hence, one of the goals of this chapter is to bridge the gap by offering elementary to advanced concepts that emphasize linkages between standard approaches and full probability modeling via Bayesian methods.

Bayesian ensemble refinement by replica simulations and reweighting

Science.gov (United States)

Hummer, Gerhard; Köfinger, Jürgen

2015-12-01

We describe different Bayesian ensemble refinement methods, examine their interrelation, and discuss their practical application. With ensemble refinement, the properties of dynamic and partially disordered (bio)molecular structures can be characterized by integrating a wide range of experimental data, including measurements of ensemble-averaged observables. We start from a Bayesian formulation in which the posterior is a functional that ranks different configuration space distributions. By maximizing this posterior, we derive an optimal Bayesian ensemble distribution. For discrete configurations, this optimal distribution is identical to that obtained by the maximum entropy "ensemble refinement of SAXS" (EROS) formulation. Bayesian replica ensemble refinement enhances the sampling of relevant configurations by imposing restraints on averages of observables in coupled replica molecular dynamics simulations. We show that the strength of the restraints should scale linearly with the number of replicas to ensure convergence to the optimal Bayesian result in the limit of infinitely many replicas. In the "Bayesian inference of ensembles" method, we combine the replica and EROS approaches to accelerate the convergence. An adaptive algorithm can be used to sample directly from the optimal ensemble, without replicas. We discuss the incorporation of single-molecule measurements and dynamic observables such as relaxation parameters. The theoretical analysis of different Bayesian ensemble refinement approaches provides a basis for practical applications and a starting point for further investigations.

Sparse reconstruction using distribution agnostic bayesian matching pursuit

KAUST Repository

Masood, Mudassir; Al-Naffouri, Tareq Y.

2013-01-01

A fast matching pursuit method using a Bayesian approach is introduced for sparse signal recovery. This method performs Bayesian estimates of sparse signals even when the signal prior is non-Gaussian or unknown. It is agnostic on signal statistics

The use of snake venom derived fibrin glue in hysterorrhaphy of ovine caesarean surgery

OpenAIRE

CHALHOUB, M.; PRESTES, N. C.; LOPES, M. D.; ROCHA, N. S.; THOMAZINI-SANTOS, I. A.; MENDES-GIANNINI, M.J.

2000-01-01

Fibrin glue has been used on its own or in conjunction with suturing materials to promote hemostasis, reduce adherence, strengthen the wound site, and improve healing. Snake venom derived fibrin glue was evaluated as an alternative to conventional uterine suturing after ovine caesarean surgery. Twenty-eight pregnant ewes of known mating date were used. The animals submitted to conventional caesarean sections showed a better wound healing process. As expected, all the operated animals had reta...

A risk management process for reinforced concrete structures by coupling modelling, monitoring and Bayesian approaches

International Nuclear Information System (INIS)

Capra, Bruno; Li, Kefei; Wolff, Valentin; Bernard, Olivier; Gerard, Bruno

2004-01-01

The impact of steel corrosion on the durability of reinforced concrete structures has since a long time been a major concern in civil engineering. The main electrochemical mechanisms of the steel corrosion are know well known. The material and structure degradation is attributed to the progressive formation of an expansive corrosion product at the steel-concrete interface. To assess quantitatively the structure lifetime, a two-stage service life model has been accepted widely. So far, the research attention is mainly given to the corrosion in an un-cracked concrete. However. practically one is often confronted to the reinforcement corrosion in an already cracked concrete. How to quantify the corrosion risk is of great interest for the long term durability of these cracked structures. To this end, this paper proposes a service life modeling for the corrosion process by carbonation in a cracked or un-cracked concrete depending on the observation or monitoring data available. Some recent experimental investigations are used to calibrate the models. Then, the models are applied to a shell structure to quantify the corrosion process and determine the optimal maintenance strategy. As corrosion processes are very difficult to model and subjected to material and environmental random variations, an example of structure reassessment is presented taking into account in situ information by the mean of Bayesian approaches. The coupling of monitoring, modelling and updating leads to a new global maintenance strategy of infrastructure. In conclusion: This paper presents an unified methodology coupling predictive models, observations and Bayesian approaches in order to assess the degradation degree of an ageing structure. The particular case of corrosion is treated on an innovative way by the development of a service life model taking into account cracking effects on the kinetics of the phenomena. At a material level, the dominant factors are the crack opening and the crack nature

Fast Bayesian Non-Negative Matrix Factorisation and Tri-Factorisation

DEFF Research Database (Denmark)

Brouwer, Thomas; Frellsen, Jes; Liò, Pietro

We present a fast variational Bayesian algorithm for performing non-negative matrix factorisation and tri-factorisation. We show that our approach achieves faster convergence per iteration and timestep (wall-clock) than Gibbs sampling and non-probabilistic approaches, and do not require additional...... samples to estimate the posterior. We show that in particular for matrix tri-factorisation convergence is difficult, but our variational Bayesian approach offers a fast solution, allowing the tri-factorisation approach to be used more effectively....

A nonparametric Bayesian approach for genetic evaluation in ...

African Journals Online (AJOL)

Unknown

Finally, one can report the whole of the posterior probability distributions of the parameters in ... the Markov Chain Monte Carlo Methods, and more specific Gibbs Sampling, these ...... Bayesian Methods in Animal Breeding Theory. J. Anim. Sci.

A comparative study of tissue glue and vicryl suture for conjunctival and scleral closure in conventional 20-gauge vitrectomy.

Science.gov (United States)

Batman, C; Ozdamar, Y; Mutevelli, S; Sonmez, K; Zilelioglu, G; Karakaya, J

2009-06-01

To describe the use of tissue glue to close scleral and conjunctival wounds, and to compare the clinical outcomes using tissue glue and vicryl suture for closing these areas in conventional 20-gauge (G) vitrectomy. Thirty eyes of 30 patients were included in this study. The indications for vitreoretinal surgery were diabetic vitreous haemorrhage with severe vitreoretinal traction in 10 eyes, retinal detachment and proliferative vitreoretinopathy in 14 eyes, and vitreous opacity in 6 eyes. Tissue glue (Tisseel, Baxter AG Industries, Vienna, Austria) was used to attach scleral and conjunctival wounds in 15 eyes and vicryl sutures in 15 eyes. The patients were allotted into two subgroups as tissue glue group (TG) and vicryl suture group (VG). The sclerotomy sites were evaluated with ultrasound biomicroscopy (UBM) postoperatively in TG. Follow-up period was 2 months. The groups were statistically compared for ocular signs and symptoms by Mann-Whitney U-test. No scleral wound leakage and conjunctival reattachment were observed at the end of the surgical procedure and during the follow-up period. No adverse effects were seen in TG. Abnormal fibrous ingrowth was not detected at the sclerotomy sites by means of UBM in TG. Patient comfort was significantly higher in TG than VG (P<0.05). Tissue glue has no adverse effects on ocular tissue and can be used as a substitute for suture materials, and the use of tissue glue decreases patient symptoms during the postoperative period after 20-G vitrectomy. Tissue glue can enable to perform sutureless surgery in the conventional 20-G vitrectomy.

Multimethod, multistate Bayesian hierarchical modeling approach for use in regional monitoring of wolves.

Science.gov (United States)

Jiménez, José; García, Emilio J; Llaneza, Luis; Palacios, Vicente; González, Luis Mariano; García-Domínguez, Francisco; Múñoz-Igualada, Jaime; López-Bao, José Vicente

2016-08-01

In many cases, the first step in large-carnivore management is to obtain objective, reliable, and cost-effective estimates of population parameters through procedures that are reproducible over time. However, monitoring predators over large areas is difficult, and the data have a high level of uncertainty. We devised a practical multimethod and multistate modeling approach based on Bayesian hierarchical-site-occupancy models that combined multiple survey methods to estimate different population states for use in monitoring large predators at a regional scale. We used wolves (Canis lupus) as our model species and generated reliable estimates of the number of sites with wolf reproduction (presence of pups). We used 2 wolf data sets from Spain (Western Galicia in 2013 and Asturias in 2004) to test the approach. Based on howling surveys, the naïve estimation (i.e., estimate based only on observations) of the number of sites with reproduction was 9 and 25 sites in Western Galicia and Asturias, respectively. Our model showed 33.4 (SD 9.6) and 34.4 (3.9) sites with wolf reproduction, respectively. The number of occupied sites with wolf reproduction was 0.67 (SD 0.19) and 0.76 (0.11), respectively. This approach can be used to design more cost-effective monitoring programs (i.e., to define the sampling effort needed per site). Our approach should inspire well-coordinated surveys across multiple administrative borders and populations and lead to improved decision making for management of large carnivores on a landscape level. The use of this Bayesian framework provides a simple way to visualize the degree of uncertainty around population-parameter estimates and thus provides managers and stakeholders an intuitive approach to interpreting monitoring results. Our approach can be widely applied to large spatial scales in wildlife monitoring where detection probabilities differ between population states and where several methods are being used to estimate different population

Tests of optical glues for the PANDA electromagnetic calorimeter

NARCIS (Netherlands)

Dbeyssi, A.; Tomasi-Gustafsson, E.; Hennino, T.; Imre, M.; Kunne, R.; Le Galliard, C.; Marchand, D.; Maroni, A.; Ramstein, B.; Rosier, P.; Bremer, D.; Dormenev, V.; Eissner, T.; Kuske, T.; Novotny, R.; Moeini, H.; Bondarenko, O.; Kavatsyuk, M.; Loehner, H.; Messchendorp, G.; Tambave, G.

2013-01-01

This paper reports on the results of tests for low temperature applications of two commercial optical glues in the electromagnetic calorimeter of PANDA at FAIR. Mechanical, thermal and optical properties are presented, as well as radiation hardness to photon and proton radiation. (C) 2013 Elsevier

Development and comparison of Bayesian modularization method in uncertainty assessment of hydrological models

Science.gov (United States)

Li, L.; Xu, C.-Y.; Engeland, K.

2012-04-01

With respect to model calibration, parameter estimation and analysis of uncertainty sources, different approaches have been used in hydrological models. Bayesian method is one of the most widely used methods for uncertainty assessment of hydrological models, which incorporates different sources of information into a single analysis through Bayesian theorem. However, none of these applications can well treat the uncertainty in extreme flows of hydrological models' simulations. This study proposes a Bayesian modularization method approach in uncertainty assessment of conceptual hydrological models by considering the extreme flows. It includes a comprehensive comparison and evaluation of uncertainty assessments by a new Bayesian modularization method approach and traditional Bayesian models using the Metropolis Hasting (MH) algorithm with the daily hydrological model WASMOD. Three likelihood functions are used in combination with traditional Bayesian: the AR (1) plus Normal and time period independent model (Model 1), the AR (1) plus Normal and time period dependent model (Model 2) and the AR (1) plus multi-normal model (Model 3). The results reveal that (1) the simulations derived from Bayesian modularization method are more accurate with the highest Nash-Sutcliffe efficiency value, and (2) the Bayesian modularization method performs best in uncertainty estimates of entire flows and in terms of the application and computational efficiency. The study thus introduces a new approach for reducing the extreme flow's effect on the discharge uncertainty assessment of hydrological models via Bayesian. Keywords: extreme flow, uncertainty assessment, Bayesian modularization, hydrological model, WASMOD

A Bayesian Decision-Theoretic Approach to Logically-Consistent Hypothesis Testing

Directory of Open Access Journals (Sweden)

Gustavo Miranda da Silva

2015-09-01

Full Text Available This work addresses an important issue regarding the performance of simultaneous test procedures: the construction of multiple tests that at the same time are optimal from a statistical perspective and that also yield logically-consistent results that are easy to communicate to practitioners of statistical methods. For instance, if hypothesis A implies hypothesis B, is it possible to create optimal testing procedures that reject A whenever they reject B? Unfortunately, several standard testing procedures fail in having such logical consistency. Although this has been deeply investigated under a frequentist perspective, the literature lacks analyses under a Bayesian paradigm. In this work, we contribute to the discussion by investigating three rational relationships under a Bayesian decision-theoretic standpoint: coherence, invertibility and union consonance. We characterize and illustrate through simple examples optimal Bayes tests that fulfill each of these requisites separately. We also explore how far one can go by putting these requirements together. We show that although fairly intuitive tests satisfy both coherence and invertibility, no Bayesian testing scheme meets the desiderata as a whole, strengthening the understanding that logical consistency cannot be combined with statistical optimality in general. Finally, we associate Bayesian hypothesis testing with Bayes point estimation procedures. We prove the performance of logically-consistent hypothesis testing by means of a Bayes point estimator to be optimal only under very restrictive conditions.

Prediction of community prevalence of human onchocerciasis in the Amazonian onchocerciasis focus: Bayesian approach.

Science.gov (United States)

Carabin, Hélène; Escalona, Marisela; Marshall, Clare; Vivas-Martínez, Sarai; Botto, Carlos; Joseph, Lawrence; Basáñez, María-Gloria

2003-01-01

To develop a Bayesian hierarchical model for human onchocerciasis with which to explore the factors that influence prevalence of microfilariae in the Amazonian focus of onchocerciasis and predict the probability of any community being at least mesoendemic (>20% prevalence of microfilariae), and thus in need of priority ivermectin treatment. Models were developed with data from 732 individuals aged > or =15 years who lived in 29 Yanomami communities along four rivers of the south Venezuelan Orinoco basin. The models' abilities to predict prevalences of microfilariae in communities were compared. The deviance information criterion, Bayesian P-values, and residual values were used to select the best model with an approximate cross-validation procedure. A three-level model that acknowledged clustering of infection within communities performed best, with host age and sex included at the individual level, a river-dependent altitude effect at the community level, and additional clustering of communities along rivers. This model correctly classified 25/29 (86%) villages with respect to their need for priority ivermectin treatment. Bayesian methods are a flexible and useful approach for public health research and control planning. Our model acknowledges the clustering of infection within communities, allows investigation of links between individual- or community-specific characteristics and infection, incorporates additional uncertainty due to missing covariate data, and informs policy decisions by predicting the probability that a new community is at least mesoendemic.

Biomechanical and histologic evaluation of two application forms of surgical glue for mesh fixation to the abdominal wall.

Science.gov (United States)

Ortillés, Á; Pascual, G; Peña, E; Rodríguez, M; Pérez-Köhler, B; Mesa-Ciller, C; Calvo, B; Bellón, J M

2017-11-01

The use of an adhesive for mesh fixation in hernia repair reduces chronic pain and minimizes tissue damage in the patient. This study was designed to assess the adhesive properties of a medium-chain (n-butyl) cyanoacrylate glue applied as drops or as a spray in a biomechanical and histologic study. Both forms of glue application were compared to the use of simple-loose or continuous-running polypropylene sutures for mesh fixation. Eighteen adult New Zealand White rabbits were used. For mechanical tests in an ex vivo and in vivo study, patches of polypropylene mesh were fixed to an excised fragment of healthy abdominal tissue or used to repair a partial abdominal wall defect in the rabbit respectively. Depending on the fixation method used, four groups of 12 implants each or 10 implants each respectively for the ex vivo and in vivo studies were established: Glue-Drops, Glue-Spray, Suture-Simple and Suture-Continuous. Biomechanical resistance in the ex vivo implants was tested five minutes after mesh fixation. In vivo implants for biomechanical and histologic assessment were collected at 14 days postimplant. In the ex vivo study, the continuous suture implants showed the highest failure sample tension, while the implants fixed with glue showed lower failure sample tension values. However, the simple and continuous suture implants returned the highest stretch values. In the in vivo implants, failure sample tension values were similar among groups while the implants fixed with a continuous running suture had the higher stretch values, and the glue-fixed implants the lower stretch values. All meshes showed good tissue integration within the host tissue regardless of the fixation method used. Our histologic study revealed the generation of a denser, more mature repair tissue when the cyanoacrylate glue was applied as a spray rather than as drops. Copyright © 2017 Elsevier Ltd. All rights reserved.

Estimation of under-reported visceral Leishmaniasis (Vl cases in Bihar: a Bayesian approach

Directory of Open Access Journals (Sweden)

A Ranjan

2013-12-01

Full Text Available Background: Visceral leishmaniasis (VL is a major health problem in the state of Bihar and adjoining areas in India. In absence of any active surveillance mechanism for the disease, there seems to be gross under-reporting of VL cases. Objective: The objective of this study was to estimate extent of under-reporting of VL cases in Bihar using pooled analysis of published papers. Method: We calculated the pooled common ratio (RRMH based on three studies and combined it with a prior distribution of ratio using inverse-variance weighting method. Bayesian method was used to estimate the posterior distribution of the “under-reporting factor” (ratio of unreported to reported cases. Results: The posterior distribution of ratio of unreported to reported cases yielded a mean of 3.558, with 95% posterior limits of 2.81 and 4.50. Conclusion: Bayesian approach gives evidence to the fact that the total number of VL cases in the state may be nearly more than three times that of currently reported figures. 

Mechanistic curiosity will not kill the Bayesian cat

NARCIS (Netherlands)

Borsboom, D.; Wagenmakers, E.-J.; Romeijn, J.-W.

2011-01-01

Jones & Love (J&L) suggest that Bayesian approaches to the explanation of human behavior should be constrained by mechanistic theories. We argue that their proposal misconstrues the relation between process models, such as the Bayesian model, and mechanisms. While mechanistic theories can answer

Mechanistic curiosity will not kill the Bayesian cat

NARCIS (Netherlands)

Borsboom, Denny; Wagenmakers, Eric-Jan; Romeijn, Jan-Willem

Jones & Love (J&L) suggest that Bayesian approaches to the explanation of human behavior should be constrained by mechanistic theories. We argue that their proposal misconstrues the relation between process models, such as the Bayesian model, and mechanisms. While mechanistic theories can answer

Non-homogeneous dynamic Bayesian networks for continuous data

NARCIS (Netherlands)

Grzegorczyk, Marco; Husmeier, Dirk

Classical dynamic Bayesian networks (DBNs) are based on the homogeneous Markov assumption and cannot deal with non-homogeneous temporal processes. Various approaches to relax the homogeneity assumption have recently been proposed. The present paper presents a combination of a Bayesian network with

Bayesian models a statistical primer for ecologists

CERN Document Server

Hobbs, N Thompson

2015-01-01

Bayesian modeling has become an indispensable tool for ecological research because it is uniquely suited to deal with complexity in a statistically coherent way. This textbook provides a comprehensive and accessible introduction to the latest Bayesian methods-in language ecologists can understand. Unlike other books on the subject, this one emphasizes the principles behind the computations, giving ecologists a big-picture understanding of how to implement this powerful statistical approach. Bayesian Models is an essential primer for non-statisticians. It begins with a definition of probabili

Bayesian image restoration, using configurations

OpenAIRE

Thorarinsdottir, Thordis

2006-01-01

In this paper, we develop a Bayesian procedure for removing noise from images that can be viewed as noisy realisations of random sets in the plane. The procedure utilises recent advances in configuration theory for noise free random sets, where the probabilities of observing the different boundary configurations are expressed in terms of the mean normal measure of the random set. These probabilities are used as prior probabilities in a Bayesian image restoration approach. Estimation of the re...

Bayesian inference for psychology. Part I : Theoretical advantages and practical ramifications

NARCIS (Netherlands)

Wagenmakers, E.-J.; Marsman, M.; Jamil, T.; Ly, A.; Verhagen, J.; Love, J.; Selker, R.; Gronau, Q.F.; Šmíra, M.; Epskamp, S.; Matzke, D.; Rouder, J.N.; Morey, R.D.

2018-01-01

Bayesian parameter estimation and Bayesian hypothesis testing present attractive alternatives to classical inference using confidence intervals and p values. In part I of this series we outline ten prominent advantages of the Bayesian approach. Many of these advantages translate to concrete

Properties of invariant modelling and invariant glueing of vector fields

International Nuclear Information System (INIS)

Petukhov, V.R.

1987-01-01

Invariant modelling and invariant glueing of both continuous (rates and accelerations) and descrete vector fields, gradient and divergence cases are considered. The following appendices are discussed: vector fields in crystals, crystal disclinations, topological charges and their fields

A Bayesian approach to extracting meaning from system behavior

Energy Technology Data Exchange (ETDEWEB)

Dress, W.B.

1998-08-01

The modeling relation and its reformulation to include the semiotic hierarchy is essential for the understanding, control, and successful re-creation of natural systems. This presentation will argue for a careful application of Rosen`s modeling relationship to the problems of intelligence and autonomy in natural and artificial systems. To this end, the authors discuss the essential need for a correct theory of induction, learning, and probability; and suggest that modern Bayesian probability theory, developed by Cox, Jaynes, and others, can adequately meet such demands, especially on the operational level of extracting meaning from observations. The methods of Bayesian and maximum Entropy parameter estimation have been applied to measurements of system observables to directly infer the underlying differential equations generating system behavior. This approach by-passes the usual method of parameter estimation based on assuming a functional form for the observable and then estimating the parameters that would lead to the particular observed behavior. The computational savings is great since only location parameters enter into the maximum-entropy calculations; this innovation finesses the need for nonlinear parameters altogether. Such an approach more directly extracts the semantics inherent in a given system by going to the root of system meaning as expressed by abstract form or shape, rather than in syntactic particulars, such as signal amplitude and phase. Examples will be shown how the form of a system can be followed while ignoring unnecessary details. In this sense, the authors are observing the meaning of the words rather than being concerned with their particular expression or language. For the present discussion, empirical models are embodied by the differential equations underlying, producing, or describing the behavior of a process as measured or tracked by a particular variable set--the observables. The a priori models are probability structures that

Editorial: Bayesian benefits for child psychology and psychiatry researchers.

Science.gov (United States)

Oldehinkel, Albertine J

2016-09-01

For many scientists, performing statistical tests has become an almost automated routine. However, p-values are frequently used and interpreted incorrectly; and even when used appropriately, p-values tend to provide answers that do not match researchers' questions and hypotheses well. Bayesian statistics present an elegant and often more suitable alternative. The Bayesian approach has rarely been applied in child psychology and psychiatry research so far, but the development of user-friendly software packages and tutorials has placed it well within reach now. Because Bayesian analyses require a more refined definition of hypothesized probabilities of possible outcomes than the classical approach, going Bayesian may offer the additional benefit of sparkling the development and refinement of theoretical models in our field. © 2016 Association for Child and Adolescent Mental Health.

A new approach for supply chain risk management: Mapping SCOR into Bayesian network

Directory of Open Access Journals (Sweden)

Mahdi Abolghasemi

2015-01-01

Full Text Available Purpose: Increase of costs and complexities in organizations beside the increase of uncertainty and risks have led the managers to use the risk management in order to decrease risk taking and deviation from goals. SCRM has a close relationship with supply chain performance. During the years different methods have been used by researchers in order to manage supply chain risk but most of them are either qualitative or quantitative. Supply chain operation reference (SCOR is a standard model for SCP evaluation which have uncertainty in its metrics. In This paper by combining qualitative and quantitative metrics of SCOR, supply chain performance will be measured by Bayesian Networks. Design/methodology/approach: First qualitative assessment will be done by recognizing uncertain metrics of SCOR model and then by quantifying them, supply chain performance will be measured by Bayesian Networks (BNs and supply chain operations reference (SCOR in which making decision on uncertain variables will be done by predictive and diagnostic capabilities. Findings: After applying the proposed method in one of the biggest automotive companies in Iran, we identified key factors of supply chain performance based on SCOR model through predictive and diagnostic capability of Bayesian Networks. After sensitivity analysis, we find out that ‘Total cost’ and its criteria that include costs of labors, warranty, transportation and inventory have the widest range and most effect on supply chain performance. So, managers should take their importance into account for decision making. We can make decisions simply by running model in different situations. Research limitations/implications: A more precise model consisted of numerous factors but it is difficult and sometimes impossible to solve big models, if we insert all of them in a Bayesian model. We have adopted real world characteristics with our software and method abilities. On the other hand, fewer data exist for some

Peering through a dirty window: A Bayesian approach to making mine detection decisions from noisy data

Energy Technology Data Exchange (ETDEWEB)

Kercel, Stephen W.

1998-10-11

For several reasons, Bayesian parameter estimation is superior to other methods for extracting features of a weak signal from noise. Since it exploits prior knowledge, the analysis begins from a more advantageous starting point than other methods. Also, since ''nuisance parameters'' can be dropped out of the Bayesian analysis, the description of the model need not be as complete as is necessary for such methods as matched filtering. In the limit for perfectly random noise and a perfect description of the model, the signal-to-noise ratio improves as the square root of the number of samples in the data. Even with the imperfections of real-world data, Bayesian approaches this ideal limit of performance more closely than other methods. A major unsolved problem in landmine detection is the fusion of data from multiple sensor types. Bayesian data fusion is only beginning to be explored as a solution to the problem. In single sensor processes Bayesian analysis can sense multiple parameters from the data stream of the one sensor. It does so by computing a joint probability density function of a set of parameter values from the sensor output. However, there is no inherent requirement that the information must come from a single sensor. If multiple sensors are applied to a single process, where several different parameters are implicit in each sensor output data stream, the joint probability density function of all the parameters of interest can be computed in exactly the same manner as the single sensor case. Thus, it is just as practical to base decisions on multiple sensor outputs as it is for single sensors. This should provide a practical way to combine the outputs of dissimilar sensors, such as ground penetrating radar and electromagnetic induction devices, producing a better detection decision than could be provided by either sensor alone.

Peering through a dirty window: A Bayesian approach to making mine detection decisions from noisy data

International Nuclear Information System (INIS)

Kercel, Stephen W.

1998-01-01

For several reasons, Bayesian parameter estimation is superior to other methods for extracting features of a weak signal from noise. Since it exploits prior knowledge, the analysis begins from a more advantageous starting point than other methods. Also, since ''nuisance parameters'' can be dropped out of the Bayesian analysis, the description of the model need not be as complete as is necessary for such methods as matched filtering. In the limit for perfectly random noise and a perfect description of the model, the signal-to-noise ratio improves as the square root of the number of samples in the data. Even with the imperfections of real-world data, Bayesian approaches this ideal limit of performance more closely than other methods. A major unsolved problem in landmine detection is the fusion of data from multiple sensor types. Bayesian data fusion is only beginning to be explored as a solution to the problem. In single sensor processes Bayesian analysis can sense multiple parameters from the data stream of the one sensor. It does so by computing a joint probability density function of a set of parameter values from the sensor output. However, there is no inherent requirement that the information must come from a single sensor. If multiple sensors are applied to a single process, where several different parameters are implicit in each sensor output data stream, the joint probability density function of all the parameters of interest can be computed in exactly the same manner as the single sensor case. Thus, it is just as practical to base decisions on multiple sensor outputs as it is for single sensors. This should provide a practical way to combine the outputs of dissimilar sensors, such as ground penetrating radar and electromagnetic induction devices, producing a better detection decision than could be provided by either sensor alone

Bayesian estimation and tracking a practical guide

CERN Document Server

Haug, Anton J

2012-01-01

A practical approach to estimating and tracking dynamic systems in real-worl applications Much of the literature on performing estimation for non-Gaussian systems is short on practical methodology, while Gaussian methods often lack a cohesive derivation. Bayesian Estimation and Tracking addresses the gap in the field on both accounts, providing readers with a comprehensive overview of methods for estimating both linear and nonlinear dynamic systems driven by Gaussian and non-Gaussian noices. Featuring a unified approach to Bayesian estimation and tracking, the book emphasizes the derivation

Glasses, Coatings, Glues and Gamma-ray Irradiation

International Nuclear Information System (INIS)

Barcala, J.M.; Fernandez, M. G.; Ferrando, A.; Fuentes, J.; Josa, M. I.; Molinero, A.; Oller, J. C.; Arce, P.; Calvo, E.; Figueroa, C. F.; Rodrigo, T.; Vila, I.; Virto, A. L.; Beigveder, J. M.; Genova, I.; Perez, G.; Ruiz, J. A.

2001-01-01

Most of the alignment systems for LHC experiments use optomechanical elements confirming a network of points that are monitored by laser beams. LHC experiments, working at the expected nominal luminosity, will induce an extremely high irradiation. basic components such as glasses, coatings and glues may change and their performance may degrade significantly. We have tested various components and identified some of them that can stand 10 years of LHC operation. (Author) 11 refs

Glasses, Coatings, Glues and Gamma-ray Irradiation

Energy Technology Data Exchange (ETDEWEB)

Barcala, J.M.; Fernandez, M. G.; Ferrando, A.; Fuentes, J.; Josa, M. I.; Molinero, A.; Oller, J. C. [Ciemat. Madrid (Spain); Arce, P.; Calvo, E.; Figueroa, C. F.; Rodrigo, T.; Vila, I.; Virto, A. L. [Universidad de Cantabria. Santander (Spain); Beigveder, J. M.; Genova, I.; Perez, G.; Ruiz, J. A. [CIDA. Madrid (Spain)

2001-07-01

Most of the alignment systems for LHC experiments use optomechanical elements confirming a network of points that are monitored by laser beams. LHC experiments, working at the expected nominal luminosity, will induce an extremely high irradiation. basic components such as glasses, coatings and glues may change and their performance may degrade significantly. We have tested various components and identified some of them that can stand 10 years of LHC operation. (Author) 11 refs.

Bayesian community detection

DEFF Research Database (Denmark)

Mørup, Morten; Schmidt, Mikkel N

2012-01-01

Many networks of scientific interest naturally decompose into clusters or communities with comparatively fewer external than internal links; however, current Bayesian models of network communities do not exert this intuitive notion of communities. We formulate a nonparametric Bayesian model...... for community detection consistent with an intuitive definition of communities and present a Markov chain Monte Carlo procedure for inferring the community structure. A Matlab toolbox with the proposed inference procedure is available for download. On synthetic and real networks, our model detects communities...... consistent with ground truth, and on real networks, it outperforms existing approaches in predicting missing links. This suggests that community structure is an important structural property of networks that should be explicitly modeled....

Successful Endoscopic Management of Non-Healing Perforated Duodenal Ulcer with Polyglycolic Acid Sheet and Fibrin Glue.

Science.gov (United States)

Mishiro, Tsuyoshi; Shibagaki, Kotaro; Matsuda, Kayo; Fukuyama, Chika; Okada, Mayumi; Mikami, Hironobu; Izumi, Daisuke; Yamashita, Noritsugu; Okimoto, Eiko; Fukuda, Naoki; Aimi, Masahito; Fukuba, Nobuhiko; Oshima, Naoki; Takanashi, Toshihiro; Matsubara, Takeshi; Ishimura, Norihisa; Ishihara, Shunji; Kinoshita, Yoshikazu

2016-08-01

In recent years, treatment techniques in which polyglycolic acid sheets are applied to various situations with fibrin glue have exhibited great clinical potential, and previous studies have reported safety and efficacy. We describe closure of a non-healing perforated duodenal ulcer with the use of a polyglycolic acid sheet and fibrin glue in an elderly patient who was not a candidate for surgery.

Efficient fuzzy Bayesian inference algorithms for incorporating expert knowledge in parameter estimation

Science.gov (United States)

Rajabi, Mohammad Mahdi; Ataie-Ashtiani, Behzad

2016-05-01

Bayesian inference has traditionally been conceived as the proper framework for the formal incorporation of expert knowledge in parameter estimation of groundwater models. However, conventional Bayesian inference is incapable of taking into account the imprecision essentially embedded in expert provided information. In order to solve this problem, a number of extensions to conventional Bayesian inference have been introduced in recent years. One of these extensions is 'fuzzy Bayesian inference' which is the result of integrating fuzzy techniques into Bayesian statistics. Fuzzy Bayesian inference has a number of desirable features which makes it an attractive approach for incorporating expert knowledge in the parameter estimation process of groundwater models: (1) it is well adapted to the nature of expert provided information, (2) it allows to distinguishably model both uncertainty and imprecision, and (3) it presents a framework for fusing expert provided information regarding the various inputs of the Bayesian inference algorithm. However an important obstacle in employing fuzzy Bayesian inference in groundwater numerical modeling applications is the computational burden, as the required number of numerical model simulations often becomes extremely exhaustive and often computationally infeasible. In this paper, a novel approach of accelerating the fuzzy Bayesian inference algorithm is proposed which is based on using approximate posterior distributions derived from surrogate modeling, as a screening tool in the computations. The proposed approach is first applied to a synthetic test case of seawater intrusion (SWI) in a coastal aquifer. It is shown that for this synthetic test case, the proposed approach decreases the number of required numerical simulations by an order of magnitude. Then the proposed approach is applied to a real-world test case involving three-dimensional numerical modeling of SWI in Kish Island, located in the Persian Gulf. An expert

Learning Bayesian Networks with Incomplete Data by Augmentation

OpenAIRE

Adel, Tameem; de Campos, Cassio P.

2016-01-01

We present new algorithms for learning Bayesian networks from data with missing values using a data augmentation approach. An exact Bayesian network learning algorithm is obtained by recasting the problem into a standard Bayesian network learning problem without missing data. To the best of our knowledge, this is the first exact algorithm for this problem. As expected, the exact algorithm does not scale to large domains. We build on the exact method to create an approximate algorithm using a ...

The Bayesian Approach to Association

Science.gov (United States)

Arora, N. S.

2017-12-01

The Bayesian approach to Association focuses mainly on quantifying the physics of the domain. In the case of seismic association for instance let X be the set of all significant events (above some threshold) and their attributes, such as location, time, and magnitude, Y1 be the set of detections that are caused by significant events and their attributes such as seismic phase, arrival time, amplitude etc., Y2 be the set of detections that are not caused by significant events, and finally Y be the set of observed detections We would now define the joint distribution P(X, Y1, Y2, Y) = P(X) P(Y1 | X) P(Y2) I(Y = Y1 + Y2) ; where the last term simply states that Y1 and Y2 are a partitioning of Y. Given the above joint distribution the inference problem is simply to find the X, Y1, and Y2 that maximizes posterior probability P(X, Y1, Y2| Y) which reduces to maximizing P(X) P(Y1 | X) P(Y2) I(Y = Y1 + Y2). In this expression P(X) captures our prior belief about event locations. P(Y1 | X) captures notions of travel time, residual error distributions as well as detection and mis-detection probabilities. While P(Y2) captures the false detection rate of our seismic network. The elegance of this approach is that all of the assumptions are stated clearly in the model for P(X), P(Y1|X) and P(Y2). The implementation of the inference is merely a by-product of this model. In contrast some of the other methods such as GA hide a number of assumptions in the implementation details of the inference - such as the so called "driver cells." The other important aspect of this approach is that all seismic knowledge including knowledge from other domains such as infrasound and hydroacoustic can be included in the same model. So, we don't need to separately account for misdetections or merge seismic and infrasound events as a separate step. Finally, it should be noted that the objective of automatic association is to simplify the job of humans who are publishing seismic bulletins based on this

A Bayesian Justification for Random Sampling in Sample Survey

Directory of Open Access Journals (Sweden)

Glen Meeden

2012-07-01

Full Text Available In the usual Bayesian approach to survey sampling the sampling design, plays a minimal role, at best. Although a close relationship between exchangeable prior distributions and simple random sampling has been noted; how to formally integrate simple random sampling into the Bayesian paradigm is not clear. Recently it has been argued that the sampling design can be thought of as part of a Bayesian's prior distribution. We will show here that under this scenario simple random sample can be given a Bayesian justification in survey sampling.

Fibrin glue mixed with platelet-rich fibrin as a scaffold seeded with dental bud cells for tooth regeneration.

Science.gov (United States)

Yang, Kai-Chiang; Wang, Chun-Hao; Chang, Hao-Hueng; Chan, Wing P; Chi, Chau-Hwa; Kuo, Tzong-Fu

2012-11-01

Odontogenesis is a complex process with a series of epithelial-mesenchymal interactions and odontogenic molecular cascades. In tissue engineering of teeth from stem cells, platelet-rich fibrin (PRF), which is rich in growth factors and cytokines, may improve regeneration. Accordingly, PRF was added into fibrin glue to enrich the microenvironment with growth factors. Unerupted second molar tooth buds were harvested from miniature swine and cultured in vitro for 3 weeks to obtain dental bud cells (DBCs). Whole blood was collected for the preparation of PRF and fibrin glue before surgery. DBCs were suspended in fibrin glue and then enclosed with PRF, and the DBC-fibrin glue-PRF composite was autografted back into the original alveolar sockets. Radiographic and histological examinations were used to identify the regenerated tooth structure 36 weeks after implantation. Immunohistochemical staining was used to detect proteins specific to tooth regeneration. One pig developed a complete tooth with crown, root, pulp, enamel, dentin, odontoblast, cementum, blood vessels, and periodontal ligaments in indiscriminate shape. Another animal had an unerupted tooth that expressed cytokeratin 14, dentin matrix protein-1, vascular endothelial growth factor, and osteopontin. This study demonstrated, using autogenic cell transplantation in a porcine model, that DBCs seeded into fibrin glue-PRF could regenerate a complete tooth. Copyright © 2011 John Wiley & Sons, Ltd.

Implementing the Bayesian paradigm in risk analysis

International Nuclear Information System (INIS)

Aven, T.; Kvaloey, J.T.

2002-01-01

The Bayesian paradigm comprises a unified and consistent framework for analyzing and expressing risk. Yet, we see rather few examples of applications where the full Bayesian setting has been adopted with specifications of priors of unknown parameters. In this paper, we discuss some of the practical challenges of implementing Bayesian thinking and methods in risk analysis, emphasizing the introduction of probability models and parameters and associated uncertainty assessments. We conclude that there is a need for a pragmatic view in order to 'successfully' apply the Bayesian approach, such that we can do the assignments of some of the probabilities without adopting the somewhat sophisticated procedure of specifying prior distributions of parameters. A simple risk analysis example is presented to illustrate ideas

Bayesian approach to inverse statistical mechanics

Science.gov (United States)

Habeck, Michael

2014-05-01

Inverse statistical mechanics aims to determine particle interactions from ensemble properties. This article looks at this inverse problem from a Bayesian perspective and discusses several statistical estimators to solve it. In addition, a sequential Monte Carlo algorithm is proposed that draws the interaction parameters from their posterior probability distribution. The posterior probability involves an intractable partition function that is estimated along with the interactions. The method is illustrated for inverse problems of varying complexity, including the estimation of a temperature, the inverse Ising problem, maximum entropy fitting, and the reconstruction of molecular interaction potentials.

Personalized Audio Systems - a Bayesian Approach

DEFF Research Database (Denmark)

Nielsen, Jens Brehm; Jensen, Bjørn Sand; Hansen, Toke Jansen

2013-01-01

Modern audio systems are typically equipped with several user-adjustable parameters unfamiliar to most users listening to the system. To obtain the best possible setting, the user is forced into multi-parameter optimization with respect to the users's own objective and preference. To address this......, the present paper presents a general inter-active framework for personalization of such audio systems. The framework builds on Bayesian Gaussian process regression in which a model of the users's objective function is updated sequentially. The parameter setting to be evaluated in a given trial is selected...

Bayesian Approach to Spectral Function Reconstruction for Euclidean Quantum Field Theories

Science.gov (United States)

Burnier, Yannis; Rothkopf, Alexander

2013-11-01

We present a novel approach to the inference of spectral functions from Euclidean time correlator data that makes close contact with modern Bayesian concepts. Our method differs significantly from the maximum entropy method (MEM). A new set of axioms is postulated for the prior probability, leading to an improved expression, which is devoid of the asymptotically flat directions present in the Shanon-Jaynes entropy. Hyperparameters are integrated out explicitly, liberating us from the Gaussian approximations underlying the evidence approach of the maximum entropy method. We present a realistic test of our method in the context of the nonperturbative extraction of the heavy quark potential. Based on hard-thermal-loop correlator mock data, we establish firm requirements in the number of data points and their accuracy for a successful extraction of the potential from lattice QCD. Finally we reinvestigate quenched lattice QCD correlators from a previous study and provide an improved potential estimation at T=2.33TC.

Optimizing Prediction Using Bayesian Model Averaging: Examples Using Large-Scale Educational Assessments.

Science.gov (United States)

Kaplan, David; Lee, Chansoon

2018-01-01

This article provides a review of Bayesian model averaging as a means of optimizing the predictive performance of common statistical models applied to large-scale educational assessments. The Bayesian framework recognizes that in addition to parameter uncertainty, there is uncertainty in the choice of models themselves. A Bayesian approach to addressing the problem of model uncertainty is the method of Bayesian model averaging. Bayesian model averaging searches the space of possible models for a set of submodels that satisfy certain scientific principles and then averages the coefficients across these submodels weighted by each model's posterior model probability (PMP). Using the weighted coefficients for prediction has been shown to yield optimal predictive performance according to certain scoring rules. We demonstrate the utility of Bayesian model averaging for prediction in education research with three examples: Bayesian regression analysis, Bayesian logistic regression, and a recently developed approach for Bayesian structural equation modeling. In each case, the model-averaged estimates are shown to yield better prediction of the outcome of interest than any submodel based on predictive coverage and the log-score rule. Implications for the design of large-scale assessments when the goal is optimal prediction in a policy context are discussed.

Approximation methods for efficient learning of Bayesian networks

CERN Document Server

Riggelsen, C

2008-01-01

This publication offers and investigates efficient Monte Carlo simulation methods in order to realize a Bayesian approach to approximate learning of Bayesian networks from both complete and incomplete data. For large amounts of incomplete data when Monte Carlo methods are inefficient, approximations are implemented, such that learning remains feasible, albeit non-Bayesian. The topics discussed are: basic concepts about probabilities, graph theory and conditional independence; Bayesian network learning from data; Monte Carlo simulation techniques; and, the concept of incomplete data. In order to provide a coherent treatment of matters, thereby helping the reader to gain a thorough understanding of the whole concept of learning Bayesian networks from (in)complete data, this publication combines in a clarifying way all the issues presented in the papers with previously unpublished work.

Bayesian emulation for optimization in multi-step portfolio decisions

OpenAIRE

Irie, Kaoru; West, Mike

2016-01-01

We discuss the Bayesian emulation approach to computational solution of multi-step portfolio studies in financial time series. "Bayesian emulation for decisions" involves mapping the technical structure of a decision analysis problem to that of Bayesian inference in a purely synthetic "emulating" statistical model. This provides access to standard posterior analytic, simulation and optimization methods that yield indirect solutions of the decision problem. We develop this in time series portf...

Gaussian process-based Bayesian nonparametric inference of population size trajectories from gene genealogies.

Science.gov (United States)

Palacios, Julia A; Minin, Vladimir N

2013-03-01

Changes in population size influence genetic diversity of the population and, as a result, leave a signature of these changes in individual genomes in the population. We are interested in the inverse problem of reconstructing past population dynamics from genomic data. We start with a standard framework based on the coalescent, a stochastic process that generates genealogies connecting randomly sampled individuals from the population of interest. These genealogies serve as a glue between the population demographic history and genomic sequences. It turns out that only the times of genealogical lineage coalescences contain information about population size dynamics. Viewing these coalescent times as a point process, estimating population size trajectories is equivalent to estimating a conditional intensity of this point process. Therefore, our inverse problem is similar to estimating an inhomogeneous Poisson process intensity function. We demonstrate how recent advances in Gaussian process-based nonparametric inference for Poisson processes can be extended to Bayesian nonparametric estimation of population size dynamics under the coalescent. We compare our Gaussian process (GP) approach to one of the state-of-the-art Gaussian Markov random field (GMRF) methods for estimating population trajectories. Using simulated data, we demonstrate that our method has better accuracy and precision. Next, we analyze two genealogies reconstructed from real sequences of hepatitis C and human Influenza A viruses. In both cases, we recover more believed aspects of the viral demographic histories than the GMRF approach. We also find that our GP method produces more reasonable uncertainty estimates than the GMRF method. Copyright © 2013, The International Biometric Society.

CT-guided percutaneous injection of the fibrin glue by 'double needle' technique for the treatment of sacral cysts

International Nuclear Information System (INIS)

Wang Ganggang; Chen Long; Yang Chao; Ni Caifang

2013-01-01

Objective: To analyze the efficacy and safety of CT-guided percutaneous injection of the fibrin glue by 'double needle' technique to treat sacral cyst. Methods: Clinical data of 20 cases with 'double-needle' injection of fibrin glue technology to treat sacral cyst were retrospectively analyzed. All patients had varying degrees of sacral nerve root compression symptoms. The treatment for sacral cyst was carried out after clear diagnosis was made. On the basis of CT-guided percutaneous injection of fibrin glue, the improved CT-guided percutaneous injection of fibrin glue by 'double-needle' technique was used to treat these patients. The average dose of fibrin glue was (5.9 ± 2.4) ml. The clinical results of improvement as to pain and neurological function were evaluated after follow-up of an average of 17 months. The assessment criteria were as follows: excellent, complete resolution of signs and symptoms, with the patient returning to his or her regular employment and no recurrence of cysts during 1 year of follow-up, good, symptoms and signs in the legs and perineal region resolved but with persistent pain in the lumbosacral region, which did not interfere with the patient's regular work (the cysts did not recur for 6 months during follow-up), fair, no improvement in clinical symptoms, but a decrease in cyst size on the imaging study, poor, no improvement in clinical symptoms and no observed changes in cyst size in imaging studies or recurrence. Results: Most patients experienced some degree of pain relief and functional improvement after fibrin glue therapy, with most experiencing complete or marked resolution of clinical symptoms. Nine patients reported excellent recovery, 8 reported good recovery, 2 reported fair recovery, and 1 reported poor recovery. The overall percentage of positive outcomes (excellent and good recovery) was 85%. No serious postoperative complications were discovered. Conclusions: CT guided percutaneous injection of the fibrin glue by

SU-F-T-141: Proton Dose Validation in a Phantom Beyond TRUFILL N-BCA Embolization Glue

International Nuclear Information System (INIS)

Mandapaka, A; Ghebremedhin, A; Patyal, B; Linda, Loma

2016-01-01

Purpose: To validate the treatment planning system predicted proton dose beyond a heterogeneity (n-BCA glue) by making a measurement in a custom acrylic phantom. Methods: A custom cubic acrylic phantom was designed for this experiment. A container was designed to fit in the phantom. This container was filled with TRUFILL™ n-Butyl Cyanoacrylate(n-BCA) glue. When the container was placed in the phantom, its center was at a distance of 7.4cm from the entrance. This depth allows us to make measurements around the center of modulation of a 126 MeV proton beam with a 3cm spread-out-Bragg peak. To make measurements at other beam energies, additional acrylic can be added in front of the phantom, to adjust the depth of the heterogeneity. A diamond detector was cross calibrated against a standard cylindrical ion chamber in a 126MeV beam. The diamond detector was then used to make dose measurements beyond the inhomogeneity. The measurement was repeated with the container filled with water. Several measurements were made at each setup, to check reproducibility of measurements. Results: For the same number of Tic3R1 counts, the dose measured with the diamond detector beyond n-BCA glue was 1.053 times the dose measured beyond the water filled container. This result is in agreement with the measured stopping power of glue (1.06). These measurements were in agreement with the dose predicted by the treatment planning system when the electron density of the heterogeneity was replaced with 1.06 before the dose calculation. Conclusion: Our initial measurements validate the dose predicted by our treatment plan in the presence of heterogeneity in a phantom. The material tested (n-BCA glue) is commonly used in the treatment of AVM’s prior to an SRS treatment. An error in dose predicted by the treatment plan in the presence of the glue can be detrimental in a single fraction high dose SRS treatment I received the n-BCA liquid embolic system samples from Codman and Shurtleff, Inc.

SU-F-T-141: Proton Dose Validation in a Phantom Beyond TRUFILL N-BCA Embolization Glue

Energy Technology Data Exchange (ETDEWEB)

Mandapaka, A; Ghebremedhin, A; Patyal, B; Linda, Loma [University Medical Center, Loma Linda, CA (United States)

2016-06-15

Purpose: To validate the treatment planning system predicted proton dose beyond a heterogeneity (n-BCA glue) by making a measurement in a custom acrylic phantom. Methods: A custom cubic acrylic phantom was designed for this experiment. A container was designed to fit in the phantom. This container was filled with TRUFILL™ n-Butyl Cyanoacrylate(n-BCA) glue. When the container was placed in the phantom, its center was at a distance of 7.4cm from the entrance. This depth allows us to make measurements around the center of modulation of a 126 MeV proton beam with a 3cm spread-out-Bragg peak. To make measurements at other beam energies, additional acrylic can be added in front of the phantom, to adjust the depth of the heterogeneity. A diamond detector was cross calibrated against a standard cylindrical ion chamber in a 126MeV beam. The diamond detector was then used to make dose measurements beyond the inhomogeneity. The measurement was repeated with the container filled with water. Several measurements were made at each setup, to check reproducibility of measurements. Results: For the same number of Tic3R1 counts, the dose measured with the diamond detector beyond n-BCA glue was 1.053 times the dose measured beyond the water filled container. This result is in agreement with the measured stopping power of glue (1.06). These measurements were in agreement with the dose predicted by the treatment planning system when the electron density of the heterogeneity was replaced with 1.06 before the dose calculation. Conclusion: Our initial measurements validate the dose predicted by our treatment plan in the presence of heterogeneity in a phantom. The material tested (n-BCA glue) is commonly used in the treatment of AVM’s prior to an SRS treatment. An error in dose predicted by the treatment plan in the presence of the glue can be detrimental in a single fraction high dose SRS treatment I received the n-BCA liquid embolic system samples from Codman and Shurtleff, Inc.

Bridging the gap between GLUE and formal statistical approaches: approximate Bayesian computation

NARCIS (Netherlands)

Sadegh, M.; Vrugt, J.A.

2013-01-01

In recent years, a strong debate has emerged in the hydrologic literature regarding how to properly treat nontraditional error residual distributions and quantify parameter and predictive uncertainty. Particularly, there is strong disagreement whether such uncertainty framework should have its roots

Bayesian Population Physiologically-Based Pharmacokinetic (PBPK Approach for a Physiologically Realistic Characterization of Interindividual Variability in Clinically Relevant Populations.

Directory of Open Access Journals (Sweden)

Markus Krauss

Full Text Available Interindividual variability in anatomical and physiological properties results in significant differences in drug pharmacokinetics. The consideration of such pharmacokinetic variability supports optimal drug efficacy and safety for each single individual, e.g. by identification of individual-specific dosings. One clear objective in clinical drug development is therefore a thorough characterization of the physiological sources of interindividual variability. In this work, we present a Bayesian population physiologically-based pharmacokinetic (PBPK approach for the mechanistically and physiologically realistic identification of interindividual variability. The consideration of a generic and highly detailed mechanistic PBPK model structure enables the integration of large amounts of prior physiological knowledge, which is then updated with new experimental data in a Bayesian framework. A covariate model integrates known relationships of physiological parameters to age, gender and body height. We further provide a framework for estimation of the a posteriori parameter dependency structure at the population level. The approach is demonstrated considering a cohort of healthy individuals and theophylline as an application example. The variability and co-variability of physiological parameters are specified within the population; respectively. Significant correlations are identified between population parameters and are applied for individual- and population-specific visual predictive checks of the pharmacokinetic behavior, which leads to improved results compared to present population approaches. In the future, the integration of a generic PBPK model into an hierarchical approach allows for extrapolations to other populations or drugs, while the Bayesian paradigm allows for an iterative application of the approach and thereby a continuous updating of physiological knowledge with new data. This will facilitate decision making e.g. from preclinical to

Lifetime Improvement of Organic Light Emitting Diodes using LiF Thin Film and UV Glue Encapsulation

Science.gov (United States)

Huang, Jian-Ji; Su, Yan-Kuin; Chang, Ming-Hua; Hsieh, Tsung-Eong; Huang, Bohr-Ran; Wang, Shun-Hsi; Chen, Wen-Ray; Tsai, Yu-Sheng; Hsieh, Huai-En; Liu, Mark O.; Juang, Fuh-Shyang

2008-07-01

This work demonstrates the use of lithium fluoride (LiF) as a passivation layer and a newly developed UV glue for encapsulation on the LiF passivation layer to enhance the stability of organic light-emitting devices (OLEDs). Devices with double protective layers showed a 25-fold increase in operational lifetime compared to those without any packaging layers. LiF has a low melting point and insulating characteristics and it can be adapted as both a protective layer and pre-encapsulation film. The newly developed UV glue has a fast curing time of only 6 s and can be directly spin-coated onto the surface of the LiF passivation layer. The LiF thin film plus spin-coated UV glue is a simple packaging method that reduces the fabrication costs of OLEDs.

Clinical experience of intrapleural administration of fibrin glue for secondary pneumothorax with advanced lung cancer

International Nuclear Information System (INIS)

Nishino, Takeshi; Takizawa, Hiromitsu; Yoshida, Mitsuteru; Kawakami, Yukikiyo; Sakiyama, Shoji; Kondo, Kazuya

2014-01-01

Secondary pneumothorax with advanced lung cancer is an intractable and serious pathosis, which directly aggravates patients' Quality of Life (QOL) and prognosis. We first select the intrapleural administration of fibrin glue for secondary pneumothorax with advanced lung cancer. From April 2009 to May 2012, we encountered 5 patients who developed secondary pneumothorax during treatment for advanced lung cancer. Their average age was 60.8 years old, and 4 of them had squamous cell carcinoma, 1 had adenocarcinoma, and all had unresectable advanced lung cancer. In 4 of them, the point of air leakage could be detected by pleurography, and leakage could be stopped by the intrapleural administration of fibrin glue. All of them could receive chemotherapy or radiotherapy after treatment for secondary pneumothorax. The intrapleural administration of fibrin glue may be an effective and valid treatment for intractable secondary pneumothorax with advanced lung cancer. (author)

A full-spectral Bayesian reconstruction approach based on the material decomposition model applied in dual-energy computed tomography

International Nuclear Information System (INIS)

Cai, C.; Rodet, T.; Mohammad-Djafari, A.; Legoupil, S.

2013-01-01

Purpose: Dual-energy computed tomography (DECT) makes it possible to get two fractions of basis materials without segmentation. One is the soft-tissue equivalent water fraction and the other is the hard-matter equivalent bone fraction. Practical DECT measurements are usually obtained with polychromatic x-ray beams. Existing reconstruction approaches based on linear forward models without counting the beam polychromaticity fail to estimate the correct decomposition fractions and result in beam-hardening artifacts (BHA). The existing BHA correction approaches either need to refer to calibration measurements or suffer from the noise amplification caused by the negative-log preprocessing and the ill-conditioned water and bone separation problem. To overcome these problems, statistical DECT reconstruction approaches based on nonlinear forward models counting the beam polychromaticity show great potential for giving accurate fraction images.Methods: This work proposes a full-spectral Bayesian reconstruction approach which allows the reconstruction of high quality fraction images from ordinary polychromatic measurements. This approach is based on a Gaussian noise model with unknown variance assigned directly to the projections without taking negative-log. Referring to Bayesian inferences, the decomposition fractions and observation variance are estimated by using the joint maximum a posteriori (MAP) estimation method. Subject to an adaptive prior model assigned to the variance, the joint estimation problem is then simplified into a single estimation problem. It transforms the joint MAP estimation problem into a minimization problem with a nonquadratic cost function. To solve it, the use of a monotone conjugate gradient algorithm with suboptimal descent steps is proposed.Results: The performance of the proposed approach is analyzed with both simulated and experimental data. The results show that the proposed Bayesian approach is robust to noise and materials. It is also

Development and comparison in uncertainty assessment based Bayesian modularization method in hydrological modeling

Science.gov (United States)

Li, Lu; Xu, Chong-Yu; Engeland, Kolbjørn

2013-04-01

SummaryWith respect to model calibration, parameter estimation and analysis of uncertainty sources, various regression and probabilistic approaches are used in hydrological modeling. A family of Bayesian methods, which incorporates different sources of information into a single analysis through Bayes' theorem, is widely used for uncertainty assessment. However, none of these approaches can well treat the impact of high flows in hydrological modeling. This study proposes a Bayesian modularization uncertainty assessment approach in which the highest streamflow observations are treated as suspect information that should not influence the inference of the main bulk of the model parameters. This study includes a comprehensive comparison and evaluation of uncertainty assessments by our new Bayesian modularization method and standard Bayesian methods using the Metropolis-Hastings (MH) algorithm with the daily hydrological model WASMOD. Three likelihood functions were used in combination with standard Bayesian method: the AR(1) plus Normal model independent of time (Model 1), the AR(1) plus Normal model dependent on time (Model 2) and the AR(1) plus Multi-normal model (Model 3). The results reveal that the Bayesian modularization method provides the most accurate streamflow estimates measured by the Nash-Sutcliffe efficiency and provide the best in uncertainty estimates for low, medium and entire flows compared to standard Bayesian methods. The study thus provides a new approach for reducing the impact of high flows on the discharge uncertainty assessment of hydrological models via Bayesian method.

A default Bayesian hypothesis test for mediation.

Science.gov (United States)

Nuijten, Michèle B; Wetzels, Ruud; Matzke, Dora; Dolan, Conor V; Wagenmakers, Eric-Jan

2015-03-01

In order to quantify the relationship between multiple variables, researchers often carry out a mediation analysis. In such an analysis, a mediator (e.g., knowledge of a healthy diet) transmits the effect from an independent variable (e.g., classroom instruction on a healthy diet) to a dependent variable (e.g., consumption of fruits and vegetables). Almost all mediation analyses in psychology use frequentist estimation and hypothesis-testing techniques. A recent exception is Yuan and MacKinnon (Psychological Methods, 14, 301-322, 2009), who outlined a Bayesian parameter estimation procedure for mediation analysis. Here we complete the Bayesian alternative to frequentist mediation analysis by specifying a default Bayesian hypothesis test based on the Jeffreys-Zellner-Siow approach. We further extend this default Bayesian test by allowing a comparison to directional or one-sided alternatives, using Markov chain Monte Carlo techniques implemented in JAGS. All Bayesian tests are implemented in the R package BayesMed (Nuijten, Wetzels, Matzke, Dolan, & Wagenmakers, 2014).

Glue-Sniffing: A Comparison Study of Sniffers and Non-Sniffers.

Science.gov (United States)

Jansen, P.; And Others

1992-01-01

Compared 22 glue sniffers and 22 nonsniffers from group of street children and adolescents living in supervised shelters. Found no statistically significant differences between groups on cognitive measures or biographical features. Shelter staff rated sniffers as significantly more disturbed in their relationships with others, although…

Bayesian Fundamentalism or Enlightenment? On the explanatory status and theoretical contributions of Bayesian models of cognition.

Science.gov (United States)

Jones, Matt; Love, Bradley C

2011-08-01

The prominence of Bayesian modeling of cognition has increased recently largely because of mathematical advances in specifying and deriving predictions from complex probabilistic models. Much of this research aims to demonstrate that cognitive behavior can be explained from rational principles alone, without recourse to psychological or neurological processes and representations. We note commonalities between this rational approach and other movements in psychology - namely, Behaviorism and evolutionary psychology - that set aside mechanistic explanations or make use of optimality assumptions. Through these comparisons, we identify a number of challenges that limit the rational program's potential contribution to psychological theory. Specifically, rational Bayesian models are significantly unconstrained, both because they are uninformed by a wide range of process-level data and because their assumptions about the environment are generally not grounded in empirical measurement. The psychological implications of most Bayesian models are also unclear. Bayesian inference itself is conceptually trivial, but strong assumptions are often embedded in the hypothesis sets and the approximation algorithms used to derive model predictions, without a clear delineation between psychological commitments and implementational details. Comparing multiple Bayesian models of the same task is rare, as is the realization that many Bayesian models recapitulate existing (mechanistic level) theories. Despite the expressive power of current Bayesian models, we argue they must be developed in conjunction with mechanistic considerations to offer substantive explanations of cognition. We lay out several means for such an integration, which take into account the representations on which Bayesian inference operates, as well as the algorithms and heuristics that carry it out. We argue this unification will better facilitate lasting contributions to psychological theory, avoiding the pitfalls

Mussel glue protein has an open conformation.

Science.gov (United States)

Williams, T; Marumo, K; Waite, J H; Henkens, R W

1989-03-01

Both native glue protein from marine mussels and a synthetic nonhydroxylated analog were analyzed by far-uv CD under a variety of conditions. Analysis of the CD spectra using various models strongly suggest a primarily random coil structure for both forms of the protein, a fact also supported by the absence of spectral change for the glue protein upon dilution into 6 M guanidine hydrochloride. The nonhydroxylated analog, which consists of 20 repeats of the peptide sequence Ala-Lys-Pro-Ser-Tyr-Pro-Pro-Thr-Tyr-Lys, was further characterized by enzyme modification using mushroom tyrosinase. Enzymatic hydroxylation of tyrosines was found to be best fit by a model containing two rate constants, 5.6 (+/- 0.6) X 10(-3) and 7.2 (+/- 0.3) X 10(-2) min-1. At equilibrium, HPLC analysis of digests showed nearly 100% conversion of Tyr-9 and only 15 to 35% conversion of Tyr-5. The Chou and Fasman rules for predicting structure were applied to the repeat sequence listed above. The rules predict the absence of alpha helix and beta pleated sheets in the structure of this peptide. On the other hand, beta turns are predicted to be present with Tyr-5 being in the region of highest probability. These data suggest that the protein in solution has only a small amount of secondary structure.

Evaluating a Bayesian approach to improve accuracy of individual photographic identification methods using ecological distribution data

Directory of Open Access Journals (Sweden)

Richard Stafford

2011-04-01

Full Text Available Photographic identification of individual organisms can be possible from natural body markings. Data from photo-ID can be used to estimate important ecological and conservation metrics such as population sizes, home ranges or territories. However, poor quality photographs or less well-studied individuals can result in a non-unique ID, potentially confounding several similar looking individuals. Here we present a Bayesian approach that uses known data about previous sightings of individuals at specific sites as priors to help assess the problems of obtaining a non-unique ID. Using a simulation of individuals with different confidence of correct ID we evaluate the accuracy of Bayesian modified (posterior probabilities. However, in most cases, the accuracy of identification decreases. Although this technique is unsuccessful, it does demonstrate the importance of computer simulations in testing such hypotheses in ecology.

Bayesian theory and applications

CERN Document Server

Dellaportas, Petros; Polson, Nicholas G; Stephens, David A

2013-01-01

The development of hierarchical models and Markov chain Monte Carlo (MCMC) techniques forms one of the most profound advances in Bayesian analysis since the 1970s and provides the basis for advances in virtually all areas of applied and theoretical Bayesian statistics. This volume guides the reader along a statistical journey that begins with the basic structure of Bayesian theory, and then provides details on most of the past and present advances in this field. The book has a unique format. There is an explanatory chapter devoted to each conceptual advance followed by journal-style chapters that provide applications or further advances on the concept. Thus, the volume is both a textbook and a compendium of papers covering a vast range of topics. It is appropriate for a well-informed novice interested in understanding the basic approach, methods and recent applications. Because of its advanced chapters and recent work, it is also appropriate for a more mature reader interested in recent applications and devel...

Risk prediction model for knee pain in the Nottingham community: a Bayesian modelling approach.

Science.gov (United States)

Fernandes, G S; Bhattacharya, A; McWilliams, D F; Ingham, S L; Doherty, M; Zhang, W

2017-03-20

Twenty-five percent of the British population over the age of 50 years experiences knee pain. Knee pain can limit physical ability and cause distress and bears significant socioeconomic costs. The objectives of this study were to develop and validate the first risk prediction model for incident knee pain in the Nottingham community and validate this internally within the Nottingham cohort and externally within the Osteoarthritis Initiative (OAI) cohort. A total of 1822 participants from the Nottingham community who were at risk for knee pain were followed for 12 years. Of this cohort, two-thirds (n = 1203) were used to develop the risk prediction model, and one-third (n = 619) were used to validate the model. Incident knee pain was defined as pain on most days for at least 1 month in the past 12 months. Predictors were age, sex, body mass index, pain elsewhere, prior knee injury and knee alignment. A Bayesian logistic regression model was used to determine the probability of an OR >1. The Hosmer-Lemeshow χ 2 statistic (HLS) was used for calibration, and ROC curve analysis was used for discrimination. The OAI cohort from the United States was also used to examine the performance of the model. A risk prediction model for knee pain incidence was developed using a Bayesian approach. The model had good calibration, with an HLS of 7.17 (p = 0.52) and moderate discriminative ability (ROC 0.70) in the community. Individual scenarios are given using the model. However, the model had poor calibration (HLS 5866.28, p prediction model for knee pain, regardless of underlying structural changes of knee osteoarthritis, in the community using a Bayesian modelling approach. The model appears to work well in a community-based population but not in individuals with a higher risk for knee osteoarthritis, and it may provide a convenient tool for use in primary care to predict the risk of knee pain in the general population.

Endoscopic ultrasound-guided coil or glue injection in post-cyanoacrylate gastric variceal re-bleed.

Science.gov (United States)

Mukkada, Roy J; Antony, Rajesh; Chooracken, Mathew J; Francis, Jose V; Chettupuzha, Antony P; Mathew, Pradeep G; Augustine, Philip; Koshy, Abraham

2018-04-09

N-butyl-cyanoacrylate injection is recommended in bleeding/recently bled gastric varices. However, cyanoacrylate injection is associated with re-bleed in 25% to 50% of patients. Endoscopic ultrasound (EUS)-guided coil application is an emerging treatment modality for bleeding gastric varices. The aim of this study was to compare EUS-guided coil application combined with or without cyanoacrylate glue injection to injection alone in post-glue gastric variceal re-bleed. A retrospective analysis of a prospectively maintained database was performed. Thirty patients who re-bled after cyanoacrylate injection and who had EUS-guided coil application to gastric varices were included. The comparison was done with data of 51 patients who had only repeat cyanoacrylate injection. Both groups had a follow up for 12 months. EUS-guided coil application was done under endosonographic guidance. A single coil was placed in 7, two coils in each of 13 patients, three in 5, four in 3, five in one, and 6 coils in one patient. In addition, cyanoacrylate glue injection was given in 15 patients. Eight patients had repeat EUS-guided coil application 1 month later. Re-bleed and mortality were assessed. Coilng: Six out of 30 (20%) patients re-bled during follow up of 9 to 365 days. Three out of 30 (10%) died. One patient died 9 days after the procedure due to acute respiratory distress syndrome, one died 4 months after the procedure due to a re-bleed and one 5 months after the procedure due to spontaneous bacterial peritonitis. Glue only: 26/51 (51%) re-bled during follow up of 45 to 365 days. EUS-guided coil application resulted in significantly less re-bleed than glue-only (Kaplan-Meir survival analysis with log-rank test, z = 5.4, p guided coil application with/without cyanoacrylate injection for the obliteration of gastric varices is effective for post-cyanoacrylate gastric variceal re-bleed.

National evaluation of Chinese coastal erosion to sea level rise using a Bayesian approach

International Nuclear Information System (INIS)

Zhan, Q; Fan, X; Du, X; Zhu, J

2014-01-01

In this paper a Causal Bayesian network is developed to predict decadal-scale shoreline evolution of China to sea-level rise. The Bayesian model defines relationships between 6 factors of Chinese coastal system such as coastal geomorphology, mean tide range, mean wave height, coastal slope, relative sea-level rise rate and shoreline erosion rate. Using the Bayesian probabilistic model, we make quantitative assessment of china's shoreline evolution in response to different future sea level rise rates. Results indicate that the probability of coastal erosion with high and very high rates increases from 28% to 32.3% when relative sea-level rise rates is 4∼6mm/a, and to 44.9% when relative sea-level rise rates is more than 6mm/a. A hindcast evaluation of the Bayesian model shows that the model correctly predicts 79.3% of the cases. Model test indicates that the Bayesian model shows higher predictive capabilities for stable coasts and very highly eroding coasts than moderately and highly eroding coasts. This study demonstrates that the Bayesian model is adapted to predicting decadal-scale Chinese coastal erosion associated with sea-level rise

Bayesian outcome-based strategy classification.

Science.gov (United States)

Lee, Michael D

2016-03-01

Hilbig and Moshagen (Psychonomic Bulletin & Review, 21, 1431-1443, 2014) recently developed a method for making inferences about the decision processes people use in multi-attribute forced choice tasks. Their paper makes a number of worthwhile theoretical and methodological contributions. Theoretically, they provide an insightful psychological motivation for a probabilistic extension of the widely-used "weighted additive" (WADD) model, and show how this model, as well as other important models like "take-the-best" (TTB), can and should be expressed in terms of meaningful priors. Methodologically, they develop an inference approach based on the Minimum Description Length (MDL) principles that balances both the goodness-of-fit and complexity of the decision models they consider. This paper aims to preserve these useful contributions, but provide a complementary Bayesian approach with some theoretical and methodological advantages. We develop a simple graphical model, implemented in JAGS, that allows for fully Bayesian inferences about which models people use to make decisions. To demonstrate the Bayesian approach, we apply it to the models and data considered by Hilbig and Moshagen (Psychonomic Bulletin & Review, 21, 1431-1443, 2014), showing how a prior predictive analysis of the models, and posterior inferences about which models people use and the parameter settings at which they use them, can contribute to our understanding of human decision making.

Doing bayesian data analysis a tutorial with R and BUGS

CERN Document Server

Kruschke, John K

2011-01-01

There is an explosion of interest in Bayesian statistics, primarily because recently created computational methods have finally made Bayesian analysis obtainable to a wide audience. Doing Bayesian Data Analysis, A Tutorial Introduction with R and BUGS provides an accessible approach to Bayesian data analysis, as material is explained clearly with concrete examples. The book begins with the basics, including essential concepts of probability and random sampling, and gradually progresses to advanced hierarchical modeling methods for realistic data. The text delivers comprehensive coverage of all

Estimating the occurrence of foreign material in Advanced Gas-cooled Reactors: A Bayesian Monte Carlo approach

International Nuclear Information System (INIS)

Mason, Paolo

2014-01-01

Highlights: • The amount of a specific type of foreign material found in UK AGRs has been estimated. • The estimate is based on very few instances of detection in numerous inspections. • A Bayesian Monte Carlo approach was used. • The study supports safety case claims on coolant flow impairment. • The methodology is applicable to any inspection campaign on any plant system. - Abstract: The current occurrence of a particular sort of foreign material in eight UK Advanced Gas-cooled Reactors has been estimated by means of a parametric approach. The study includes both variability, treated in analytic fashion via the combination of standard probability distributions, and the uncertainty in the parameters of the model of choice, whose posterior distribution was inferred in Bayesian fashion by means of a Monte Carlo route consisting in the conditional acceptance of sets of model parameters drawn from a prior distribution based on engineering judgement. The model underlying the present study specifically refers to the re-loading and inspection routines of UK Advanced Gas-cooled Reactors. The approach to inference here presented, however, is of general validity and can be applied to the outcome of any inspection campaign on any plant system, and indeed to any situation in which the outcome of a stochastic process is more easily simulated than described by a probability density or mass function

Gravity: The Glue of the Universe. History and Activities.

Science.gov (United States)

Gilbert, Harry; Smith, Diana Gilbert

This book presents a story of the history of gravity, the glue of the universe, and is based on two premises: (1) an understanding of mathematics is not required to grasp the concepts and implications of relativity; and (2) relativity has altered forever the perceptions of gravity, space, time, and how the universe works. A narrative text section…

A Bayesian Developmental Approach to Robotic Goal-Based Imitation Learning.

Directory of Open Access Journals (Sweden)

Michael Jae-Yoon Chung

Full Text Available A fundamental challenge in robotics today is building robots that can learn new skills by observing humans and imitating human actions. We propose a new Bayesian approach to robotic learning by imitation inspired by the developmental hypothesis that children use self-experience to bootstrap the process of intention recognition and goal-based imitation. Our approach allows an autonomous agent to: (i learn probabilistic models of actions through self-discovery and experience, (ii utilize these learned models for inferring the goals of human actions, and (iii perform goal-based imitation for robotic learning and human-robot collaboration. Such an approach allows a robot to leverage its increasing repertoire of learned behaviors to interpret increasingly complex human actions and use the inferred goals for imitation, even when the robot has very different actuators from humans. We demonstrate our approach using two different scenarios: (i a simulated robot that learns human-like gaze following behavior, and (ii a robot that learns to imitate human actions in a tabletop organization task. In both cases, the agent learns a probabilistic model of its own actions, and uses this model for goal inference and goal-based imitation. We also show that the robotic agent can use its probabilistic model to seek human assistance when it recognizes that its inferred actions are too uncertain, risky, or impossible to perform, thereby opening the door to human-robot collaboration.

Hypokalemic muscular paralysis causing acute respiratory failure due to rhabdomyolysis with renal tubular acidosis in a chronic glue sniffer.

Science.gov (United States)

Kao, K C; Tsai, Y H; Lin, M C; Huang, C C; Tsao, C Y; Chen, Y C

2000-01-01

A 34-year-old male was admitted to the emergency department with the development of quadriparesis and respiratory failure due to hypokalemia after prolonged glue sniffing. The patient was subsequently given mechanical ventilatory support for respiratory failure. He was weaned from the ventilator 4 days later after potassium replacement. Toluene is an aromatic hydrocarbon found in glues, cements, and solvents. It is known to be toxic to the nervous system, hematopoietic system, and causes acid-base and electrolyte disorders. Acute respiratory failure with hypokalemia and rhabdomyolysis with acute renal failure should be considered as potential events in a protracted glue sniffing.

Staple line reinforcement with fleece-coated fibrin glue (TachoComb) after thoracoscopic bullectomy for the treatment of spontaneous pneumothorax

International Nuclear Information System (INIS)

Muramatsu, Takashi; Ohmori, Kazumitsu; Shimamura, Mie; Furuichi, Motohiko; Takeshita, Shinji; Negishi, Nanao

2007-01-01

We investigated the cause of pneumothorax recurrence after thoracoscopic surgery and the effectiveness of staple line reinforcement with fleece-coated fibrin glue (TachoComb) in the prevention of postoperative pneumothorax recurrence. From April 3, 1992 to the end of December 2005, thoracoscopic bullectomy was performed on 499 patients of primary spontaneous pneumothorax. The causes of recurrence were investigated on 39 patients on the basis of surgical observations, preoperative chest computed tomography, and so on. The most common cause was new bulla formation (37 cases), 19 of which were apparently related to the staple line (within 1 cm of the staple lines) and 15 of which were not related to the staple line. After 2000, we stopped using forceps to grasp lungs and we have reinforced the staple line by applying fleece-coated fibrin glue. The staple line reinforced with fleece-coated fibrin glue, or sprayed with fibrin glue solution and the untreated group (bullectomy only with staples) were compared, and the recurrence rates were 1.22%, 7.25%, and 10.00%, respectively (P=0.0006021). The recurrence rate after thoracoscopic bullectomy with fleece-coated fibrin glue was significantly lowered and we consider this procedure to be the treatment of choice for the management of spontaneous pneumothorax. (author)

Is there need for baryons with constituent glue

International Nuclear Information System (INIS)

Meissner, U.G.

1983-01-01

We investigate the breathing-mode spectrum of the Λ-particle in the framework of a general bag model including confinement via surface tension and volume energy. We show that the experimental states Λ 1/2 (1600) and Λ 1/2 (1800) can be described as radial surface excitations of the Λ. We further comment on a recent paper describing these Λ-excitations as baryons with constituent glue. (orig.)

Structure-based bayesian sparse reconstruction

KAUST Repository

Quadeer, Ahmed Abdul; Al-Naffouri, Tareq Y.

2012-01-01

Sparse signal reconstruction algorithms have attracted research attention due to their wide applications in various fields. In this paper, we present a simple Bayesian approach that utilizes the sparsity constraint and a priori statistical

Evaluating Vegetation Potential for Wildfire Impacted Watershed Using a Bayesian Network Modeling Approach

Science.gov (United States)

Jaramillo, L. V.; Stone, M. C.; Morrison, R. R.

2017-12-01

Decision-making for natural resource management is complex especially for fire impacted watersheds in the Southwestern US because of the vital importance of water resources, exorbitant cost of fire management and restoration, and the risks of the wildland-urban interface (WUI). While riparian and terrestrial vegetation are extremely important to ecosystem health and provide ecosystem services, loss of vegetation due to wildfire, post-fire flooding, and debris flows can lead to further degradation of the watershed and increased vulnerability to erosion and debris flow. Land managers are charged with taking measures to mitigate degradation of the watershed effectively and efficiently with limited time, money, and data. For our study, a Bayesian network (BN) approach is implemented to understand vegetation potential for Kashe-Katuwe Tent Rocks National Monument in the fire-impacted Peralta Canyon Watershed, New Mexico, USA. We implement both two-dimensional hydrodynamic and Bayesian network modeling to incorporate spatial variability in the system. Our coupled modeling framework presents vegetation recruitment and succession potential for three representative plant types (native riparian, native terrestrial, and non-native) under several hydrologic scenarios and management actions. In our BN model, we use variables that address timing, hydrologic, and groundwater conditions as well as recruitment and succession constraints for the plant types based on expert knowledge and literature. Our approach allows us to utilize small and incomplete data, incorporate expert knowledge, and explicitly account for uncertainty in the system. Our findings can be used to help land managers and local decision-makers determine their plan of action to increase watershed health and resilience.

Using the DP-190 glue for adhesive attachment of a large space mirror and its rim

Science.gov (United States)

Vlasenko, Oleg; Zverev, Alexey; Sachkov, Mikhail

2014-07-01

The glue DP-190 is widely used for adhesive attachment of astrositall (zerodur) lightweight large-size space astronomical mirrors (diameter of 1.7 m and more) with elements of their frames of invar. Peculiarities of physicalmechanical behavior of the glue DP-190 when exposed to the environment during the ground operation and in orbit cause instability of the reflective surface quality of mirrors. In this report we show that even a small (around 1%-5%) volumetric deformation of a cylindrical adhesive layer with a thickness of 0.8 mm between the mirror and the rim element causes significant mirrors deformation. We propose to use adhesive layer of special form that allows to reduce volumetric deformations of the glue DP-190 up to three times. Here we present results based on primary mirror tests of the WSO-UV project.

[The influence of biological compatibility of the cyanoacrylate glue on regeneration of the cartilaginous tissue].

Science.gov (United States)

Semenov, F V; Skibitskaya, N F

The objective of the present study was to evaluate the possibility of the application of the cyanoacrylate-based glue for the strengthening of the reconstructed elements of the middle ear and its influence on the regeneration of the cartilaginous tissue. We used the cartilaginous tissue from the auricles of the male California rabbits as a model. The cartilage was destroyed in a standard press. Half of the cartilage thus fragmented was implanted into the left auricle. The remaining part was mixed up with the cyanoacrylate glue and implanted into the right auricle of the same animal. The implanted material was used for the morphological study on day 10, within 1 and 2 months after the beginning of the experiment. The results of the study confirm the absence of the toxic action of the biologically compatible cyanoacrylate-based glue on the regeneration of the cartilaginous and connective tissues which suggests the possibility of its application for the surgical treatment of the diseases of the middle ear.

A Bayesian inverse modeling approach to estimate soil hydraulic properties of a toposequence in southeastern Amazonia.

Science.gov (United States)

Stucchi Boschi, Raquel; Qin, Mingming; Gimenez, Daniel; Cooper, Miguel

2016-04-01

Modeling is an important tool for better understanding and assessing land use impacts on landscape processes. A key point for environmental modeling is the knowledge of soil hydraulic properties. However, direct determination of soil hydraulic properties is difficult and costly, particularly in vast and remote regions such as one constituting the Amazon Biome. One way to overcome this problem is to extrapolate accurately estimated data to pedologically similar sites. The van Genuchten (VG) parametric equation is the most commonly used for modeling SWRC. The use of a Bayesian approach in combination with the Markov chain Monte Carlo to estimate the VG parameters has several advantages compared to the widely used global optimization techniques. The Bayesian approach provides posterior distributions of parameters that are independent from the initial values and allow for uncertainty analyses. The main objectives of this study were: i) to estimate hydraulic parameters from data of pasture and forest sites by the Bayesian inverse modeling approach; and ii) to investigate the extrapolation of the estimated VG parameters to a nearby toposequence with pedologically similar soils to those used for its estimate. The parameters were estimated from volumetric water content and tension observations obtained after rainfall events during a 207-day period from pasture and forest sites located in the southeastern Amazon region. These data were used to run HYDRUS-1D under a Differential Evolution Adaptive Metropolis (DREAM) scheme 10,000 times, and only the last 2,500 times were used to calculate the posterior distributions of each hydraulic parameter along with 95% confidence intervals (CI) of volumetric water content and tension time series. Then, the posterior distributions were used to generate hydraulic parameters for two nearby toposequences composed by six soil profiles, three are under forest and three are under pasture. The parameters of the nearby site were accepted when

Determining characteristic principal clusters in the “cluster-plus-glue-atom” model

International Nuclear Information System (INIS)

Du, Jinglian; Wen, Bin; 2NeT Lab, Wilfrid Laurier University, Waterloo, 75 University Ave West, Ontario N2L 3C5 (Canada))" data-affiliation=" (M2NeT Lab, Wilfrid Laurier University, Waterloo, 75 University Ave West, Ontario N2L 3C5 (Canada))" >Melnik, Roderick; Kawazoe, Yoshiyuki

2014-01-01

The “cluster-plus-glue-atom” model can easily describe the structure of complex metallic alloy phases. However, the biggest obstacle limiting the application of this model is that it is difficult to determine the characteristic principal cluster. In the case when interatomic force constants (IFCs) inside the cluster lead to stronger interaction than the interaction between the clusters, a new rule for determining the characteristic principal cluster in the “cluster-plus-glue-atom” model has been proposed on the basis of IFCs. To verify this new rule, the alloy phases in Cu–Zr and Al–Ni–Zr systems have been tested, and our results indicate that the present new rule for determining characteristic principal clusters is effective and reliable

A Bayesian Approach to Model Selection in Hierarchical Mixtures-of-Experts Architectures.

Science.gov (United States)

Tanner, Martin A.; Peng, Fengchun; Jacobs, Robert A.

1997-03-01

There does not exist a statistical model that shows good performance on all tasks. Consequently, the model selection problem is unavoidable; investigators must decide which model is best at summarizing the data for each task of interest. This article presents an approach to the model selection problem in hierarchical mixtures-of-experts architectures. These architectures combine aspects of generalized linear models with those of finite mixture models in order to perform tasks via a recursive "divide-and-conquer" strategy. Markov chain Monte Carlo methodology is used to estimate the distribution of the architectures' parameters. One part of our approach to model selection attempts to estimate the worth of each component of an architecture so that relatively unused components can be pruned from the architecture's structure. A second part of this approach uses a Bayesian hypothesis testing procedure in order to differentiate inputs that carry useful information from nuisance inputs. Simulation results suggest that the approach presented here adheres to the dictum of Occam's razor; simple architectures that are adequate for summarizing the data are favored over more complex structures. Copyright 1997 Elsevier Science Ltd. All Rights Reserved.

Clinical evaluations of complete autologous fibrin glue, produced by the CryoSeal® FS system, and polyglycolic acid sheets as wound coverings after oral surgery.

Science.gov (United States)

Kouketsu, Atsumu; Nogami, Shinnosuke; Yamada-Fujiwara, Minami; Nagai, Hirokazu; Yamauchi, Kensuke; Mori, Shiro; Miyashita, Hitoshi; Kawai, Tadashi; Matsui, Aritsune; Kataoka, Yoshihiro; Satomi, Norihisa; Ezoe, Yushi; Abe, Satoko; Takeda, Yuri; Tone, Takeshi; Hirayama, Bunnichi; Kurobane, Tsuyoshi; Tashiro, Kazuki; Yanagisawa, Yuta; Takahashi, Tetsu

2017-09-01

The CryoSeal ® FS System has been recently introduced as an automated device for the production of complete fibrin glue from autologous plasma, rather than from pool allogenic or cattle blood, to prevent viral infection and allergic reaction. We evaluated the effectiveness of complete autologous fibrin glue and polyglycolic acid (PGA) sheet wound coverings in mucosa defect oral surgery. Postoperative pain, scar contracture, ingestion, tongue dyskinesia, and postoperative bleeding were evaluated in 12 patients who underwent oral (including the tongue) mucosa excision, and received a PGA sheet and an autologous fibrin glue covering. They were compared with 12 patients who received a PGA sheet and commercial allogenic fibrin glue. All cases in the complete autologous fibrin glue group demonstrated good wound healing without complications such as local infection or incomplete cure. All evaluated clinical measures in this group were similar or superior to the commercial allogenic fibrin glue group. Coagulation and adhesion quality achieved with this method was comparable to that with a PGA sheet and commercial fibrin glue. Covering oral surgery wounds with complete autologous fibrin glue produced by an automated device was convenient, safe, and reduced the risk of viral infection and allergic reaction associated with conventional techniques. Copyright © 2017 European Association for Cranio-Maxillo-Facial Surgery. Published by Elsevier Ltd. All rights reserved.

Comparison of Bayesian and frequentist approaches in modelling risk of preterm birth near the Sydney Tar Ponds, Nova Scotia, Canada

Directory of Open Access Journals (Sweden)

Canty Angelo

2007-09-01

Full Text Available Abstract Background This study compares the Bayesian and frequentist (non-Bayesian approaches in the modelling of the association between the risk of preterm birth and maternal proximity to hazardous waste and pollution from the Sydney Tar Pond site in Nova Scotia, Canada. Methods The data includes 1604 observed cases of preterm birth out of a total population of 17559 at risk of preterm birth from 144 enumeration districts in the Cape Breton Regional Municipality. Other covariates include the distance from the Tar Pond; the rate of unemployment to population; the proportion of persons who are separated, divorced or widowed; the proportion of persons who have no high school diploma; the proportion of persons living alone; the proportion of single parent families and average income. Bayesian hierarchical Poisson regression, quasi-likelihood Poisson regression and weighted linear regression models were fitted to the data. Results The results of the analyses were compared together with their limitations. Conclusion The results of the weighted linear regression and the quasi-likelihood Poisson regression agrees with the result from the Bayesian hierarchical modelling which incorporates the spatial effects.

BELM: Bayesian extreme learning machine.

Science.gov (United States)

Soria-Olivas, Emilio; Gómez-Sanchis, Juan; Martín, José D; Vila-Francés, Joan; Martínez, Marcelino; Magdalena, José R; Serrano, Antonio J

2011-03-01

The theory of extreme learning machine (ELM) has become very popular on the last few years. ELM is a new approach for learning the parameters of the hidden layers of a multilayer neural network (as the multilayer perceptron or the radial basis function neural network). Its main advantage is the lower computational cost, which is especially relevant when dealing with many patterns defined in a high-dimensional space. This brief proposes a bayesian approach to ELM, which presents some advantages over other approaches: it allows the introduction of a priori knowledge; obtains the confidence intervals (CIs) without the need of applying methods that are computationally intensive, e.g., bootstrap; and presents high generalization capabilities. Bayesian ELM is benchmarked against classical ELM in several artificial and real datasets that are widely used for the evaluation of machine learning algorithms. Achieved results show that the proposed approach produces a competitive accuracy with some additional advantages, namely, automatic production of CIs, reduction of probability of model overfitting, and use of a priori knowledge.

Bayesian statistical inference

Directory of Open Access Journals (Sweden)

Bruno De Finetti

2017-04-01

Full Text Available This work was translated into English and published in the volume: Bruno De Finetti, Induction and Probability, Biblioteca di Statistica, eds. P. Monari, D. Cocchi, Clueb, Bologna, 1993.Bayesian statistical Inference is one of the last fundamental philosophical papers in which we can find the essential De Finetti's approach to the statistical inference.

Bayesian Exponential Smoothing.

OpenAIRE

Forbes, C.S.; Snyder, R.D.; Shami, R.S.

2000-01-01

In this paper, a Bayesian version of the exponential smoothing method of forecasting is proposed. The approach is based on a state space model containing only a single source of error for each time interval. This model allows us to improve current practices surrounding exponential smoothing by providing both point predictions and measures of the uncertainty surrounding them.

Dynamic Bayesian Network Modeling of Game Based Diagnostic Assessments. CRESST Report 837

Science.gov (United States)

Levy, Roy

2014-01-01

Digital games offer an appealing environment for assessing student proficiencies, including skills and misconceptions in a diagnostic setting. This paper proposes a dynamic Bayesian network modeling approach for observations of student performance from an educational video game. A Bayesian approach to model construction, calibration, and use in…

Bayesian models: A statistical primer for ecologists

Science.gov (United States)

Hobbs, N. Thompson; Hooten, Mevin B.

2015-01-01

Bayesian modeling has become an indispensable tool for ecological research because it is uniquely suited to deal with complexity in a statistically coherent way. This textbook provides a comprehensive and accessible introduction to the latest Bayesian methods—in language ecologists can understand. Unlike other books on the subject, this one emphasizes the principles behind the computations, giving ecologists a big-picture understanding of how to implement this powerful statistical approach.Bayesian Models is an essential primer for non-statisticians. It begins with a definition of probability and develops a step-by-step sequence of connected ideas, including basic distribution theory, network diagrams, hierarchical models, Markov chain Monte Carlo, and inference from single and multiple models. This unique book places less emphasis on computer coding, favoring instead a concise presentation of the mathematical statistics needed to understand how and why Bayesian analysis works. It also explains how to write out properly formulated hierarchical Bayesian models and use them in computing, research papers, and proposals.This primer enables ecologists to understand the statistical principles behind Bayesian modeling and apply them to research, teaching, policy, and management.Presents the mathematical and statistical foundations of Bayesian modeling in language accessible to non-statisticiansCovers basic distribution theory, network diagrams, hierarchical models, Markov chain Monte Carlo, and moreDeemphasizes computer coding in favor of basic principlesExplains how to write out properly factored statistical expressions representing Bayesian models

Mesh fixation with glue versus suture for chronic pain and recurrence in Lichtenstein inguinal hernioplasty.

Science.gov (United States)

Sun, Ping; Cheng, Xiang; Deng, Shichang; Hu, Qinggang; Sun, Yi; Zheng, Qichang

2017-02-07

Chronic pain following mesh-based inguinal hernia repair is frequently reported, and has a significant impact on quality of life. Whether mesh fixation with glue can reduce chronic pain without increasing the recurrence rate is still controversial. To determine whether tissue adhesives can reduce postoperative complications, especially chronic pain, with no increase in recurrence rate, compared with sutures for mesh fixation in Lichtenstein hernia repair. We searched the following electronic databases with no language restrictions: the Cochrane Central Register of Controlled Trials (CENTRAL; issue 4, 2016) in the Cochrane Library (searched 11 May 2016), MEDLINE Ovid (1986 to 11 May 2016), Embase Ovid (1986 to 11 May 2016), Science Citation Index (Web of Science) (1986 to 11 May 2016), CBM (Chinese Biomedical Database), CNKI (China National Knowledge Infrastructure), VIP (a full-text database in China), Wanfang databases. We also checked reference lists of identified papers (included studies and relevant reviews). We included all randomised and quasi-randomised controlled trials comparing glue versus sutures for mesh fixation in Lichtenstein hernia repair. Cluster-RCTs were also eligible. Two review authors extracted data and assessed the risk of bias independently. Dichotomous outcomes were expressed as odds ratio (OR) with 95% confidence intervals (CI). Continuous outcomes were expressed as mean differences (MD) with 95% CIs. Twelve trials with a total of 1932 participants were included in this review. The overall postoperative chronic pain in the glue group was reduced by 37% (OR 0.63, 95% CI 0.44 to 0.91; 10 studies, 1418 participants, low-quality evidence) compared with the suture group. However, the results changed when we conducted subgroup analysis with regard to the type of mesh. Subgroup analysis of included studies using lightweight mesh showed the reduction of chronic pain was less profound and insignificant (OR 0.77, 95% CI 0.50 to 1.17). Subgroup

Embedding the results of focussed Bayesian fusion into a global context

Science.gov (United States)

Sander, Jennifer; Heizmann, Michael

2014-05-01

Bayesian statistics offers a well-founded and powerful fusion methodology also for the fusion of heterogeneous information sources. However, except in special cases, the needed posterior distribution is not analytically derivable. As consequence, Bayesian fusion may cause unacceptably high computational and storage costs in practice. Local Bayesian fusion approaches aim at reducing the complexity of the Bayesian fusion methodology significantly. This is done by concentrating the actual Bayesian fusion on the potentially most task relevant parts of the domain of the Properties of Interest. Our research on these approaches is motivated by an analogy to criminal investigations where criminalists pursue clues also only locally. This publication follows previous publications on a special local Bayesian fusion technique called focussed Bayesian fusion. Here, the actual calculation of the posterior distribution gets completely restricted to a suitably chosen local context. By this, the global posterior distribution is not completely determined. Strategies for using the results of a focussed Bayesian analysis appropriately are needed. In this publication, we primarily contrast different ways of embedding the results of focussed Bayesian fusion explicitly into a global context. To obtain a unique global posterior distribution, we analyze the application of the Maximum Entropy Principle that has been shown to be successfully applicable in metrology and in different other areas. To address the special need for making further decisions subsequently to the actual fusion task, we further analyze criteria for decision making under partial information.

First Results from The GlueX Experiment

Energy Technology Data Exchange (ETDEWEB)

Meyer, Curtis [Carnegie Mellon Univ., Pittsburgh, PA (United States)

2016-05-01

The GlueX experiment at Jefferson Lab ran with its first commissioning beam in late 2014 and the spring of 2015. Data were collected on both plastic and liquid hydrogen targets, and much of the detector has been commissioned. All of the detector systems are now performing at or near design specifications and events are being fully reconstructed, including exclusive production of pi^0, eta and omega mesons. Linearly-polarized photons were successfully produced through coherent bremsstrahlung and polarization transfer to the ρ has been observed.

Bayesian artificial intelligence

CERN Document Server

Korb, Kevin B

2010-01-01

Updated and expanded, Bayesian Artificial Intelligence, Second Edition provides a practical and accessible introduction to the main concepts, foundation, and applications of Bayesian networks. It focuses on both the causal discovery of networks and Bayesian inference procedures. Adopting a causal interpretation of Bayesian networks, the authors discuss the use of Bayesian networks for causal modeling. They also draw on their own applied research to illustrate various applications of the technology.New to the Second EditionNew chapter on Bayesian network classifiersNew section on object-oriente

Quantitative methods in ethnobotany and ethnopharmacology: considering the overall flora--hypothesis testing for over- and underused plant families with the Bayesian approach.

Science.gov (United States)

Weckerle, Caroline S; Cabras, Stefano; Castellanos, Maria Eugenia; Leonti, Marco

2011-09-01

We introduce and explain the advantages of the Bayesian approach and exemplify the method with an analysis of the medicinal flora of Campania, Italy. The Bayesian approach is a new method, which allows to compare medicinal floras with the overall flora of a given area and to investigate over- and underused plant families. In contrast to previously used methods (regression analysis and binomial method) it considers the inherent uncertainty around the analyzed data. The medicinal flora with 423 species was compiled based on nine studies on local medicinal plant use in Campania. The total flora comprises 2237 species belonging to 128 families. Statistical analysis was performed with the Bayesian method and the binomial method. An approximated χ(2)-test was used to analyze the relationship between use categories and higher taxonomic groups. Among the larger plant families we find the Lamiaceae, Rosaceae, and Malvaceae, to be overused in the local medicine of Campania and the Orchidaceae, Caryophyllaceae, Poaceae, and Fabaceae to be underused compared to the overall flora. Furthermore, do specific medicinal uses tend to be correlated with taxonomic plant groups. For example, are the Monocots heavily used for urological complaints. Testing for over- and underused taxonomic groups of a flora with the Bayesian method is easy to adopt and can readily be calculated in excel spreadsheets using the excel function Inverse beta (INV.BETA). In contrast to the binomial method the presented method is also suitable for small datasets. With larger datasets the two methods tend to converge. However, results are generally more conservative with the Bayesian method pointing out fewer families as over- or underused. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

Effect of Wheat Straw Pretreatments and Glue Formulations on particle board properties

International Nuclear Information System (INIS)

Jabeen, S.; Naveed, S.; Yousaf, S.; Ramzan, N.

2014-01-01

In this paper, the effect of wheat straw (WS) pretreatments and glue formulations on mechanical (i.e. Compressive Strength (CS) and Impact Strength (IS)) and water resistance properties (i.e. Thickness Swelling (TS) and water absorption (WA)) of particle board have been investigated and the results have been compared with conventional wooden particleboard. Wheat straw was treated with steam available at 110 degree C and 20 psig, for the retention time of 5, 10 and 15 min. The solution of 10% HCl was also used for removing the lignin. Particleboard was prepared by bonding treated WS with four types of glue recipes of synthetic and natural binders like urea formaldehyde (UF), polyvinyl acetate (PVA), corn flour (CF) and wheat flour (WF). The particle board was formed at the hydraulic pressure and temperature of 2800 psig and 80 degree C respectively. It was observed that WS particleboard has low mechanical strength and high water resistance in comparison with conventional board. The particle board prepared with HCl cured wheat straw and glue having high urea formaldehyde and corn flour has higher CS and IS as well as low TS and WA. It may be concluded that wheat straw is a good substitute of wood for particle board while using HCl as a modifying chemical and strong binders like urea formaldehyde and corn flour. (author)

Contribution of glue layer into epidermis sample fluorescence dynamics

Science.gov (United States)

Salomatina, Elena V.; Chernova, Svetlana P.; Pravdin, Alexander B.

2000-04-01

In this work, the temporal behavior of autofluorescence of epidermis samples under UV-irradiation has ben studied. The samples were prepared using surface epidermis stripping technique. Fluorescence spectra and kinetic curves of fluorescence intensity have been obtained. It has been concluded that the glue composition used allows the measurement of epidermis fluorescence dynamics with the first 60 min of experiment.

Towards a Fuzzy Bayesian Network Based Approach for Safety Risk Analysis of Tunnel-Induced Pipeline Damage.

Science.gov (United States)

Zhang, Limao; Wu, Xianguo; Qin, Yawei; Skibniewski, Miroslaw J; Liu, Wenli

2016-02-01

Tunneling excavation is bound to produce significant disturbances to surrounding environments, and the tunnel-induced damage to adjacent underground buried pipelines is of considerable importance for geotechnical practice. A fuzzy Bayesian networks (FBNs) based approach for safety risk analysis is developed in this article with detailed step-by-step procedures, consisting of risk mechanism analysis, the FBN model establishment, fuzzification, FBN-based inference, defuzzification, and decision making. In accordance with the failure mechanism analysis, a tunnel-induced pipeline damage model is proposed to reveal the cause-effect relationships between the pipeline damage and its influential variables. In terms of the fuzzification process, an expert confidence indicator is proposed to reveal the reliability of the data when determining the fuzzy probability of occurrence of basic events, with both the judgment ability level and the subjectivity reliability level taken into account. By means of the fuzzy Bayesian inference, the approach proposed in this article is capable of calculating the probability distribution of potential safety risks and identifying the most likely potential causes of accidents under both prior knowledge and given evidence circumstances. A case concerning the safety analysis of underground buried pipelines adjacent to the construction of the Wuhan Yangtze River Tunnel is presented. The results demonstrate the feasibility of the proposed FBN approach and its application potential. The proposed approach can be used as a decision tool to provide support for safety assurance and management in tunnel construction, and thus increase the likelihood of a successful project in a complex project environment. © 2015 Society for Risk Analysis.

Exploiting neurovascular coupling: a Bayesian sequential Monte Carlo approach applied to simulated EEG fNIRS data

Science.gov (United States)

Croce, Pierpaolo; Zappasodi, Filippo; Merla, Arcangelo; Chiarelli, Antonio Maria

2017-08-01

Objective. Electrical and hemodynamic brain activity are linked through the neurovascular coupling process and they can be simultaneously measured through integration of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS). Thanks to the lack of electro-optical interference, the two procedures can be easily combined and, whereas EEG provides electrophysiological information, fNIRS can provide measurements of two hemodynamic variables, such as oxygenated and deoxygenated hemoglobin. A Bayesian sequential Monte Carlo approach (particle filter, PF) was applied to simulated recordings of electrical and neurovascular mediated hemodynamic activity, and the advantages of a unified framework were shown. Approach. Multiple neural activities and hemodynamic responses were simulated in the primary motor cortex of a subject brain. EEG and fNIRS recordings were obtained by means of forward models of volume conduction and light propagation through the head. A state space model of combined EEG and fNIRS data was built and its dynamic evolution was estimated through a Bayesian sequential Monte Carlo approach (PF). Main results. We showed the feasibility of the procedure and the improvements in both electrical and hemodynamic brain activity reconstruction when using the PF on combined EEG and fNIRS measurements. Significance. The investigated procedure allows one to combine the information provided by the two methodologies, and, by taking advantage of a physical model of the coupling between electrical and hemodynamic response, to obtain a better estimate of brain activity evolution. Despite the high computational demand, application of such an approach to in vivo recordings could fully exploit the advantages of this combined brain imaging technology.

Spectral analysis of the IntCal98 calibration curve: a Bayesian view

International Nuclear Information System (INIS)

Palonen, V.; Tikkanen, P.

2004-01-01

Preliminary results from a Bayesian approach to find periodicities in the IntCal98 calibration curve are given. It has been shown in the literature that the discrete Fourier transform (Schuster periodogram) corresponds to the use of an approximate Bayesian model of one harmonic frequency and Gaussian noise. Advantages of the Bayesian approach include the possibility to use models for variable, attenuated and multiple frequencies, the capability to analyze unevenly spaced data and the possibility to assess the significance and uncertainties of spectral estimates. In this work, a new Bayesian model using random walk noise to take care of the trend in the data is developed. Both Bayesian models are described and the first results of the new model are reported and compared with results from straightforward discrete-Fourier-transform and maximum-entropy-method spectral analyses

A Bayesian account of quantum histories

International Nuclear Information System (INIS)

Marlow, Thomas

2006-01-01

We investigate whether quantum history theories can be consistent with Bayesian reasoning and whether such an analysis helps clarify the interpretation of such theories. First, we summarise and extend recent work categorising two different approaches to formalising multi-time measurements in quantum theory. The standard approach consists of describing an ordered series of measurements in terms of history propositions with non-additive 'probabilities.' The non-standard approach consists of defining multi-time measurements to consist of sets of exclusive and exhaustive history propositions and recovering the single-time exclusivity of results when discussing single-time history propositions. We analyse whether such history propositions can be consistent with Bayes' rule. We show that certain class of histories are given a natural Bayesian interpretation, namely, the linearly positive histories originally introduced by Goldstein and Page. Thus, we argue that this gives a certain amount of interpretational clarity to the non-standard approach. We also attempt a justification of our analysis using Cox's axioms of probability theory

Clinical and morphological evaluation of snake venom derived fibrin glue on the tendon healing in dogs

Directory of Open Access Journals (Sweden)

G. C. Ferraro

2005-12-01

Full Text Available The aim of this study was to evaluate the effect of snake venom derived fibrin glue on the healing of the deep digital flexor tendon, during three periods. The tendon of the 2nd digit of 30 thoracic limbs of dogs was partially sectioned for glue application. Biopsies were performed 7, 15, and 30 days post surgery for the clinical and morphological study of tendons. Analysis of the results showed that 73.3% of the tendons showed stump retraction and 16.6% moderate to excessive adherence, which affected sliding. There was a significant difference in the number of inflammatory cells among the three studied periods, being the highest on day 15. The morphological analysis revealed a typical tendon healing process with a lower level of inflammation in the acute phase, facilitating the cicatricial maturation phase. Snake venom derived fibrin glue promotes the healing in dog flexor tendon.

Bayesian probabilistic network approach for managing earthquake risks of cities

DEFF Research Database (Denmark)

Bayraktarli, Yahya; Faber, Michael

2011-01-01

This paper considers the application of Bayesian probabilistic networks (BPNs) to large-scale risk based decision making in regard to earthquake risks. A recently developed risk management framework is outlined which utilises Bayesian probabilistic modelling, generic indicator based risk models...... and a fourth module on the consequences of an earthquake. Each of these modules is integrated into a BPN. Special attention is given to aggregated risk, i.e. the risk contribution from assets at multiple locations in a city subjected to the same earthquake. The application of the methodology is illustrated...... on an example considering a portfolio of reinforced concrete structures in a city located close to the western part of the North Anatolian Fault in Turkey....

A Bayesian Network approach to the evaluation of building design and its consequences for employee performance and operational costs

DEFF Research Database (Denmark)

Jensen, Kasper Lynge; Toftum, Jørn; Friis-Hansen, Peter

2009-01-01

A Bayesian Network approach has been developed that can compare different building designs by estimating the effects of the thermal indoor environment on the mental performance of office workers. A part of this network is based on the compilation of subjective thermal sensation data and the assoc...

A population-based Bayesian approach to the minimal model of glucose and insulin homeostasis

DEFF Research Database (Denmark)

Andersen, Kim Emil; Højbjerre, Malene

2005-01-01

-posed estimation problem, where the reconstruction most often has been done by non-linear least squares techniques separately for each entity. The minmal model was originally specified for a single individual and does not combine several individuals with the advantage of estimating the metabolic portrait...... to a population-based model. The estimation of the parameters are efficiently implemented in a Bayesian approach where posterior inference is made through the use of Markov chain Monte Carlo techniques. Hereby we obtain a powerful and flexible modelling framework for regularizing the ill-posed estimation problem...

A Bayesian approach to the analysis of quantal bioassay studies using nonparametric mixture models.

Science.gov (United States)

Fronczyk, Kassandra; Kottas, Athanasios

2014-03-01

We develop a Bayesian nonparametric mixture modeling framework for quantal bioassay settings. The approach is built upon modeling dose-dependent response distributions. We adopt a structured nonparametric prior mixture model, which induces a monotonicity restriction for the dose-response curve. Particular emphasis is placed on the key risk assessment goal of calibration for the dose level that corresponds to a specified response. The proposed methodology yields flexible inference for the dose-response relationship as well as for other inferential objectives, as illustrated with two data sets from the literature. © 2013, The International Biometric Society.

An efficient multiple particle filter based on the variational Bayesian approach

KAUST Repository

Ait-El-Fquih, Boujemaa

2015-12-07

This paper addresses the filtering problem in large-dimensional systems, in which conventional particle filters (PFs) remain computationally prohibitive owing to the large number of particles needed to obtain reasonable performances. To overcome this drawback, a class of multiple particle filters (MPFs) has been recently introduced in which the state-space is split into low-dimensional subspaces, and then a separate PF is applied to each subspace. In this paper, we adopt the variational Bayesian (VB) approach to propose a new MPF, the VBMPF. The proposed filter is computationally more efficient since the propagation of each particle requires generating one (new) particle only, while in the standard MPFs a set of (children) particles needs to be generated. In a numerical test, the proposed VBMPF behaves better than the PF and MPF.

On the limitations of standard statistical modeling in biological systems: a full Bayesian approach for biology.

Science.gov (United States)

Gomez-Ramirez, Jaime; Sanz, Ricardo

2013-09-01

One of the most important scientific challenges today is the quantitative and predictive understanding of biological function. Classical mathematical and computational approaches have been enormously successful in modeling inert matter, but they may be inadequate to address inherent features of biological systems. We address the conceptual and methodological obstacles that lie in the inverse problem in biological systems modeling. We introduce a full Bayesian approach (FBA), a theoretical framework to study biological function, in which probability distributions are conditional on biophysical information that physically resides in the biological system that is studied by the scientist. Copyright © 2013 Elsevier Ltd. All rights reserved.

Assessment of successful smoking cessation by psychological factors using the Bayesian network approach.

Science.gov (United States)

Yang, Xiaorong; Li, Suyun; Pan, Lulu; Wang, Qiang; Li, Huijie; Han, Mingkui; Zhang, Nan; Jiang, Fan; Jia, Chongqi

2016-07-01

The association between psychological factors and smoking cessation is complicated and inconsistent in published researches, and the joint effect of psychological factors on smoking cessation is unclear. This study explored how psychological factors jointly affect the success of smoking cessation using a Bayesian network approach. A community-based case control study was designed with 642 adult male successful smoking quitters as the cases, and 700 adult male failed smoking quitters as the controls. General self-efficacy (GSE), trait coping style (positive-trait coping style (PTCS) and negative-trait coping style (NTCS)) and self-rating anxiety (SA) were evaluated by GSE Scale, Trait Coping Style Questionnaire and SA Scale, respectively. Bayesian network was applied to evaluate the relationship between psychological factors and successful smoking cessation. The local conditional probability table of smoking cessation indicated that different joint conditions of psychological factors led to different outcomes for smoking cessation. Among smokers with high PTCS, high NTCS and low SA, only 36.40% successfully quitted smoking. However, among smokers with low pack-years of smoking, high GSE, high PTCS and high SA, 63.64% successfully quitted smoking. Our study indicates psychological factors jointly influence smoking cessation outcome. According to different joint situations, different solutions should be developed to control tobacco in practical intervention.

Dynamic probability evaluation of safety levels of earth-rockfill dams using Bayesian approach

Directory of Open Access Journals (Sweden)

Zi-wu Fan

2009-06-01

Full Text Available In order to accurately predict and control the aging process of dams, new information should be collected continuously to renew the quantitative evaluation of dam safety levels. Owing to the complex structural characteristics of dams, it is quite difficult to predict the time-varying factors affecting their safety levels. It is not feasible to employ dynamic reliability indices to evaluate the actual safety levels of dams. Based on the relevant regulations for dam safety classification in China, a dynamic probability description of dam safety levels was developed. Using the Bayesian approach and effective information mining, as well as real-time information, this study achieved more rational evaluation and prediction of dam safety levels. With the Bayesian expression of discrete stochastic variables, the a priori probabilities of the dam safety levels determined by experts were combined with the likelihood probability of the real-time check information, and the probability information for the evaluation of dam safety levels was renewed. The probability index was then applied to dam rehabilitation decision-making. This method helps reduce the difficulty and uncertainty of the evaluation of dam safety levels and complies with the current safe decision-making regulations for dams in China. It also enhances the application of current risk analysis methods for dam safety levels.

Development of Silicon Photomultipliers and their Applications to GlueX Calorimetry

Energy Technology Data Exchange (ETDEWEB)

Smith, Elton S. [Thomas Jefferson National Accelerator Facility (TJNAF), Newport News, VA (United States)

2016-07-01

The GlueX experiment is a photoproduction experiment in Hall D at Jefferson Lab that is being commissioned for use with the new 12 GeV accelerator. The purpose of the experiment is to search for Hybrid mesons, which are mesons with quark and gluon degrees of freedom. The barrel calorimeter of GlueX is instrumented with 4000 large-area (1.2 x1.2 cm2) silicon photomultipliers (SiPMs). These photon sensors have properties similar to vacuum photomultipliers, but are unaffected by high magnetic fields. In our experiment they operate in magnetic fields exceeding 1T. After extensive tests with a variety of sensors, we chose the S12045(X) custom SiPM arrays manufactured by Hamamatsu Corporation, also known as multi-pixel photon counters (MPPCs). We will give an overview of this new technology as well as the experience gained during two commissioning periods with beam.

Development of silicon photomultipliers and their applications to GlueX calorimetry

Energy Technology Data Exchange (ETDEWEB)

Smith, Elton S., E-mail: elton@jlab.org [Jefferson Lab, Newport News, VA 23606 (United States)

2016-07-07

The GlueX experiment is a photoproduction experiment in Hall D at Jefferson Lab that is being commissioned for use with the new 12 GeV accelerator. The purpose of the experiment is to search for Hybrid mesons, which are mesons with quark and gluon degrees of freedom. The barrel calorimeter of GlueX is instrumented with 4000 large-area (1.2 × 1.2 cm{sup 2}) silicon photomultipliers (SiPMs). These photon sensors have properties similar to vacuum photomultipliers, but are unaffected by high magnetic fields. In our experiment they operate in magnetic fields exceeding 1T. After extensive tests with a variety of sensors, we chose the S12045(X) custom SiPM arrays manufactured by Hamamatsu Corporation, also known as multi-pixel photon counters (MPPCs). We will give an overview of this new technology as well as the experience gained during two commissioning periods with beam.

Generalized likelihood uncertainty estimation (GLUE) using adaptive Markov chain Monte Carlo sampling

DEFF Research Database (Denmark)

Blasone, Roberta-Serena; Vrugt, Jasper A.; Madsen, Henrik

2008-01-01

propose an alternative strategy to determine the value of the cutoff threshold based on the appropriate coverage of the resulting uncertainty bounds. We demonstrate the superiority of this revised GLUE method with three different conceptual watershed models of increasing complexity, using both synthetic......In the last few decades hydrologists have made tremendous progress in using dynamic simulation models for the analysis and understanding of hydrologic systems. However, predictions with these models are often deterministic and as such they focus on the most probable forecast, without an explicit...... of applications. However, the MC based sampling strategy of the prior parameter space typically utilized in GLUE is not particularly efficient in finding behavioral simulations. This becomes especially problematic for high-dimensional parameter estimation problems, and in the case of complex simulation models...

Use of fibrin glue in the management of recurrent pterygium by conjunctival autograft

International Nuclear Information System (INIS)

Virendra K. Malik; Sandeep Kumar

2010-01-01

To evaluate whether use of fibrin glue instead of sutures for the treatment of recurrent pterygium with conjunctival auto-graft (CAG) further decreases its recurrence. A prospective, clinical open trial of 50 eyes of 50 patients with recurrent pterygium, who were randomly assigned to either, pterygium excision and CAG with fibrin glue (Group 1) or with 6 interrupted sutures (Group 2), was carried out from January 2009 to July 2010 at the outpatient department of Subharti Medical College, Meerut, North India. Both groups had 25 patients each. The groups were compared with regards to the surgical time taken, development of recurrence, postoperative symptoms (irritation, watering, and redness), and complications. Recurrence was seen in one (4%) eye in group I, and 3 (12%) eyes in group II after 9-13 months of follow up. The difference between the 2 groups was not statistically significant. The surgical time was significantly reduced, and postoperative inflammation and complications were less in group I. Postoperative symptoms were significantly more in group II patients. While conjunctival autograft with sutures for management of recurrent pterygium appears to be a safe and feasible modality, fibrin glue fixation of the autograft is a more viable option in terms of surgical ease, less time consuming, less postoperative complications, and less recurrence (Author).

Bayesian Meta-Analysis of Coefficient Alpha

Science.gov (United States)

Brannick, Michael T.; Zhang, Nanhua

2013-01-01

The current paper describes and illustrates a Bayesian approach to the meta-analysis of coefficient alpha. Alpha is the most commonly used estimate of the reliability or consistency (freedom from measurement error) for educational and psychological measures. The conventional approach to meta-analysis uses inverse variance weights to combine…

A general contact mechanical formulation of multilayered structures and its application to deconvolute thickness/mechanical properties of glue used in surface force apparatus.

Science.gov (United States)

Math, Souvik; Horn, Roger; Jayaram, Vikram; Biswas, Sanjay Kumar

2007-04-15

Currently data obtained from surface force apparatus experiments are convoluted with the mechanical response of glue of unknown thickness, used to bond mica sheets to the substrates. This paper describes a formulation to precisely deconvolute out the forces between the mica sheets by determining the thickness of glue, knowing the mechanical properties of the glue. The formulation consists of a general solution based on the noniterative Hankel transform of the Laplace equation. The generality is achieved by treating all the layers except the one in contact as an effective lumped system consisting of a set of springs in series, where each spring represents a layer. The solution is validated by nanoindentation of trilayer systems consisting of layers with widely diverse mechanical properties, some differing from each other by three orders of magnitude. SFA experiments are done with carefully metered slabs of glue. The proposed method is validated by comparing the actual glue thicknesses with those determined using the present analysis.

Bayesian Graphical Models

DEFF Research Database (Denmark)

Jensen, Finn Verner; Nielsen, Thomas Dyhre

2016-01-01

Mathematically, a Bayesian graphical model is a compact representation of the joint probability distribution for a set of variables. The most frequently used type of Bayesian graphical models are Bayesian networks. The structural part of a Bayesian graphical model is a graph consisting of nodes...

Application of fibrin glue with bandage contact lens in pterygium surgery

Directory of Open Access Journals (Sweden)

Wei Liu

2014-05-01

Full Text Available AIM: To explore the efficacy of fibrin glue with bandage contact lens for pain relief after pterygium surgery performed with limbal autograft transplantation.METHODS: A prospective clinical trial was carried out in 52 patients(72 eyesoperated for primary nasal pterygium. All patients were randomly divided into the fibrin glue with bandage contact lens group(experimental group, 28 cases, 38 eyesand suture group(control group, 24 cases, 34 eyes. Autologous limbal graft taken from the superotemporal limbus was used to cover the sclera after pterygium excision under local anesthesia with 20g/L lidocaine. In experimental group, the transplant was attached to the sclera with fibrin tissue adhesive and in control group with 10-0 Virgin silk sutures. Experimental group weared bandage contact lens after surgery while the control group did not. The degree of pain after surgery was evaluated at 1, 2, 3, 5 and 7d after surgery. Follow-up was 6mo, matching degree of graft and complication such as infection, relapse, implant healing badness and subconjunctival cyst were mainly observed and recorded.RESULTS: The pain index scores of the experimental group were significantly less than those of control group(all P=0.000. In observation period, all conjunctival autografts in both groups were successfully attached and were intact without falling off, dissolution or recurrence and there were no complications such as infection, relapse, implant healing badness and subconjunctival cyst.CONCLUSION: Fibrin glue with bandage contact lens could significantly release pain response afterpterygium excision surgery.

Predicting Drug Safety and Communicating Risk: Benefits of a Bayesian Approach.

Science.gov (United States)

Lazic, Stanley E; Edmunds, Nicholas; Pollard, Christopher E

2018-03-01

Drug toxicity is a major source of attrition in drug discovery and development. Pharmaceutical companies routinely use preclinical data to predict clinical outcomes and continue to invest in new assays to improve predictions. However, there are many open questions about how to make the best use of available data, combine diverse data, quantify risk, and communicate risk and uncertainty to enable good decisions. The costs of suboptimal decisions are clear: resources are wasted and patients may be put at risk. We argue that Bayesian methods provide answers to all of these problems and use hERG-mediated QT prolongation as a case study. Benefits of Bayesian machine learning models include intuitive probabilistic statements of risk that incorporate all sources of uncertainty, the option to include diverse data and external information, and visualizations that have a clear link between the output from a statistical model and what this means for risk. Furthermore, Bayesian methods are easy to use with modern software, making their adoption for safety screening straightforward. We include R and Python code to encourage the adoption of these methods.

BAYESIAN APPROACH TO THE PROCESS OF IDENTIFICATION OF THE DETERMINANTS OF INNOVATIVENESS

Directory of Open Access Journals (Sweden)

Marta Czyżewska

2014-08-01

Full Text Available Bayesian belief networks are applied in determining the most important factors of the innovativeness level of national economies. The paper is divided into two parts. The first presentsthe basic theory of Bayesian networks whereas in the second, the belief networks have been generated by an inhouse developed computer system called BeliefSEEKER which was implemented to generate the determinants influencing the innovativeness level of national economies.Qualitative analysis of the generated belief networks provided a way to define a set of the most important dimensions influencing the innovativeness level of economies and then the indicators that form these dimensions. It has been proven that Bayesian networks are very effective methods for multidimensional analysis and forming conclusions and recommendations regarding the strength of each innovative determinant influencing the overall performance of a country’s economy.

Estimating mental states of a depressed person with bayesian networks

NARCIS (Netherlands)

Klein, Michel C.A.; Modena, Gabriele

2013-01-01

In this work in progress paper we present an approach based on Bayesian Networks to model the relationship between mental states and empirical observations in a depressed person. We encode relationships and domain expertise as a Hierarchical Bayesian Network. Mental states are represented as latent

A Bayesian Hierarchical Modeling Approach to Predicting Flow in Ungauged Basins

Science.gov (United States)

Gronewold, A.; Alameddine, I.; Anderson, R. M.

2009-12-01

Recent innovative approaches to identifying and applying regression-based relationships between land use patterns (such as increasing impervious surface area and decreasing vegetative cover) and rainfall-runoff model parameters represent novel and promising improvements to predicting flow from ungauged basins. In particular, these approaches allow for predicting flows under uncertain and potentially variable future conditions due to rapid land cover changes, variable climate conditions, and other factors. Despite the broad range of literature on estimating rainfall-runoff model parameters, however, the absence of a robust set of modeling tools for identifying and quantifying uncertainties in (and correlation between) rainfall-runoff model parameters represents a significant gap in current hydrological modeling research. Here, we build upon a series of recent publications promoting novel Bayesian and probabilistic modeling strategies for quantifying rainfall-runoff model parameter estimation uncertainty. Our approach applies alternative measures of rainfall-runoff model parameter joint likelihood (including Nash-Sutcliffe efficiency, among others) to simulate samples from the joint parameter posterior probability density function. We then use these correlated samples as response variables in a Bayesian hierarchical model with land use coverage data as predictor variables in order to develop a robust land use-based tool for forecasting flow in ungauged basins while accounting for, and explicitly acknowledging, parameter estimation uncertainty. We apply this modeling strategy to low-relief coastal watersheds of Eastern North Carolina, an area representative of coastal resource waters throughout the world because of its sensitive embayments and because of the abundant (but currently threatened) natural resources it hosts. Consequently, this area is the subject of several ongoing studies and large-scale planning initiatives, including those conducted through the United

Alternative glues for the production of ATLAS silicon strip modules for the Phase-II upgrade of the ATLAS Inner Detector

CERN Document Server

INSPIRE-00407830; Bloch, Ingo; Edwards, Sam; Friedrich, Conrad; Gregor, Ingrid M.; Jones, T; Lacker, Heiko; Pyatt, Simon; Rehnisch, Laura; Sperlich, Dennis; Wilson, John

2016-05-24

The Phase-II upgrade of the ATLAS detector for the High Luminosity Large Hadron Collider (HL-LHC) includes the replacement of the current Inner Detector with an all-silicon tracker consisting of pixel and strip detectors. The current Phase-II detector layout requires the construction of 20,000 strip detector modules consisting of sensor, circuit boards and readout chips, which are connected mechanically using adhesives. The adhesive between readout chips and circuit board is a silver epoxy glue as was used in the current ATLAS SemiConductor Tracker (SCT). This glue has several disadvantages, which motivated the search for an alternative. This paper presents a study concerning the use of six ultra-violet (UV) cure glues and a glue pad for use in the assembly of silicon strip detector modules for the ATLAS upgrade. Trials were carried out to determine the ease of use, the thermal conduction and shear strength, thermal cycling, radiation hardness, corrosion resistance and shear strength tests. These investigatio...

Re: Fibrin Glue Injections: A Minimally Invasive and Cost-Effective Treatment for Post-Renal Transplant Lymphoceles and Lymph Fistulas

Directory of Open Access Journals (Sweden)

Presser N

2016-03-01

Full Text Available In this retrospective study, 46 (2.7% patients out of 1662 kidney transplant recipients had developed symptomatic lymphocele/lymph fistula requiring intervention over an 11-year period. Open surgical drainage (22, laparoscopic surgical drainage (11 and percutaneous fibrin glue injections into the drained lymphocele cavity (13 were used to treat this complication. Besides being effective both on the early and late developed lymphoceles, significantly lower recurrence rates by fibrin glue injections and lower median treatment costs were observed when compared with the other two surgical modalities. It has also the advantage of an outpatient procedure that can be performed using fluoroscopic guidance, under local anesthesia. However, due to era effect, most of the open and laparoscopic surgical recipients were treated with sirolimus, a well-known antiproliferative immunosuppressive agent, which can promote development of lymphoceles and surgical failure. However, the majority of fibrin glue-treated cases were with tacrolimus-based regimens, but this study, in its nature, is far from giving the answer for decreased number of recurrences with fibrin glue

Integrating health economics modeling in the product development cycle of medical devices: a Bayesian approach.

Science.gov (United States)

Vallejo-Torres, Laura; Steuten, Lotte M G; Buxton, Martin J; Girling, Alan J; Lilford, Richard J; Young, Terry

2008-01-01

Medical device companies are under growing pressure to provide health-economic evaluations of their products. Cost-effectiveness analyses are commonly undertaken as a one-off exercise at the late stage of development of new technologies; however, the benefits of an iterative use of economic evaluation during the development process of new products have been acknowledged in the literature. Furthermore, the use of Bayesian methods within health technology assessment has been shown to be of particular value in the dynamic framework of technology appraisal when new information becomes available in the life cycle of technologies. In this study, we set out a methodology to adapt these methods for their application to directly support investment decisions in a commercial setting from early stages of the development of new medical devices. Starting with relatively simple analysis from the very early development phase and proceeding to greater depth of analysis at later stages, a Bayesian approach facilitates the incorporation of all available evidence and would help companies to make better informed choices at each decision point.

Percutaneous Glue Embolization of a Visceral Artery Pseudoaneurysm in a Case of Sickle Cell Anemia

International Nuclear Information System (INIS)

Gulati, Gurpreet S.; Gulati, Manpreet S.; Makharia, Govind; Hatimota, Pradeep; Saikia, Nripen; Paul, Shashi B.; Acharya, Subrat

2006-01-01

Although aneurysmal complications of sickle cell anemia have been described in the intracranial circulation, visceral artery pseudoaneurysms in this disease entity have not previously been reported in the literature. Conventional treatment of visceral pseudoaneurysms has been surgical ligation or resection of the aneurysm. Transcatheter embolization has emerged as an attractive, minimally invasive alternative to surgery in the treatment of these lesions. In certain situations, however, due to the unfavorable angiographic anatomy precluding safe transcatheter embolization, direct percutaneous glue injection of the pseudoaneurysm sac may be considered to achieve successful occlusion of the sac. The procedure may be rendered safer by simultaneous balloon protection of the parent artery. We describe this novel treatment modality in a case of inferior pancreaticoduodenal artery pseudoaneurysm in a patient with sickle cell anemia. Although a complication in the form of glue reflux into the parent vessel occurred that necessitated surgery, this treatment modality may be used in very selected cases (where conventional endovascular embolization techniques are not applicable) after careful selection of the balloon diameter and appropriate concentration of the glue-lipiodol mixture

Bayesian artificial intelligence

CERN Document Server

Korb, Kevin B

2003-01-01

As the power of Bayesian techniques has become more fully realized, the field of artificial intelligence has embraced Bayesian methodology and integrated it to the point where an introduction to Bayesian techniques is now a core course in many computer science programs. Unlike other books on the subject, Bayesian Artificial Intelligence keeps mathematical detail to a minimum and covers a broad range of topics. The authors integrate all of Bayesian net technology and learning Bayesian net technology and apply them both to knowledge engineering. They emphasize understanding and intuition but also provide the algorithms and technical background needed for applications. Software, exercises, and solutions are available on the authors' website.

BAYESIAN APPROACH TO THE ANALYSIS OF MONETARY POLICY IMPACT ON RUSSIAN MACROECONOMICS INDICATORS

Directory of Open Access Journals (Sweden)

Sheveleva O. A.

2017-12-01

Full Text Available In this paper the interaction between the production macroeconomic indicators of the Russian economy and MIBOR (the main operational benchmark of the Bank of Russia, as well as the relationship between the inflation indicators and money supply were investigated with Bayesian approach. Conjugate Normal Inverse Wishart Prior was used. According to the study, tough monetary policy has a deterrent effect on the Russian economy. The growth of the money market rate causes a reduction in investments and output in the main sectors of the economy, as well as a drop in the income of the population with an increase in the unemployment rate.

Bayesian adaptive methods for clinical trials

National Research Council Canada - National Science Library

Berry, Scott M

2011-01-01

.... One is that Bayesian approaches implemented with the majority of their informative content coming from the current data, and not any external prior informa- tion, typically have good frequentist properties (e.g...

CytoBayesJ: software tools for Bayesian analysis of cytogenetic radiation dosimetry data.

Science.gov (United States)

Ainsbury, Elizabeth A; Vinnikov, Volodymyr; Puig, Pedro; Maznyk, Nataliya; Rothkamm, Kai; Lloyd, David C

2013-08-30

A number of authors have suggested that a Bayesian approach may be most appropriate for analysis of cytogenetic radiation dosimetry data. In the Bayesian framework, probability of an event is described in terms of previous expectations and uncertainty. Previously existing, or prior, information is used in combination with experimental results to infer probabilities or the likelihood that a hypothesis is true. It has been shown that the Bayesian approach increases both the accuracy and quality assurance of radiation dose estimates. New software entitled CytoBayesJ has been developed with the aim of bringing Bayesian analysis to cytogenetic biodosimetry laboratory practice. CytoBayesJ takes a number of Bayesian or 'Bayesian like' methods that have been proposed in the literature and presents them to the user in the form of simple user-friendly tools, including testing for the most appropriate model for distribution of chromosome aberrations and calculations of posterior probability distributions. The individual tools are described in detail and relevant examples of the use of the methods and the corresponding CytoBayesJ software tools are given. In this way, the suitability of the Bayesian approach to biological radiation dosimetry is highlighted and its wider application encouraged by providing a user-friendly software interface and manual in English and Russian. Copyright © 2013 Elsevier B.V. All rights reserved.

A Robust Obstacle Avoidance for Service Robot Using Bayesian Approach

Directory of Open Access Journals (Sweden)

Widodo Budiharto

2011-03-01

Full Text Available The objective of this paper is to propose a robust obstacle avoidance method for service robot in indoor environment. The method for obstacles avoidance uses information about static obstacles on the landmark using edge detection. Speed and direction of people that walks as moving obstacle obtained by single camera using tracking and recognition system and distance measurement using 3 ultrasonic sensors. A new geometrical model and maneuvering method for moving obstacle avoidance introduced and combined with Bayesian approach for state estimation. The obstacle avoidance problem is formulated using decision theory, prior and posterior distribution and loss function to determine an optimal response based on inaccurate sensor data. Algorithms for moving obstacles avoidance method proposed and experiment results implemented to service robot also presented. Various experiments show that our proposed method very fast, robust and successfully implemented to service robot called Srikandi II that equipped with 4 DOF arm robot developed in our laboratory.

Prediction of road accidents: A Bayesian hierarchical approach.

Science.gov (United States)

Deublein, Markus; Schubert, Matthias; Adey, Bryan T; Köhler, Jochen; Faber, Michael H

2013-03-01

In this paper a novel methodology for the prediction of the occurrence of road accidents is presented. The methodology utilizes a combination of three statistical methods: (1) gamma-updating of the occurrence rates of injury accidents and injured road users, (2) hierarchical multivariate Poisson-lognormal regression analysis taking into account correlations amongst multiple dependent model response variables and effects of discrete accident count data e.g. over-dispersion, and (3) Bayesian inference algorithms, which are applied by means of data mining techniques supported by Bayesian Probabilistic Networks in order to represent non-linearity between risk indicating and model response variables, as well as different types of uncertainties which might be present in the development of the specific models. Prior Bayesian Probabilistic Networks are first established by means of multivariate regression analysis of the observed frequencies of the model response variables, e.g. the occurrence of an accident, and observed values of the risk indicating variables, e.g. degree of road curvature. Subsequently, parameter learning is done using updating algorithms, to determine the posterior predictive probability distributions of the model response variables, conditional on the values of the risk indicating variables. The methodology is illustrated through a case study using data of the Austrian rural motorway network. In the case study, on randomly selected road segments the methodology is used to produce a model to predict the expected number of accidents in which an injury has occurred and the expected number of light, severe and fatally injured road users. Additionally, the methodology is used for geo-referenced identification of road sections with increased occurrence probabilities of injury accident events on a road link between two Austrian cities. It is shown that the proposed methodology can be used to develop models to estimate the occurrence of road accidents for any

Human dental age estimation using third molar developmental stages: does a Bayesian approach outperform regression models to discriminate between juveniles and adults?

Science.gov (United States)

Thevissen, P W; Fieuws, S; Willems, G

2010-01-01

Dental age estimation methods based on the radiologically detected third molar developmental stages are implemented in forensic age assessments to discriminate between juveniles and adults considering the judgment of young unaccompanied asylum seekers. Accurate and unbiased age estimates combined with appropriate quantified uncertainties are the required properties for accurate forensic reporting. In this study, a subset of 910 individuals uniformly distributed in age between 16 and 22 years was selected from an existing dataset collected by Gunst et al. containing 2,513 panoramic radiographs with known third molar developmental stages of Belgian Caucasian men and women. This subset was randomly split in a training set to develop a classical regression analysis and a Bayesian model for the multivariate distribution of the third molar developmental stages conditional on age and in a test set to assess the performance of both models. The aim of this study was to verify if the Bayesian approach differentiates the age of maturity more precisely and removes the bias, which disadvantages the systematically overestimated young individuals. The Bayesian model offers the discrimination of subjects being older than 18 years more appropriate and produces more meaningful prediction intervals but does not strongly outperform the classical approaches.

A flexible Bayesian assessment for the expected impact of data on prediction confidence for optimal sampling designs

Science.gov (United States)

Leube, Philipp; Geiges, Andreas; Nowak, Wolfgang

2010-05-01

Incorporating hydrogeological data, such as head and tracer data, into stochastic models of subsurface flow and transport helps to reduce prediction uncertainty. Considering limited financial resources available for the data acquisition campaign, information needs towards the prediction goal should be satisfied in a efficient and task-specific manner. For finding the best one among a set of design candidates, an objective function is commonly evaluated, which measures the expected impact of data on prediction confidence, prior to their collection. An appropriate approach to this task should be stochastically rigorous, master non-linear dependencies between data, parameters and model predictions, and allow for a wide variety of different data types. Existing methods fail to fulfill all these requirements simultaneously. For this reason, we introduce a new method, denoted as CLUE (Cross-bred Likelihood Uncertainty Estimator), that derives the essential distributions and measures of data utility within a generalized, flexible and accurate framework. The method makes use of Bayesian GLUE (Generalized Likelihood Uncertainty Estimator) and extends it to an optimal design method by marginalizing over the yet unknown data values. Operating in a purely Bayesian Monte-Carlo framework, CLUE is a strictly formal information processing scheme free of linearizations. It provides full flexibility associated with the type of measurements (linear, non-linear, direct, indirect) and accounts for almost arbitrary sources of uncertainty (e.g. heterogeneity, geostatistical assumptions, boundary conditions, model concepts) via stochastic simulation and Bayesian model averaging. This helps to minimize the strength and impact of possible subjective prior assumptions, that would be hard to defend prior to data collection. Our study focuses on evaluating two different uncertainty measures: (i) expected conditional variance and (ii) expected relative entropy of a given prediction goal. The

A Bayesian approach for predicting risk of autonomous underwater vehicle loss during their missions

International Nuclear Information System (INIS)

Brito, Mario; Griffiths, Gwyn

2016-01-01

Autonomous Underwater Vehicles (AUVs) are effective platforms for science research and monitoring, and for military and commercial data-gathering purposes. However, there is an inevitable risk of loss during any mission. Quantifying the risk of loss is complex, due to the combination of vehicle reliability and environmental factors, and cannot be determined through analytical means alone. An alternative approach – formal expert judgment – is a time-consuming process; consequently a method is needed to broaden the applicability of judgments beyond the narrow confines of an elicitation for a defined environment. We propose and explore a solution founded on a Bayesian Belief Network (BBN), where the results of the expert judgment elicitation are taken as the initial prior probability of loss due to failure. The network topology captures the causal effects of the environment separately on the vehicle and on the support platform, and combines these to produce an updated probability of loss due to failure. An extended version of the Kaplan–Meier estimator is then used to update the mission risk profile with travelled distance. Sensitivity analysis of the BBN is presented and a case study of Autosub3 AUV deployment in the Amundsen Sea is discussed in detail. - Highlights: • Novel method to estimate risk of autonomous vehicle loss in uncertain environments. • A framework to integrate frequentist and subjective probability modelling. • A Bayesian belief updating method for capturing variation in operating environment. • Graphical approach for sensitivity analysis, applicable to any BBN model validation. • Pragmatic case studies showing the application of the proposed framework.

Testing students' e-learning via Facebook through Bayesian structural equation modeling.

Science.gov (United States)

Salarzadeh Jenatabadi, Hashem; Moghavvemi, Sedigheh; Wan Mohamed Radzi, Che Wan Jasimah Bt; Babashamsi, Parastoo; Arashi, Mohammad

2017-01-01

Learning is an intentional activity, with several factors affecting students' intention to use new learning technology. Researchers have investigated technology acceptance in different contexts by developing various theories/models and testing them by a number of means. Although most theories/models developed have been examined through regression or structural equation modeling, Bayesian analysis offers more accurate data analysis results. To address this gap, the unified theory of acceptance and technology use in the context of e-learning via Facebook are re-examined in this study using Bayesian analysis. The data (S1 Data) were collected from 170 students enrolled in a business statistics course at University of Malaya, Malaysia, and tested with the maximum likelihood and Bayesian approaches. The difference between the two methods' results indicates that performance expectancy and hedonic motivation are the strongest factors influencing the intention to use e-learning via Facebook. The Bayesian estimation model exhibited better data fit than the maximum likelihood estimator model. The results of the Bayesian and maximum likelihood estimator approaches are compared and the reasons for the result discrepancy are deliberated.

Testing students' e-learning via Facebook through Bayesian structural equation modeling.

Directory of Open Access Journals (Sweden)

Hashem Salarzadeh Jenatabadi

Full Text Available Learning is an intentional activity, with several factors affecting students' intention to use new learning technology. Researchers have investigated technology acceptance in different contexts by developing various theories/models and testing them by a number of means. Although most theories/models developed have been examined through regression or structural equation modeling, Bayesian analysis offers more accurate data analysis results. To address this gap, the unified theory of acceptance and technology use in the context of e-learning via Facebook are re-examined in this study using Bayesian analysis. The data (S1 Data were collected from 170 students enrolled in a business statistics course at University of Malaya, Malaysia, and tested with the maximum likelihood and Bayesian approaches. The difference between the two methods' results indicates that performance expectancy and hedonic motivation are the strongest factors influencing the intention to use e-learning via Facebook. The Bayesian estimation model exhibited better data fit than the maximum likelihood estimator model. The results of the Bayesian and maximum likelihood estimator approaches are compared and the reasons for the result discrepancy are deliberated.

Software Health Management with Bayesian Networks

Science.gov (United States)

Mengshoel, Ole; Schumann, JOhann

2011-01-01

Most modern aircraft as well as other complex machinery is equipped with diagnostics systems for its major subsystems. During operation, sensors provide important information about the subsystem (e.g., the engine) and that information is used to detect and diagnose faults. Most of these systems focus on the monitoring of a mechanical, hydraulic, or electromechanical subsystem of the vehicle or machinery. Only recently, health management systems that monitor software have been developed. In this paper, we will discuss our approach of using Bayesian networks for Software Health Management (SWHM). We will discuss SWHM requirements, which make advanced reasoning capabilities for the detection and diagnosis important. Then we will present our approach to using Bayesian networks for the construction of health models that dynamically monitor a software system and is capable of detecting and diagnosing faults.

Validation of Sustainable Development Practices Scale Using the Bayesian Approach to Item Response Theory

Directory of Open Access Journals (Sweden)

Martin Hernani Merino

2014-12-01

Full Text Available There has been growing recognition of the importance of creating performance measurement tools for the economic, social and environmental management of micro and small enterprise (MSE. In this context, this study aims to validate an instrument to assess perceptions of sustainable development practices by MSEs by means of a Graded Response Model (GRM with a Bayesian approach to Item Response Theory (IRT. The results based on a sample of 506 university students in Peru, suggest that a valid measurement instrument was achieved. At the end of the paper, methodological and managerial contributions are presented.

A tutorial introduction to Bayesian models of cognitive development.

Science.gov (United States)

Perfors, Amy; Tenenbaum, Joshua B; Griffiths, Thomas L; Xu, Fei

2011-09-01

We present an introduction to Bayesian inference as it is used in probabilistic models of cognitive development. Our goal is to provide an intuitive and accessible guide to the what, the how, and the why of the Bayesian approach: what sorts of problems and data the framework is most relevant for, and how and why it may be useful for developmentalists. We emphasize a qualitative understanding of Bayesian inference, but also include information about additional resources for those interested in the cognitive science applications, mathematical foundations, or machine learning details in more depth. In addition, we discuss some important interpretation issues that often arise when evaluating Bayesian models in cognitive science. Copyright © 2010 Elsevier B.V. All rights reserved.

Bayesian inference in probabilistic risk assessment-The current state of the art

International Nuclear Information System (INIS)

Kelly, Dana L.; Smith, Curtis L.

2009-01-01

Markov chain Monte Carlo (MCMC) approaches to sampling directly from the joint posterior distribution of aleatory model parameters have led to tremendous advances in Bayesian inference capability in a wide variety of fields, including probabilistic risk analysis. The advent of freely available software coupled with inexpensive computing power has catalyzed this advance. This paper examines where the risk assessment community is with respect to implementing modern computational-based Bayesian approaches to inference. Through a series of examples in different topical areas, it introduces salient concepts and illustrates the practical application of Bayesian inference via MCMC sampling to a variety of important problems

Bayesian data analysis in population ecology: motivations, methods, and benefits

Science.gov (United States)

Dorazio, Robert

2016-01-01

During the 20th century ecologists largely relied on the frequentist system of inference for the analysis of their data. However, in the past few decades ecologists have become increasingly interested in the use of Bayesian methods of data analysis. In this article I provide guidance to ecologists who would like to decide whether Bayesian methods can be used to improve their conclusions and predictions. I begin by providing a concise summary of Bayesian methods of analysis, including a comparison of differences between Bayesian and frequentist approaches to inference when using hierarchical models. Next I provide a list of problems where Bayesian methods of analysis may arguably be preferred over frequentist methods. These problems are usually encountered in analyses based on hierarchical models of data. I describe the essentials required for applying modern methods of Bayesian computation, and I use real-world examples to illustrate these methods. I conclude by summarizing what I perceive to be the main strengths and weaknesses of using Bayesian methods to solve ecological inference problems.

Airline Sustainability Modeling: A New Framework with Application of Bayesian Structural Equation Modeling

Directory of Open Access Journals (Sweden)

Hashem Salarzadeh Jenatabadi

2016-11-01

Full Text Available There are many factors which could influence the sustainability of airlines. The main purpose of this study is to introduce a framework for a financial sustainability index and model it based on structural equation modeling (SEM with maximum likelihood and Bayesian predictors. The introduced framework includes economic performance, operational performance, cost performance, and financial performance. Based on both Bayesian SEM (Bayesian-SEM and Classical SEM (Classical-SEM, it was found that economic performance with both operational performance and cost performance are significantly related to the financial performance index. The four mathematical indices employed are root mean square error, coefficient of determination, mean absolute error, and mean absolute percentage error to compare the efficiency of Bayesian-SEM and Classical-SEM in predicting the airline financial performance. The outputs confirmed that the framework with Bayesian prediction delivered a good fit with the data, although the framework predicted with a Classical-SEM approach did not prepare a well-fitting model. The reasons for this discrepancy between Classical and Bayesian predictions, as well as the potential advantages and caveats with the application of Bayesian approach in airline sustainability studies, are debated.

On the prior probabilities for two-stage Bayesian estimates

International Nuclear Information System (INIS)

Kohut, P.

1992-01-01

The method of Bayesian inference is reexamined for its applicability and for the required underlying assumptions in obtaining and using prior probability estimates. Two different approaches are suggested to determine the first-stage priors in the two-stage Bayesian analysis which avoid certain assumptions required for other techniques. In the first scheme, the prior is obtained through a true frequency based distribution generated at selected intervals utilizing actual sampling of the failure rate distributions. The population variability distribution is generated as the weighed average of the frequency distributions. The second method is based on a non-parametric Bayesian approach using the Maximum Entropy Principle. Specific features such as integral properties or selected parameters of prior distributions may be obtained with minimal assumptions. It is indicated how various quantiles may also be generated with a least square technique

Versatile fabrication of paper-based microfluidic devices with high chemical resistance using scholar glue and magnetic masks.

Science.gov (United States)

Cardoso, Thiago M G; de Souza, Fabrício R; Garcia, Paulo T; Rabelo, Denilson; Henry, Charles S; Coltro, Wendell K T

2017-06-29

Simple methods have been developed for fabricating microfluidic paper-based analytical devices (μPADs) but few of these devices can be used with organic solvents and/or aqueous solutions containing surfactants. This study describes a simple fabrication strategy for μPADs that uses readily available scholar glue to create the hydrophobic flow barriers that are resistant to surfactants and organic solvents. Microfluidic structures were defined by magnetic masks designed with either neodymium magnets or magnetic sheets to define the patter, and structures were created by spraying an aqueous solution of glue on the paper surface. The glue-coated paper was then exposed to UV/Vis light for cross-linking to maximize chemical resistance. Examples of microzone arrays and microfluidic devices are demonstrated. μPADs fabricated with scholar glue retained their barriers when used with surfactants, organic solvents, and strong/weak acids and bases unlike common wax-printed barriers. Paper microzones and microfluidic devices were successfully used for colorimetric assays of clinically relevant analytes commonly detected in urinalysis to demonstrate the low background of the barrier material and generally applicability to sensing. The proposed fabrication method is attractive for both its ability to be used with diverse chemistries and the low cost and simplicity of the materials and process. Copyright © 2017 Elsevier B.V. All rights reserved.

Pilot study of dynamic Bayesian networks approach for fault diagnostics and accident progression prediction in HTR-PM

Energy Technology Data Exchange (ETDEWEB)

Zhao, Yunfei; Tong, Jiejuan; Zhang, Liguo, E-mail: lgzhang@tsinghua.edu.cn; Zhang, Qin

2015-09-15

Highlights: • Dynamic Bayesian network is used to diagnose and predict accident progress in HTR-PM. • Dynamic Bayesian network model of HTR-PM is built based on detailed system analysis. • LOCA Simulations validate the above model even if part monitors are lost or false. - Abstract: The first high-temperature-reactor pebble-bed demonstration module (HTR-PM) is under construction currently in China. At the same time, development of a system that is used to support nuclear emergency response is in progress. The supporting system is expected to complete two tasks. The first one is diagnostics of the fault in the reactor based on abnormal sensor measurements obtained. The second one is prognostic of the accident progression based on sensor measurements obtained and operator actions. Both tasks will provide valuable guidance for emergency staff to take appropriate protective actions. Traditional method for the two tasks relies heavily on expert judgment, and has been proven to be inappropriate in some cases, such as Three Mile Island accident. To better perform the two tasks, dynamic Bayesian networks (DBN) is introduced in this paper and a pilot study based on the approach is carried out. DBN is advantageous in representing complex dynamic systems and taking full consideration of evidences obtained to perform diagnostics and prognostics. Pearl's loopy belief propagation (LBP) algorithm is recommended for diagnostics and prognostics in DBN. The DBN model of HTR-PM is created based on detailed system analysis and accident progression analysis. A small break loss of coolant accident (SBLOCA) is selected to illustrate the application of the DBN model of HTR-PM in fault diagnostics (FD) and accident progression prognostics (APP). Several advantages of DBN approach compared with other techniques are discussed. The pilot study lays the foundation for developing the nuclear emergency response supporting system (NERSS) for HTR-PM.

Characterisation of a new bioadhesive system based on polysaccharides with the potential to be used as bone glue.

Science.gov (United States)

Hoffmann, Bettina; Volkmer, Elias; Kokott, Andreas; Augat, Peter; Ohnmacht, Michael; Sedlmayr, Nicole; Schieker, Matthias; Claes, Lutz; Mutschler, Wolf; Ziegler, Günter

2009-10-01

Although gluing bone is in theory a very attractive alternative to classical fracture treatment, this method is not yet clinically established due to the lack of an adhesive which would meet all the necessary requirements. We therefore developed a novel two-component bioadhesive system with the potential to be used as a bone adhesive based on biocompatible and degradable biopolymers (chitosan, oxidised dextran or starch). After mixing in water, the two components covalently cross-link by forming a Schiff's base. By the same mechanism, the glue binds to any other exposed amino group such as for example those exposed in fractured bone, even in the presence of water. Modified chitosan was synthesised from commercially available chitosan by deacetylation and was then reduced in molecular weight by heating in acid. The amount of free amino groups was analysed by IR. The molecular weight was determined by viscosimetry. Starch or dextran were oxidised with periodic acid to generate aldehyde groups, which were quantified by titration. l-Dopa was conjugated to oxidised dextran or starch in analogy to the gluing mechanism of mussels. Biomechanical studies revealed that the new glue is superior to fibrin glue, but has less adhesive strength than cyanoacrylates. In vitro cell testing demonstrated excellent biocompatibility, rendering this glue a potential candidate for clinical use.

Coronary artery narrowing after aortic root reconstruction with resorcin-formalin glue.

Science.gov (United States)

Martinelli, L; Graffigna, A; Guarnerio, M; Bonmassari, R; Disertori, M

2000-11-01

Severe stenosis of right and left main coronary artery ostia developed after aortic root reconstruction with gelatin-resorcin-formol glue for correction of acute type A aortic dissection. Surgical treatment of this condition required grafting of the right and left anterior descending arteries with bilateral mammary arteries on the beating heart.

Bayesian hypothesis testing and the maintenance rule

International Nuclear Information System (INIS)

Kelly, D.L.

1997-01-01

The Maintenance Rule (10 CFR 50.65) went into effect in the United States in July 1996. It requires commercial nuclear utilities to monitor system performance (system reliability and maintenance unavailability) for systems that are determined by the utility to be important to plant safety. Utilities must set performance goals for such systems and monitor system performance against these goals. In addition, these performance goals are intended to be commensurate with the safety significance of the system, which can be established by a probabilistic safety assessment of the plant. The author examines the frequents approach to monitoring performance, which is being used by several utilities, and proposes an alternative Bayesian approach. The Bayesian approach makes more complete use of the information in the probabilistic safety assessment, is consistent philosophically with the subjective interpretation given to probability in most probabilistic safety assessments, overcomes several pitfalls in the frequents approach, provides results which are easily interpretable, and is straightforward to implement using the information in the probabilistic safety assessment

The impact and adoption of GLUE 2.0 in the LCG/EGEE production Grid

International Nuclear Information System (INIS)

Burke, Stephen; Andreozzi, Sergio; Donno, Flavia; Ehm, Felix; Field, Laurence; Litmaath, Maarten; Millar, Paul

2010-01-01

The GLUE information schema has been in use in the LCG/EGEE production Grid since the first version was defined in 2002. In 2007 a major redesign of GLUE, version 2.0, was started in the context of the Open Grid Forum following the creation of the GLUE Working Group. This process has taken input from a number of Grid projects, but as a major user of the version 1 schema LCG/EGEE has had a strong interest that the new schema should support its needs. In this paper we discuss the structure of the new schema in the light of the LCG/EGEE requirements and explain how they are met, and where improvements have been achieved compared with the version 1 schema. In particular we consider some difficulties encountered in recent extensions of the use of the version 1 schema to aid resource accounting in LCG, to enable the use of the SRM version 2 storage protocol by the LHC experiments, and to publish information about a wider range of services to improve service discovery. We describe how these can be better met by the new schema, and we also discuss the way in which the transition to the new schema is being managed.

Bayesian inference method for stochastic damage accumulation modeling

International Nuclear Information System (INIS)

Jiang, Xiaomo; Yuan, Yong; Liu, Xian

2013-01-01

Damage accumulation based reliability model plays an increasingly important role in successful realization of condition based maintenance for complicated engineering systems. This paper developed a Bayesian framework to establish stochastic damage accumulation model from historical inspection data, considering data uncertainty. Proportional hazards modeling technique is developed to model the nonlinear effect of multiple influencing factors on system reliability. Different from other hazard modeling techniques such as normal linear regression model, the approach does not require any distribution assumption for the hazard model, and can be applied for a wide variety of distribution models. A Bayesian network is created to represent the nonlinear proportional hazards models and to estimate model parameters by Bayesian inference with Markov Chain Monte Carlo simulation. Both qualitative and quantitative approaches are developed to assess the validity of the established damage accumulation model. Anderson–Darling goodness-of-fit test is employed to perform the normality test, and Box–Cox transformation approach is utilized to convert the non-normality data into normal distribution for hypothesis testing in quantitative model validation. The methodology is illustrated with the seepage data collected from real-world subway tunnels.

Bayesian models of cognition revisited: Setting optimality aside and letting data drive psychological theory.

Science.gov (United States)

Tauber, Sean; Navarro, Daniel J; Perfors, Amy; Steyvers, Mark

2017-07-01

Recent debates in the psychological literature have raised questions about the assumptions that underpin Bayesian models of cognition and what inferences they license about human cognition. In this paper we revisit this topic, arguing that there are 2 qualitatively different ways in which a Bayesian model could be constructed. The most common approach uses a Bayesian model as a normative standard upon which to license a claim about optimality. In the alternative approach, a descriptive Bayesian model need not correspond to any claim that the underlying cognition is optimal or rational, and is used solely as a tool for instantiating a substantive psychological theory. We present 3 case studies in which these 2 perspectives lead to different computational models and license different conclusions about human cognition. We demonstrate how the descriptive Bayesian approach can be used to answer different sorts of questions than the optimal approach, especially when combined with principled tools for model evaluation and model selection. More generally we argue for the importance of making a clear distinction between the 2 perspectives. Considerable confusion results when descriptive models and optimal models are conflated, and if Bayesians are to avoid contributing to this confusion it is important to avoid making normative claims when none are intended. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Measurement of the Z and W production cross section in pp collisions at LHC using a bayesian approach

CERN Document Server

Ragoni, Simone

The aim of all my work has been to compute the fiducial production cross sections of W± and Z0 bosons in their leptonic (e, µ) decays using the data collected by the ATLAS detector at LHC with a center of mass energy of √s = 13 TeV during summer 2015. The selected events are exactly the same as the ones employed by the recently published article by the ATLAS Collaboration over the same topic, enabling us to compare the obtained results. Necessary comparison, if I may, for the results were obtained with two different procedures: baseline (classical) for the article, bayesian in this thesis. The bayesian approach allows for a natural combination among the many channels and a straightforward treatment of the systematic uncertainties. The obtained results are in excellent agreement with the Standard Model predictions and those published by ATLAS.

Residual lifetime prediction for lithium-ion battery based on functional principal component analysis and Bayesian approach

International Nuclear Information System (INIS)

Cheng, Yujie; Lu, Chen; Li, Tieying; Tao, Laifa

2015-01-01

Existing methods for predicting lithium-ion (Li-ion) battery residual lifetime mostly depend on a priori knowledge on aging mechanism, the use of chemical or physical formulation and analytical battery models. This dependence is usually difficult to determine in practice, which restricts the application of these methods. In this study, we propose a new prediction method for Li-ion battery residual lifetime evaluation based on FPCA (functional principal component analysis) and Bayesian approach. The proposed method utilizes FPCA to construct a nonparametric degradation model for Li-ion battery, based on which the residual lifetime and the corresponding confidence interval can be evaluated. Furthermore, an empirical Bayes approach is utilized to achieve real-time updating of the degradation model and concurrently determine residual lifetime distribution. Based on Bayesian updating, a more accurate prediction result and a more precise confidence interval are obtained. Experiments are implemented based on data provided by the NASA Ames Prognostics Center of Excellence. Results confirm that the proposed prediction method performs well in real-time battery residual lifetime prediction. - Highlights: • Capacity is considered functional and FPCA is utilized to extract more information. • No features required which avoids drawbacks induced by feature extraction. • A good combination of both population and individual information. • Avoiding complex aging mechanism and accurate analytical models of batteries. • Easily applicable to different batteries for life prediction and RLD calculation.

Bayesian inference for Hawkes processes

DEFF Research Database (Denmark)

Rasmussen, Jakob Gulddahl

The Hawkes process is a practically and theoretically important class of point processes, but parameter-estimation for such a process can pose various problems. In this paper we explore and compare two approaches to Bayesian inference. The first approach is based on the so-called conditional...... intensity function, while the second approach is based on an underlying clustering and branching structure in the Hawkes process. For practical use, MCMC (Markov chain Monte Carlo) methods are employed. The two approaches are compared numerically using three examples of the Hawkes process....

Bayesian inference for Hawkes processes

DEFF Research Database (Denmark)

Rasmussen, Jakob Gulddahl

2013-01-01

The Hawkes process is a practically and theoretically important class of point processes, but parameter-estimation for such a process can pose various problems. In this paper we explore and compare two approaches to Bayesian inference. The first approach is based on the so-called conditional...... intensity function, while the second approach is based on an underlying clustering and branching structure in the Hawkes process. For practical use, MCMC (Markov chain Monte Carlo) methods are employed. The two approaches are compared numerically using three examples of the Hawkes process....

Comparison of mechanical compressive properties of commercial and autologous fibrin glues for tissue engineering applications.

Science.gov (United States)

Cravens, Matthew G; Behn, Anthony W; Dragoo, Jason L

2017-11-01

Fibrin glues are widely used in orthopedic surgery as adhesives and hemostatic agents. We evaluated the compressive properties of selected fibrin glues in order to identify which are appropriate for tissue regeneration applications subject to compression. Uniaxial unconfined compression tests were performed on fibrin gels prepared from commercial and autologous products: (1) Evicel (Ethicon), (2) Tisseel (Baxter), (3) Angel (Arthrex), and (4) ProPlaz (Biorich). Cyclic loads were applied from 0 to 30% strain for 100cycles at 0.5Hz. Following cyclic testing, specimens were subjected to ramp displacement of 1% strain per second to 80% strain. Throughout cyclic loading, Evicel and Tisseel deformed (shortened) less than Angel at all but one time point, and deformed less than ProPlaz at cycles 10 and 20. The dynamic moduli, peak stress, and strain energy were significantly greater in Tisseel than all other groups. Evicel displayed significantly greater dynamic moduli, peak stress, and strain energy than Angel and ProPlaz. Following cyclic testing, Tisseel and Evicel were significantly less deformed than Angel. No specimens exhibited gross failure during ramp loading to 80% strain. Ramp loading trends mirrored those of cyclic loading. The tested commercial glues were significantly more resistant to compression than the autologous products. The compressive properties of Tisseel were approximately twice those of Evicel. All preparations displayed moduli multiple orders of magnitude less than that of native articular cartilage. We conclude that in knee surgeries requiring fibrin glue to undergo compression of daily activity, commercial products are preferable to autologous preparations from platelet-poor plasma, though both will deform significantly. Copyright © 2017 Elsevier Ltd. All rights reserved.

Bayesian Sampling using Condition Indicators

DEFF Research Database (Denmark)

Faber, Michael H.; Sørensen, John Dalsgaard

2002-01-01

of condition indicators introduced by Benjamin and Cornell (1970) a Bayesian approach to quality control is formulated. The formulation is then extended to the case where the quality control is based on sampling of indirect information about the condition of the components, i.e. condition indicators...

Bayesian signal processing classical, modern, and particle filtering methods

CERN Document Server

Candy, James V

2016-01-01

This book aims to give readers a unified Bayesian treatment starting from the basics (Baye's rule) to the more advanced (Monte Carlo sampling), evolving to the next-generation model-based techniques (sequential Monte Carlo sampling). This next edition incorporates a new chapter on "Sequential Bayesian Detection," a new section on "Ensemble Kalman Filters" as well as an expansion of Case Studies that detail Bayesian solutions for a variety of applications. These studies illustrate Bayesian approaches to real-world problems incorporating detailed particle filter designs, adaptive particle filters and sequential Bayesian detectors. In addition to these major developments a variety of sections are expanded to "fill-in-the gaps" of the first edition. Here metrics for particle filter (PF) designs with emphasis on classical "sanity testing" lead to ensemble techniques as a basic requirement for performance analysis. The expansion of information theory metrics and their application to PF designs is fully developed an...

A 3D model retrieval approach based on Bayesian networks lightfield descriptor

Science.gov (United States)

Xiao, Qinhan; Li, Yanjun

2009-12-01

A new 3D model retrieval methodology is proposed by exploiting a novel Bayesian networks lightfield descriptor (BNLD). There are two key novelties in our approach: (1) a BN-based method for building lightfield descriptor; and (2) a 3D model retrieval scheme based on the proposed BNLD. To overcome the disadvantages of the existing 3D model retrieval methods, we explore BN for building a new lightfield descriptor. Firstly, 3D model is put into lightfield, about 300 binary-views can be obtained along a sphere, then Fourier descriptors and Zernike moments descriptors can be calculated out from binaryviews. Then shape feature sequence would be learned into a BN model based on BN learning algorithm; Secondly, we propose a new 3D model retrieval method by calculating Kullback-Leibler Divergence (KLD) between BNLDs. Beneficial from the statistical learning, our BNLD is noise robustness as compared to the existing methods. The comparison between our method and the lightfield descriptor-based approach is conducted to demonstrate the effectiveness of our proposed methodology.

Bayesian accounts of covert selective attention: A tutorial review.

Science.gov (United States)

Vincent, Benjamin T

2015-05-01

Decision making and optimal observer models offer an important theoretical approach to the study of covert selective attention. While their probabilistic formulation allows quantitative comparison to human performance, the models can be complex and their insights are not always immediately apparent. Part 1 establishes the theoretical appeal of the Bayesian approach, and introduces the way in which probabilistic approaches can be applied to covert search paradigms. Part 2 presents novel formulations of Bayesian models of 4 important covert attention paradigms, illustrating optimal observer predictions over a range of experimental manipulations. Graphical model notation is used to present models in an accessible way and Supplementary Code is provided to help bridge the gap between model theory and practical implementation. Part 3 reviews a large body of empirical and modelling evidence showing that many experimental phenomena in the domain of covert selective attention are a set of by-products. These effects emerge as the result of observers conducting Bayesian inference with noisy sensory observations, prior expectations, and knowledge of the generative structure of the stimulus environment.

A direct biocombinatorial strategy toward next generation, mussel-glue inspired saltwater adhesives.

Science.gov (United States)

Wilke, Patrick; Helfricht, Nicolas; Mark, Andreas; Papastavrou, Georg; Faivre, Damien; Börner, Hans G

2014-09-10

Biological materials exhibit remarkable, purpose-adapted properties that provide a source of inspiration for designing new materials to meet the requirements of future applications. For instance, marine mussels are able to attach to a broad spectrum of hard surfaces under hostile conditions. Controlling wet-adhesion of synthetic macromolecules by analogue processes promises to strongly impact materials sciences by offering advanced coatings, adhesives, and glues. The de novo design of macromolecules to mimic complex aspects of mussel adhesion still constitutes a challenge. Phage display allows material scientists to design specifically interacting molecules with tailored affinity to material surfaces. Here, we report on the integration of enzymatic processing steps into phage display biopanning to expand the biocombinatorial procedure and enable the direct selection of enzymatically activable peptide adhesion domains. Adsorption isotherms and single molecule force spectroscopy show that those de novo peptides mimic complex aspects of bioadhesion, such as enzymatic activation (by tyrosinase), the switchability from weak to strong binders, and adsorption under hostile saltwater conditions. Furthermore, peptide-poly(ethylene oxide) conjugates are synthesized to generate protective coatings, which possess anti-fouling properties and suppress irreversible interactions with blood-plasma protein cocktails. The extended phage display procedure provides a generic way to non-natural peptide adhesion domains, which not only mimic nature but also improve biological sequence sections extractable from mussel-glue proteins. The de novo peptides manage to combine several tasks in a minimal 12-mer sequence and thus pave the way to overcome major challenges of technical wet glues.

An overview on Approximate Bayesian computation*

Directory of Open Access Journals (Sweden)

Baragatti Meïli

2014-01-01

Full Text Available Approximate Bayesian computation techniques, also called likelihood-free methods, are one of the most satisfactory approach to intractable likelihood problems. This overview presents recent results since its introduction about ten years ago in population genetics.

Bayesian uncertainty analyses of probabilistic risk models

International Nuclear Information System (INIS)

Pulkkinen, U.

1989-01-01

Applications of Bayesian principles to the uncertainty analyses are discussed in the paper. A short review of the most important uncertainties and their causes is provided. An application of the principle of maximum entropy to the determination of Bayesian prior distributions is described. An approach based on so called probabilistic structures is presented in order to develop a method of quantitative evaluation of modelling uncertainties. The method is applied to a small example case. Ideas for application areas for the proposed method are discussed

Probabilistic forecasting and Bayesian data assimilation

CERN Document Server

Reich, Sebastian

2015-01-01

In this book the authors describe the principles and methods behind probabilistic forecasting and Bayesian data assimilation. Instead of focusing on particular application areas, the authors adopt a general dynamical systems approach, with a profusion of low-dimensional, discrete-time numerical examples designed to build intuition about the subject. Part I explains the mathematical framework of ensemble-based probabilistic forecasting and uncertainty quantification. Part II is devoted to Bayesian filtering algorithms, from classical data assimilation algorithms such as the Kalman filter, variational techniques, and sequential Monte Carlo methods, through to more recent developments such as the ensemble Kalman filter and ensemble transform filters. The McKean approach to sequential filtering in combination with coupling of measures serves as a unifying mathematical framework throughout Part II. Assuming only some basic familiarity with probability, this book is an ideal introduction for graduate students in ap...

Exploring the Influence of Neighborhood Characteristics on Burglary Risks: A Bayesian Random Effects Modeling Approach

Directory of Open Access Journals (Sweden)

Hongqiang Liu

2016-06-01

Full Text Available A Bayesian random effects modeling approach was used to examine the influence of neighborhood characteristics on burglary risks in Jianghan District, Wuhan, China. This random effects model is essentially spatial; a spatially structured random effects term and an unstructured random effects term are added to the traditional non-spatial Poisson regression model. Based on social disorganization and routine activity theories, five covariates extracted from the available data at the neighborhood level were used in the modeling. Three regression models were fitted and compared by the deviance information criterion to identify which model best fit our data. A comparison of the results from the three models indicates that the Bayesian random effects model is superior to the non-spatial models in fitting the data and estimating regression coefficients. Our results also show that neighborhoods with above average bar density and department store density have higher burglary risks. Neighborhood-specific burglary risks and posterior probabilities of neighborhoods having a burglary risk greater than 1.0 were mapped, indicating the neighborhoods that should warrant more attention and be prioritized for crime intervention and reduction. Implications and limitations of the study are discussed in our concluding section.

Bayesian Inference Methods for Sparse Channel Estimation

DEFF Research Database (Denmark)

Pedersen, Niels Lovmand

2013-01-01

This thesis deals with sparse Bayesian learning (SBL) with application to radio channel estimation. As opposed to the classical approach for sparse signal representation, we focus on the problem of inferring complex signals. Our investigations within SBL constitute the basis for the development...... of Bayesian inference algorithms for sparse channel estimation. Sparse inference methods aim at finding the sparse representation of a signal given in some overcomplete dictionary of basis vectors. Within this context, one of our main contributions to the field of SBL is a hierarchical representation...... analysis of the complex prior representation, where we show that the ability to induce sparse estimates of a given prior heavily depends on the inference method used and, interestingly, whether real or complex variables are inferred. We also show that the Bayesian estimators derived from the proposed...

Bayesian image restoration, using configurations

DEFF Research Database (Denmark)

Thorarinsdottir, Thordis

configurations are expressed in terms of the mean normal measure of the random set. These probabilities are used as prior probabilities in a Bayesian image restoration approach. Estimation of the remaining parameters in the model is outlined for salt and pepper noise. The inference in the model is discussed...

A Bayesian perspective on some replacement strategies

International Nuclear Information System (INIS)

Mazzuchi, Thomas A.; Soyer, Refik

1996-01-01

In this paper we present a Bayesian decision theoretic approach for determining optimal replacement strategies. This approach enables us to formally incorporate, express, and update our uncertainty when determining optimal replacement strategies. We develop relevant expressions for both the block replacement protocol with minimal repair and the age replacement protocol and illustrate the use of our approach with real data

Quantum Bayesian rule for weak measurements of qubits in superconducting circuit QED

International Nuclear Information System (INIS)

Wang, Peiyue; Qin, Lupei; Li, Xin-Qi

2014-01-01

Compared with the quantum trajectory equation (QTE), the quantum Bayesian approach has the advantage of being more efficient to infer a quantum state under monitoring, based on the integrated output of measurements. For weak measurement of qubits in circuit quantum electrodynamics (cQED), properly accounting for the measurement backaction effects within the Bayesian framework is an important problem of current interest. Elegant work towards this task was carried out by Korotkov in ‘bad-cavity’ and weak-response limits (Korotkov 2011 Quantum Bayesian approach to circuit QED measurement (arXiv:1111.4016)). In the present work, based on insights from the cavity-field states (dynamics) and the help of an effective QTE, we generalize the results of Korotkov to more general system parameters. The obtained Bayesian rule is in full agreement with Korotkov's result in limiting cases and as well holds satisfactory accuracy in non-limiting cases in comparison with the QTE simulations. We expect the proposed Bayesian rule to be useful for future cQED measurement and control experiments. (paper)

A Bayesian Panel Data Approach to Explaining Market Beta Dynamics

NARCIS (Netherlands)

R. Bauer (Rob); M.M.J.E. Cosemans (Mathijs); R. Frehen (Rik); P.C. Schotman (Peter)

2008-01-01

markdownabstractWe characterize the process that drives the market betas of individual stocks by setting up a hierarchical Bayesian panel data model that allows a flexible specification for beta. We show that combining the parametric relationship between betas and conditioning variables specified by

A Bayesian decision approach to rainfall thresholds based flood warning

Directory of Open Access Journals (Sweden)

M. L. V. Martina

2006-01-01

Full Text Available Operational real time flood forecasting systems generally require a hydrological model to run in real time as well as a series of hydro-informatics tools to transform the flood forecast into relatively simple and clear messages to the decision makers involved in flood defense. The scope of this paper is to set forth the possibility of providing flood warnings at given river sections based on the direct comparison of the quantitative precipitation forecast with critical rainfall threshold values, without the need of an on-line real time forecasting system. This approach leads to an extremely simplified alert system to be used by non technical stakeholders and could also be used to supplement the traditional flood forecasting systems in case of system failures. The critical rainfall threshold values, incorporating the soil moisture initial conditions, result from statistical analyses using long hydrological time series combined with a Bayesian utility function minimization. In the paper, results of an application of the proposed methodology to the Sieve river, a tributary of the Arno river in Italy, are given to exemplify its practical applicability.

A Bayesian alternative for multi-objective ecohydrological model specification

Science.gov (United States)

Tang, Yating; Marshall, Lucy; Sharma, Ashish; Ajami, Hoori

2018-01-01

Recent studies have identified the importance of vegetation processes in terrestrial hydrologic systems. Process-based ecohydrological models combine hydrological, physical, biochemical and ecological processes of the catchments, and as such are generally more complex and parametric than conceptual hydrological models. Thus, appropriate calibration objectives and model uncertainty analysis are essential for ecohydrological modeling. In recent years, Bayesian inference has become one of the most popular tools for quantifying the uncertainties in hydrological modeling with the development of Markov chain Monte Carlo (MCMC) techniques. The Bayesian approach offers an appealing alternative to traditional multi-objective hydrologic model calibrations by defining proper prior distributions that can be considered analogous to the ad-hoc weighting often prescribed in multi-objective calibration. Our study aims to develop appropriate prior distributions and likelihood functions that minimize the model uncertainties and bias within a Bayesian ecohydrological modeling framework based on a traditional Pareto-based model calibration technique. In our study, a Pareto-based multi-objective optimization and a formal Bayesian framework are implemented in a conceptual ecohydrological model that combines a hydrological model (HYMOD) and a modified Bucket Grassland Model (BGM). Simulations focused on one objective (streamflow/LAI) and multiple objectives (streamflow and LAI) with different emphasis defined via the prior distribution of the model error parameters. Results show more reliable outputs for both predicted streamflow and LAI using Bayesian multi-objective calibration with specified prior distributions for error parameters based on results from the Pareto front in the ecohydrological modeling. The methodology implemented here provides insight into the usefulness of multiobjective Bayesian calibration for ecohydrologic systems and the importance of appropriate prior

New sampling strategy using a Bayesian approach to assess iohexol clearance in kidney transplant recipients.

Science.gov (United States)

Benz-de Bretagne, I; Le Guellec, C; Halimi, J M; Gatault, P; Barbet, C; Alnajjar, A; Büchler, M; Lebranchu, Y; Andres, Christian Robert; Vourcʼh, P; Blasco, H

2012-06-01

%, and about 6% if the bounds of acceptance were set at ± 15%. This Bayesian approach can help to reduce the number of samples required to calculate GFR using Bröchner-Mortensen formula with good accuracy.

Bayesian image restoration, using configurations

DEFF Research Database (Denmark)

Thorarinsdottir, Thordis Linda

2006-01-01

configurations are expressed in terms of the mean normal measure of the random set. These probabilities are used as prior probabilities in a Bayesian image restoration approach. Estimation of the remaining parameters in the model is outlined for the salt and pepper noise. The inference in the model is discussed...

A unifying probabilistic Bayesian approach to derive electron density from MRI for radiation therapy treatment planning

International Nuclear Information System (INIS)

Gudur, Madhu Sudhan Reddy; Hara, Wendy; Le, Quynh-Thu; Wang, Lei; Xing, Lei; Li, Ruijiang

2014-01-01

MRI significantly improves the accuracy and reliability of target delineation in radiation therapy for certain tumors due to its superior soft tissue contrast compared to CT. A treatment planning process with MRI as the sole imaging modality will eliminate systematic CT/MRI co-registration errors, reduce cost and radiation exposure, and simplify clinical workflow. However, MRI lacks the key electron density information necessary for accurate dose calculation and generating reference images for patient setup. The purpose of this work is to develop a unifying method to derive electron density from standard T1-weighted MRI. We propose to combine both intensity and geometry information into a unifying probabilistic Bayesian framework for electron density mapping. For each voxel, we compute two conditional probability density functions (PDFs) of electron density given its: (1) T1-weighted MRI intensity, and (2) geometry in a reference anatomy, obtained by deformable image registration between the MRI of the atlas and test patient. The two conditional PDFs containing intensity and geometry information are combined into a unifying posterior PDF, whose mean value corresponds to the optimal electron density value under the mean-square error criterion. We evaluated the algorithm’s accuracy of electron density mapping and its ability to detect bone in the head for eight patients, using an additional patient as the atlas or template. Mean absolute HU error between the estimated and true CT, as well as receiver operating characteristics for bone detection (HU > 200) were calculated. The performance was compared with a global intensity approach based on T1 and no density correction (set whole head to water). The proposed technique significantly reduced the errors in electron density estimation, with a mean absolute HU error of 126, compared with 139 for deformable registration (p = 2  ×  10 −4 ), 283 for the intensity approach (p = 2  ×  10 −6 ) and 282

Data assimilation and uncertainty analysis of environmental assessment problems--an application of Stochastic Transfer Function and Generalised Likelihood Uncertainty Estimation techniques

International Nuclear Information System (INIS)

Romanowicz, Renata; Young, Peter C.

2003-01-01

Stochastic Transfer Function (STF) and Generalised Likelihood Uncertainty Estimation (GLUE) techniques are outlined and applied to an environmental problem concerned with marine dose assessment. The goal of both methods in this application is the estimation and prediction of the environmental variables, together with their associated probability distributions. In particular, they are used to estimate the amount of radionuclides transferred to marine biota from a given source: the British Nuclear Fuel Ltd (BNFL) repository plant in Sellafield, UK. The complexity of the processes involved, together with the large dispersion and scarcity of observations regarding radionuclide concentrations in the marine environment, require efficient data assimilation techniques. In this regard, the basic STF methods search for identifiable, linear model structures that capture the maximum amount of information contained in the data with a minimal parameterisation. They can be extended for on-line use, based on recursively updated Bayesian estimation and, although applicable to only constant or time-variable parameter (non-stationary) linear systems in the form used in this paper, they have the potential for application to non-linear systems using recently developed State Dependent Parameter (SDP) non-linear STF models. The GLUE based-methods, on the other hand, formulate the problem of estimation using a more general Bayesian approach, usually without prior statistical identification of the model structure. As a result, they are applicable to almost any linear or non-linear stochastic model, although they are much less efficient both computationally and in their use of the information contained in the observations. As expected in this particular environmental application, it is shown that the STF methods give much narrower confidence limits for the estimates due to their more efficient use of the information contained in the data. Exploiting Monte Carlo Simulation (MCS) analysis

Bayesian analysis of rare events

Energy Technology Data Exchange (ETDEWEB)

Straub, Daniel, E-mail: straub@tum.de; Papaioannou, Iason; Betz, Wolfgang

2016-06-01

In many areas of engineering and science there is an interest in predicting the probability of rare events, in particular in applications related to safety and security. Increasingly, such predictions are made through computer models of physical systems in an uncertainty quantification framework. Additionally, with advances in IT, monitoring and sensor technology, an increasing amount of data on the performance of the systems is collected. This data can be used to reduce uncertainty, improve the probability estimates and consequently enhance the management of rare events and associated risks. Bayesian analysis is the ideal method to include the data into the probabilistic model. It ensures a consistent probabilistic treatment of uncertainty, which is central in the prediction of rare events, where extrapolation from the domain of observation is common. We present a framework for performing Bayesian updating of rare event probabilities, termed BUS. It is based on a reinterpretation of the classical rejection-sampling approach to Bayesian analysis, which enables the use of established methods for estimating probabilities of rare events. By drawing upon these methods, the framework makes use of their computational efficiency. These methods include the First-Order Reliability Method (FORM), tailored importance sampling (IS) methods and Subset Simulation (SuS). In this contribution, we briefly review these methods in the context of the BUS framework and investigate their applicability to Bayesian analysis of rare events in different settings. We find that, for some applications, FORM can be highly efficient and is surprisingly accurate, enabling Bayesian analysis of rare events with just a few model evaluations. In a general setting, BUS implemented through IS and SuS is more robust and flexible.

Assessing offshore emergency evacuation behavior in a virtual environment using a Bayesian Network approach

International Nuclear Information System (INIS)

Musharraf, Mashrura; Smith, Jennifer; Khan, Faisal; Veitch, Brian; MacKinnon, Scott

2016-01-01

In the performance influencing factor (PIF) hierarchy, person-based influencing factors reside in the top level along with machine-based, team-based, organization-based and situation/stressor-based factors. Though person-based PIFs like morale, motivation, and attitude (MMA) play an important role in shaping performance, it is nearly impossible to assess such PIFs directly. However, it is possible to measure behavioral indicators (e.g. compliance, use of information) that can provide insight regarding the state of the unobservable person-based PIFs. One common approach to measuring these indicators is to carry out a self-reported questionnaire survey. Significant work has been done to make such questionnaires reliable, but the potential validity problem associated with any questionnaire is that the data are subjective and thus may bear a limited relationship to reality. This paper describes the use of a virtual environment to measure behavioral indicators, which in turn can be used as proxies to assess otherwise unobservable PIFs like MMA. A Bayesian Network (BN) model is first developed to define the relationship between person-based PIFs and measurable behavioral indicators. The paper then shows how these indicators can be measured using evidence collected from a virtual environment of an offshore petroleum installation. A study that focused on emergency evacuation scenarios was done with 36 participants. The participants were first assessed using a multiple choice test. They were then assessed based on their observed performance during simulated offshore emergency evacuation conditions. A comparison of the two assessments demonstrates the potential benefits and challenges of using virtual environments to assess behavioral indicators, and thus the person-based PIFs. - Highlights: • New approach to use virtual environment as measure of behavioral indicators. • New model to study morale, motivation, and attitude. • Bayesian Network model to define the

Bayesian Plackett-Luce Mixture Models for Partially Ranked Data.

Science.gov (United States)

Mollica, Cristina; Tardella, Luca

2017-06-01

The elicitation of an ordinal judgment on multiple alternatives is often required in many psychological and behavioral experiments to investigate preference/choice orientation of a specific population. The Plackett-Luce model is one of the most popular and frequently applied parametric distributions to analyze rankings of a finite set of items. The present work introduces a Bayesian finite mixture of Plackett-Luce models to account for unobserved sample heterogeneity of partially ranked data. We describe an efficient way to incorporate the latent group structure in the data augmentation approach and the derivation of existing maximum likelihood procedures as special instances of the proposed Bayesian method. Inference can be conducted with the combination of the Expectation-Maximization algorithm for maximum a posteriori estimation and the Gibbs sampling iterative procedure. We additionally investigate several Bayesian criteria for selecting the optimal mixture configuration and describe diagnostic tools for assessing the fitness of ranking distributions conditionally and unconditionally on the number of ranked items. The utility of the novel Bayesian parametric Plackett-Luce mixture for characterizing sample heterogeneity is illustrated with several applications to simulated and real preference ranked data. We compare our method with the frequentist approach and a Bayesian nonparametric mixture model both assuming the Plackett-Luce model as a mixture component. Our analysis on real datasets reveals the importance of an accurate diagnostic check for an appropriate in-depth understanding of the heterogenous nature of the partial ranking data.

Sparse reconstruction using distribution agnostic bayesian matching pursuit

KAUST Repository

Masood, Mudassir

2013-11-01

A fast matching pursuit method using a Bayesian approach is introduced for sparse signal recovery. This method performs Bayesian estimates of sparse signals even when the signal prior is non-Gaussian or unknown. It is agnostic on signal statistics and utilizes a priori statistics of additive noise and the sparsity rate of the signal, which are shown to be easily estimated from data if not available. The method utilizes a greedy approach and order-recursive updates of its metrics to find the most dominant sparse supports to determine the approximate minimum mean-square error (MMSE) estimate of the sparse signal. Simulation results demonstrate the power and robustness of our proposed estimator. © 2013 IEEE.

Bayesian dynamic mediation analysis.

Science.gov (United States)

Huang, Jing; Yuan, Ying

2017-12-01

Most existing methods for mediation analysis assume that mediation is a stationary, time-invariant process, which overlooks the inherently dynamic nature of many human psychological processes and behavioral activities. In this article, we consider mediation as a dynamic process that continuously changes over time. We propose Bayesian multilevel time-varying coefficient models to describe and estimate such dynamic mediation effects. By taking the nonparametric penalized spline approach, the proposed method is flexible and able to accommodate any shape of the relationship between time and mediation effects. Simulation studies show that the proposed method works well and faithfully reflects the true nature of the mediation process. By modeling mediation effect nonparametrically as a continuous function of time, our method provides a valuable tool to help researchers obtain a more complete understanding of the dynamic nature of the mediation process underlying psychological and behavioral phenomena. We also briefly discuss an alternative approach of using dynamic autoregressive mediation model to estimate the dynamic mediation effect. The computer code is provided to implement the proposed Bayesian dynamic mediation analysis. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Detection of multiple damages employing best achievable eigenvectors under Bayesian inference

Science.gov (United States)

Prajapat, Kanta; Ray-Chaudhuri, Samit

2018-05-01

A novel approach is presented in this work to localize simultaneously multiple damaged elements in a structure along with the estimation of damage severity for each of the damaged elements. For detection of damaged elements, a best achievable eigenvector based formulation has been derived. To deal with noisy data, Bayesian inference is employed in the formulation wherein the likelihood of the Bayesian algorithm is formed on the basis of errors between the best achievable eigenvectors and the measured modes. In this approach, the most probable damage locations are evaluated under Bayesian inference by generating combinations of various possible damaged elements. Once damage locations are identified, damage severities are estimated using a Bayesian inference Markov chain Monte Carlo simulation. The efficiency of the proposed approach has been demonstrated by carrying out a numerical study involving a 12-story shear building. It has been found from this study that damage scenarios involving as low as 10% loss of stiffness in multiple elements are accurately determined (localized and severities quantified) even when 2% noise contaminated modal data are utilized. Further, this study introduces a term parameter impact (evaluated based on sensitivity of modal parameters towards structural parameters) to decide the suitability of selecting a particular mode, if some idea about the damaged elements are available. It has been demonstrated here that the accuracy and efficiency of the Bayesian quantification algorithm increases if damage localization is carried out a-priori. An experimental study involving a laboratory scale shear building and different stiffness modification scenarios shows that the proposed approach is efficient enough to localize the stories with stiffness modification.

BAYESIAN IMAGE RESTORATION, USING CONFIGURATIONS

Directory of Open Access Journals (Sweden)

Thordis Linda Thorarinsdottir

2011-05-01

Full Text Available In this paper, we develop a Bayesian procedure for removing noise from images that can be viewed as noisy realisations of random sets in the plane. The procedure utilises recent advances in configuration theory for noise free random sets, where the probabilities of observing the different boundary configurations are expressed in terms of the mean normal measure of the random set. These probabilities are used as prior probabilities in a Bayesian image restoration approach. Estimation of the remaining parameters in the model is outlined for salt and pepper noise. The inference in the model is discussed in detail for 3 X 3 and 5 X 5 configurations and examples of the performance of the procedure are given.

Diagnosis and Reconfiguration using Bayesian Networks: An Electrical Power System Case Study

Science.gov (United States)

Knox, W. Bradley; Mengshoel, Ole

2009-01-01

Automated diagnosis and reconfiguration are important computational techniques that aim to minimize human intervention in autonomous systems. In this paper, we develop novel techniques and models in the context of diagnosis and reconfiguration reasoning using causal Bayesian networks (BNs). We take as starting point a successful diagnostic approach, using a static BN developed for a real-world electrical power system. We discuss in this paper the extension of this diagnostic approach along two dimensions, namely: (i) from a static BN to a dynamic BN; and (ii) from a diagnostic task to a reconfiguration task. More specifically, we discuss the auto-generation of a dynamic Bayesian network from a static Bayesian network. In addition, we discuss subtle, but important, differences between Bayesian networks when used for diagnosis versus reconfiguration. We discuss a novel reconfiguration agent, which models a system causally, including effects of actions through time, using a dynamic Bayesian network. Though the techniques we discuss are general, we demonstrate them in the context of electrical power systems (EPSs) for aircraft and spacecraft. EPSs are vital subsystems on-board aircraft and spacecraft, and many incidents and accidents of these vehicles have been attributed to EPS failures. We discuss a case study that provides initial but promising results for our approach in the setting of electrical power systems.

A Bayesian approach to PET reconstruction using image-modeling Gibbs priors: Implementation and comparison

International Nuclear Information System (INIS)

Chan, M.T.; Herman, G.T.; Levitan, E.

1996-01-01

We demonstrate that (i) classical methods of image reconstruction from projections can be improved upon by considering the output of such a method as a distorted version of the original image and applying a Bayesian approach to estimate from it the original image (based on a model of distortion and on a Gibbs distribution as the prior) and (ii) by selecting an open-quotes image-modelingclose quotes prior distribution (i.e., one which is such that it is likely that a random sample from it shares important characteristics of the images of the application area) one can improve over another Gibbs prior formulated using only pairwise interactions. We illustrate our approach using simulated Positron Emission Tomography (PET) data from realistic brain phantoms. Since algorithm performance ultimately depends on the diagnostic task being performed. we examine a number of different medically relevant figures of merit to give a fair comparison. Based on a training-and-testing evaluation strategy, we demonstrate that statistically significant improvements can be obtained using the proposed approach

Stochastic models with heteroskedasticity: a Bayesian approach for Ibovespa returns - doi: 10.4025/actascitechnol.v35i2.13547

Directory of Open Access Journals (Sweden)

Sandra Cristina de Oliveira

2013-04-01

Full Text Available Current research compares the Bayesian estimates obtained for the parameters of processes of ARCH family with normal and Student’s t distributions for the conditional distribution of the return series. A non-informative prior distribution was adopted and a reparameterization of models under analysis was taken into account to map parameters’ space into real space. The procedure adopts a normal prior distribution for the transformed parameters. The posterior summaries were obtained by Monte Carlo Markov Chain (MCMC simulation methods. The methodology was evaluated by a series of Bovespa Index returns and the predictive ordinate criterion was employed to select the best adjustment model to the data. Results show that, as a rule, the proposed Bayesian approach provides satisfactory estimates and that the GARCH process with Student’s t distribution adjusted better to the data.  

Humidity control and hydrophilic glue coating applied to mounted protein crystals improves X-ray diffraction experiments

International Nuclear Information System (INIS)

Baba, Seiki; Hoshino, Takeshi; Ito, Len; Kumasaka, Takashi

2013-01-01

A new crystal-mounting method has been developed that involves a combination of controlled humid air and polymer glue for crystal coating. This method is particularly useful when applied to fragile protein crystals that are known to be sensitive to subtle changes in their physicochemical environment. Protein crystals are fragile, and it is sometimes difficult to find conditions suitable for handling and cryocooling the crystals before conducting X-ray diffraction experiments. To overcome this issue, a protein crystal-mounting method has been developed that involves a water-soluble polymer and controlled humid air that can adjust the moisture content of a mounted crystal. By coating crystals with polymer glue and exposing them to controlled humid air, the crystals were stable at room temperature and were cryocooled under optimized humidity. Moreover, the glue-coated crystals reproducibly showed gradual transformations of their lattice constants in response to a change in humidity; thus, using this method, a series of isomorphous crystals can be prepared. This technique is valuable when working on fragile protein crystals, including membrane proteins, and will also be useful for multi-crystal data collection

Humidity control and hydrophilic glue coating applied to mounted protein crystals improves X-ray diffraction experiments

Energy Technology Data Exchange (ETDEWEB)

Baba, Seiki; Hoshino, Takeshi; Ito, Len; Kumasaka, Takashi, E-mail: kumasaka@spring8.or.jp [Japan Synchrotron Radiation Research Institute (JASRI/SPring-8), 1-1-1 Kouto, Sayo, Hyogo 679-5198 (Japan)

2013-09-01

A new crystal-mounting method has been developed that involves a combination of controlled humid air and polymer glue for crystal coating. This method is particularly useful when applied to fragile protein crystals that are known to be sensitive to subtle changes in their physicochemical environment. Protein crystals are fragile, and it is sometimes difficult to find conditions suitable for handling and cryocooling the crystals before conducting X-ray diffraction experiments. To overcome this issue, a protein crystal-mounting method has been developed that involves a water-soluble polymer and controlled humid air that can adjust the moisture content of a mounted crystal. By coating crystals with polymer glue and exposing them to controlled humid air, the crystals were stable at room temperature and were cryocooled under optimized humidity. Moreover, the glue-coated crystals reproducibly showed gradual transformations of their lattice constants in response to a change in humidity; thus, using this method, a series of isomorphous crystals can be prepared. This technique is valuable when working on fragile protein crystals, including membrane proteins, and will also be useful for multi-crystal data collection.

Bayesian biostatistics

CERN Document Server

Lesaffre, Emmanuel

2012-01-01

The growth of biostatistics has been phenomenal in recent years and has been marked by considerable technical innovation in both methodology and computational practicality. One area that has experienced significant growth is Bayesian methods. The growing use of Bayesian methodology has taken place partly due to an increasing number of practitioners valuing the Bayesian paradigm as matching that of scientific discovery. In addition, computational advances have allowed for more complex models to be fitted routinely to realistic data sets. Through examples, exercises and a combination of introd

Uncertainty analysis for effluent trading planning using a Bayesian estimation-based simulation-optimization modeling approach.

Science.gov (United States)

Zhang, J L; Li, Y P; Huang, G H; Baetz, B W; Liu, J

2017-06-01

In this study, a Bayesian estimation-based simulation-optimization modeling approach (BESMA) is developed for identifying effluent trading strategies. BESMA incorporates nutrient fate modeling with soil and water assessment tool (SWAT), Bayesian estimation, and probabilistic-possibilistic interval programming with fuzzy random coefficients (PPI-FRC) within a general framework. Based on the water quality protocols provided by SWAT, posterior distributions of parameters can be analyzed through Bayesian estimation; stochastic characteristic of nutrient loading can be investigated which provides the inputs for the decision making. PPI-FRC can address multiple uncertainties in the form of intervals with fuzzy random boundaries and the associated system risk through incorporating the concept of possibility and necessity measures. The possibility and necessity measures are suitable for optimistic and pessimistic decision making, respectively. BESMA is applied to a real case of effluent trading planning in the Xiangxihe watershed, China. A number of decision alternatives can be obtained under different trading ratios and treatment rates. The results can not only facilitate identification of optimal effluent-trading schemes, but also gain insight into the effects of trading ratio and treatment rate on decision making. The results also reveal that decision maker's preference towards risk would affect decision alternatives on trading scheme as well as system benefit. Compared with the conventional optimization methods, it is proved that BESMA is advantageous in (i) dealing with multiple uncertainties associated with randomness and fuzziness in effluent-trading planning within a multi-source, multi-reach and multi-period context; (ii) reflecting uncertainties existing in nutrient transport behaviors to improve the accuracy in water quality prediction; and (iii) supporting pessimistic and optimistic decision making for effluent trading as well as promoting diversity of decision

A Probability-based Evolutionary Algorithm with Mutations to Learn Bayesian Networks

Directory of Open Access Journals (Sweden)

Sho Fukuda

2014-12-01

Full Text Available Bayesian networks are regarded as one of the essential tools to analyze causal relationship between events from data. To learn the structure of highly-reliable Bayesian networks from data as quickly as possible is one of the important problems that several studies have been tried to achieve. In recent years, probability-based evolutionary algorithms have been proposed as a new efficient approach to learn Bayesian networks. In this paper, we target on one of the probability-based evolutionary algorithms called PBIL (Probability-Based Incremental Learning, and propose a new mutation operator. Through performance evaluation, we found that the proposed mutation operator has a good performance in learning Bayesian networks

Parameter Estimation of Structural Equation Modeling Using Bayesian Approach

Directory of Open Access Journals (Sweden)

Dewi Kurnia Sari

2016-05-01

Full Text Available Leadership is a process of influencing, directing or giving an example of employees in order to achieve the objectives of the organization and is a key element in the effectiveness of the organization. In addition to the style of leadership, the success of an organization or company in achieving its objectives can also be influenced by the commitment of the organization. Where organizational commitment is a commitment created by each individual for the betterment of the organization. The purpose of this research is to obtain a model of leadership style and organizational commitment to job satisfaction and employee performance, and determine the factors that influence job satisfaction and employee performance using SEM with Bayesian approach. This research was conducted at Statistics FNI employees in Malang, with 15 people. The result of this study showed that the measurement model, all significant indicators measure each latent variable. Meanwhile in the structural model, it was concluded there are a significant difference between the variables of Leadership Style and Organizational Commitment toward Job Satisfaction directly as well as a significant difference between Job Satisfaction on Employee Performance. As for the influence of Leadership Style and variable Organizational Commitment on Employee Performance directly declared insignificant.

GLUE Based Uncertainty Estimation of Urban Drainage Modeling Using Weather Radar Precipitation Estimates

DEFF Research Database (Denmark)

Nielsen, Jesper Ellerbæk; Thorndahl, Søren Liedtke; Rasmussen, Michael R.

2011-01-01

Distributed weather radar precipitation measurements are used as rainfall input for an urban drainage model, to simulate the runoff from a small catchment of Denmark. It is demonstrated how the Generalized Likelihood Uncertainty Estimation (GLUE) methodology can be implemented and used to estimate...

COBRA: a Bayesian approach to pulsar searching

Science.gov (United States)

Lentati, L.; Champion, D. J.; Kramer, M.; Barr, E.; Torne, P.

2018-02-01

We introduce COBRA, a GPU-accelerated Bayesian analysis package for performing pulsar searching, that uses candidates from traditional search techniques to set the prior used for the periodicity of the source, and performs a blind search in all remaining parameters. COBRA incorporates models for both isolated and accelerated systems, as well as both Keplerian and relativistic binaries, and exploits pulse phase information to combine search epochs coherently, over time, frequency or across multiple telescopes. We demonstrate the efficacy of our approach in a series of simulations that challenge typical search techniques, including highly aliased signals, and relativistic binary systems. In the most extreme case, we simulate an 8 h observation containing 24 orbits of a pulsar in a binary with a 30 M⊙ companion. Even in this scenario we show that we can build up from an initial low-significance candidate, to fully recovering the signal. We also apply the method to survey data of three pulsars from the globular cluster 47Tuc: PSRs J0024-7204D, J0023-7203J and J0024-7204R. This final pulsar is in a 1.6 h binary, the shortest of any pulsar in 47Tuc, and additionally shows significant scintillation. By allowing the amplitude of the source to vary as a function of time, however, we show that we are able to obtain optimal combinations of such noisy data. We also demonstrate the ability of COBRA to perform high-precision pulsar timing directly on the single pulse survey data, and obtain a 95 per cent upper limit on the eccentricity of PSR J0024-7204R of εb < 0.0007.

A systematic review of Bayesian articles in psychology : The last 25 years

OpenAIRE

van de Schoot, Rens; Winter, Sonja; Ryan, Oisín; Zondervan - Zwijnenburg, Mariëlle; Depaoli, Sarah

2017-01-01

Although the statistical tools most often used by researchers in the field of psychology over the last 25 years are based on frequentist statistics, it is often claimed that the alternative Bayesian approach to statistics is gaining in popularity. In the current article, we investigated this claim by performing the very first systematic review of Bayesian psychological articles published between 1990 and 2015 (n = 1,579). We aim to provide a thorough presentation of the role Bayesian statisti...

BUMPER: the Bayesian User-friendly Model for Palaeo-Environmental Reconstruction

Science.gov (United States)

Holden, Phil; Birks, John; Brooks, Steve; Bush, Mark; Hwang, Grace; Matthews-Bird, Frazer; Valencia, Bryan; van Woesik, Robert

2017-04-01

We describe the Bayesian User-friendly Model for Palaeo-Environmental Reconstruction (BUMPER), a Bayesian transfer function for inferring past climate and other environmental variables from microfossil assemblages. The principal motivation for a Bayesian approach is that the palaeoenvironment is treated probabilistically, and can be updated as additional data become available. Bayesian approaches therefore provide a reconstruction-specific quantification of the uncertainty in the data and in the model parameters. BUMPER is fully self-calibrating, straightforward to apply, and computationally fast, requiring 2 seconds to build a 100-taxon model from a 100-site training-set on a standard personal computer. We apply the model's probabilistic framework to generate thousands of artificial training-sets under ideal assumptions. We then use these to demonstrate both the general applicability of the model and the sensitivity of reconstructions to the characteristics of the training-set, considering assemblage richness, taxon tolerances, and the number of training sites. We demonstrate general applicability to real data, considering three different organism types (chironomids, diatoms, pollen) and different reconstructed variables. In all of these applications an identically configured model is used, the only change being the input files that provide the training-set environment and taxon-count data.

Development of dynamic Bayesian models for web application test management

Science.gov (United States)

Azarnova, T. V.; Polukhin, P. V.; Bondarenko, Yu V.; Kashirina, I. L.

2018-03-01

The mathematical apparatus of dynamic Bayesian networks is an effective and technically proven tool that can be used to model complex stochastic dynamic processes. According to the results of the research, mathematical models and methods of dynamic Bayesian networks provide a high coverage of stochastic tasks associated with error testing in multiuser software products operated in a dynamically changing environment. Formalized representation of the discrete test process as a dynamic Bayesian model allows us to organize the logical connection between individual test assets for multiple time slices. This approach gives an opportunity to present testing as a discrete process with set structural components responsible for the generation of test assets. Dynamic Bayesian network-based models allow us to combine in one management area individual units and testing components with different functionalities and a direct influence on each other in the process of comprehensive testing of various groups of computer bugs. The application of the proposed models provides an opportunity to use a consistent approach to formalize test principles and procedures, methods used to treat situational error signs, and methods used to produce analytical conclusions based on test results.

Introduction of a methodology for visualization and graphical interpretation of Bayesian classification models.

Science.gov (United States)

Balfer, Jenny; Bajorath, Jürgen

2014-09-22

Supervised machine learning models are widely used in chemoinformatics, especially for the prediction of new active compounds or targets of known actives. Bayesian classification methods are among the most popular machine learning approaches for the prediction of activity from chemical structure. Much work has focused on predicting structure-activity relationships (SARs) on the basis of experimental training data. By contrast, only a few efforts have thus far been made to rationalize the performance of Bayesian or other supervised machine learning models and better understand why they might succeed or fail. In this study, we introduce an intuitive approach for the visualization and graphical interpretation of naïve Bayesian classification models. Parameters derived during supervised learning are visualized and interactively analyzed to gain insights into model performance and identify features that determine predictions. The methodology is introduced in detail and applied to assess Bayesian modeling efforts and predictions on compound data sets of varying structural complexity. Different classification models and features determining their performance are characterized in detail. A prototypic implementation of the approach is provided.

Competing risk models in reliability systems, a Weibull distribution model with Bayesian analysis approach

International Nuclear Information System (INIS)

Iskandar, Ismed; Gondokaryono, Yudi Satria

2016-01-01

In reliability theory, the most important problem is to determine the reliability of a complex system from the reliability of its components. The weakness of most reliability theories is that the systems are described and explained as simply functioning or failed. In many real situations, the failures may be from many causes depending upon the age and the environment of the system and its components. Another problem in reliability theory is one of estimating the parameters of the assumed failure models. The estimation may be based on data collected over censored or uncensored life tests. In many reliability problems, the failure data are simply quantitatively inadequate, especially in engineering design and maintenance system. The Bayesian analyses are more beneficial than the classical one in such cases. The Bayesian estimation analyses allow us to combine past knowledge or experience in the form of an apriori distribution with life test data to make inferences of the parameter of interest. In this paper, we have investigated the application of the Bayesian estimation analyses to competing risk systems. The cases are limited to the models with independent causes of failure by using the Weibull distribution as our model. A simulation is conducted for this distribution with the objectives of verifying the models and the estimators and investigating the performance of the estimators for varying sample size. The simulation data are analyzed by using Bayesian and the maximum likelihood analyses. The simulation results show that the change of the true of parameter relatively to another will change the value of standard deviation in an opposite direction. For a perfect information on the prior distribution, the estimation methods of the Bayesian analyses are better than those of the maximum likelihood. The sensitivity analyses show some amount of sensitivity over the shifts of the prior locations. They also show the robustness of the Bayesian analysis within the range

Bayesian versus frequentist statistical inference for investigating a one-off cancer cluster reported to a health department

Directory of Open Access Journals (Sweden)

Wills Rachael A

2009-05-01

Full Text Available Abstract Background The problem of silent multiple comparisons is one of the most difficult statistical problems faced by scientists. It is a particular problem for investigating a one-off cancer cluster reported to a health department because any one of hundreds, or possibly thousands, of neighbourhoods, schools, or workplaces could have reported a cluster, which could have been for any one of several types of cancer or any one of several time periods. Methods This paper contrasts the frequentist approach with a Bayesian approach for dealing with silent multiple comparisons in the context of a one-off cluster reported to a health department. Two published cluster investigations were re-analysed using the Dunn-Sidak method to adjust frequentist p-values and confidence intervals for silent multiple comparisons. Bayesian methods were based on the Gamma distribution. Results Bayesian analysis with non-informative priors produced results similar to the frequentist analysis, and suggested that both clusters represented a statistical excess. In the frequentist framework, the statistical significance of both clusters was extremely sensitive to the number of silent multiple comparisons, which can only ever be a subjective "guesstimate". The Bayesian approach is also subjective: whether there is an apparent statistical excess depends on the specified prior. Conclusion In cluster investigations, the frequentist approach is just as subjective as the Bayesian approach, but the Bayesian approach is less ambitious in that it treats the analysis as a synthesis of data and personal judgements (possibly poor ones, rather than objective reality. Bayesian analysis is (arguably a useful tool to support complicated decision-making, because it makes the uncertainty associated with silent multiple comparisons explicit.

Bayesian Probability Theory

Science.gov (United States)

von der Linden, Wolfgang; Dose, Volker; von Toussaint, Udo

2014-06-01

Preface; Part I. Introduction: 1. The meaning of probability; 2. Basic definitions; 3. Bayesian inference; 4. Combinatrics; 5. Random walks; 6. Limit theorems; 7. Continuous distributions; 8. The central limit theorem; 9. Poisson processes and waiting times; Part II. Assigning Probabilities: 10. Transformation invariance; 11. Maximum entropy; 12. Qualified maximum entropy; 13. Global smoothness; Part III. Parameter Estimation: 14. Bayesian parameter estimation; 15. Frequentist parameter estimation; 16. The Cramer-Rao inequality; Part IV. Testing Hypotheses: 17. The Bayesian way; 18. The frequentist way; 19. Sampling distributions; 20. Bayesian vs frequentist hypothesis tests; Part V. Real World Applications: 21. Regression; 22. Inconsistent data; 23. Unrecognized signal contributions; 24. Change point problems; 25. Function estimation; 26. Integral equations; 27. Model selection; 28. Bayesian experimental design; Part VI. Probabilistic Numerical Techniques: 29. Numerical integration; 30. Monte Carlo methods; 31. Nested sampling; Appendixes; References; Index.

Improving the reliability of POD curves in NDI methods using a Bayesian inversion approach for uncertainty quantification

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Ben Abdessalem, A.; Jenson, F.; Calmon, P.

2016-02-01

This contribution provides an example of the possible advantages of adopting a Bayesian inversion approach to uncertainty quantification in nondestructive inspection methods. In such problem, the uncertainty associated to the random parameters is not always known and needs to be characterised from scattering signal measurements. The uncertainties may then correctly propagated in order to determine a reliable probability of detection curve. To this end, we establish a general Bayesian framework based on a non-parametric maximum likelihood function formulation and some priors from expert knowledge. However, the presented inverse problem is time-consuming and computationally intensive. To cope with this difficulty, we replace the real model by a surrogate one in order to speed-up the model evaluation and to make the problem to be computationally feasible for implementation. The least squares support vector regression is adopted as metamodelling technique due to its robustness to deal with non-linear problems. We illustrate the usefulness of this methodology through the control of tube with enclosed defect using ultrasonic inspection method.

Bayesian approach to the assessment of the population-specific risk of inhibitors in hemophilia A patients: a case study.

Science.gov (United States)

Cheng, Ji; Iorio, Alfonso; Marcucci, Maura; Romanov, Vadim; Pullenayegum, Eleanor M; Marshall, John K; Thabane, Lehana

2016-01-01

Developing inhibitors is a rare event during the treatment of hemophilia A. The multifacets and uncertainty surrounding the development of inhibitors further complicate the process of estimating inhibitor rate from the limited data. Bayesian statistical modeling provides a useful tool in generating, enhancing, and exploring the evidence through incorporating all the available information. We built our Bayesian analysis using three study cases to estimate the inhibitor rates of patients with hemophilia A in three different scenarios: Case 1, a single cohort of previously treated patients (PTPs) or previously untreated patients; Case 2, a meta-analysis of PTP cohorts; and Case 3, a previously unexplored patient population - patients with baseline low-titer inhibitor or history of inhibitor development. The data used in this study were extracted from three published ADVATE (antihemophilic factor [recombinant] is a product of Baxter for treating hemophilia A) post-authorization surveillance studies. Noninformative and informative priors were applied to Bayesian standard (Case 1) or random-effects (Case 2 and Case 3) logistic models. Bayesian probabilities of satisfying three meaningful thresholds of the risk of developing a clinical significant inhibitor (10/100, 5/100 [high rates], and 1/86 [the Food and Drug Administration mandated cutoff rate in PTPs]) were calculated. The effect of discounting prior information or scaling up the study data was evaluated. Results based on noninformative priors were similar to the classical approach. Using priors from PTPs lowered the point estimate and narrowed the 95% credible intervals (Case 1: from 1.3 [0.5, 2.7] to 0.8 [0.5, 1.1]; Case 2: from 1.9 [0.6, 6.0] to 0.8 [0.5, 1.1]; Case 3: 2.3 [0.5, 6.8] to 0.7 [0.5, 1.1]). All probabilities of satisfying a threshold of 1/86 were above 0.65. Increasing the number of patients by two and ten times substantially narrowed the credible intervals for the single cohort study (1.4 [0.7, 2

A Bayesian belief network approach for assessing uncertainty in conceptual site models at contaminated sites

DEFF Research Database (Denmark)

Thomsen, Nanna Isbak; Binning, Philip John; McKnight, Ursula S.

2016-01-01

the most important site-specific features and processes that may affect the contaminant transport behavior at the site. However, the development of a CSM will always be associated with uncertainties due to limited data and lack of understanding of the site conditions. CSM uncertainty is often found...... to be a major source of model error and it should therefore be accounted for when evaluating uncertainties in risk assessments. We present a Bayesian belief network (BBN) approach for constructing CSMs and assessing their uncertainty at contaminated sites. BBNs are graphical probabilistic models...... that are effective for integrating quantitative and qualitative information, and thus can strengthen decisions when empirical data are lacking. The proposed BBN approach facilitates a systematic construction of multiple CSMs, and then determines the belief in each CSM using a variety of data types and/or expert...

A simple Bayesian approach to quantifying confidence level of adverse event incidence proportion in small samples.

Science.gov (United States)

Liu, Fang

2016-01-01

In both clinical development and post-marketing of a new therapy or a new treatment, incidence of an adverse event (AE) is always a concern. When sample sizes are small, large sample-based inferential approaches on an AE incidence proportion in a certain time period no longer apply. In this brief discussion, we introduce a simple Bayesian framework to quantify, in small sample studies and the rare AE case, (1) the confidence level that the incidence proportion of a particular AE p is over or below a threshold, (2) the lower or upper bounds on p with a certain level of confidence, and (3) the minimum required number of patients with an AE before we can be certain that p surpasses a specific threshold, or the maximum allowable number of patients with an AE after which we can no longer be certain that p is below a certain threshold, given a certain confidence level. The method is easy to understand and implement; the interpretation of the results is intuitive. This article also demonstrates the usefulness of simple Bayesian concepts when it comes to answering practical questions.

Bayesian log-periodic model for financial crashes

DEFF Research Database (Denmark)

Rodríguez-Caballero, Carlos Vladimir; Knapik, Oskar

2014-01-01

This paper introduces a Bayesian approach in econophysics literature about financial bubbles in order to estimate the most probable time for a financial crash to occur. To this end, we propose using noninformative prior distributions to obtain posterior distributions. Since these distributions...... cannot be performed analytically, we develop a Markov Chain Monte Carlo algorithm to draw from posterior distributions. We consider three Bayesian models that involve normal and Student’s t-distributions in the disturbances and an AR(1)-GARCH(1,1) structure only within the first case. In the empirical...... part of the study, we analyze a well-known example of financial bubble – the S&P 500 1987 crash – to show the usefulness of the three methods under consideration and crashes of Merval-94, Bovespa-97, IPCMX-94, Hang Seng-97 using the simplest method. The novelty of this research is that the Bayesian...

Bayesian component separation: The Planck experience

Science.gov (United States)

Wehus, Ingunn Kathrine; Eriksen, Hans Kristian

2018-05-01

Bayesian component separation techniques have played a central role in the data reduction process of Planck. The most important strength of this approach is its global nature, in which a parametric and physical model is fitted to the data. Such physical modeling allows the user to constrain very general data models, and jointly probe cosmological, astrophysical and instrumental parameters. This approach also supports statistically robust goodness-of-fit tests in terms of data-minus-model residual maps, which are essential for identifying residual systematic effects in the data. The main challenges are high code complexity and computational cost. Whether or not these costs are justified for a given experiment depends on its final uncertainty budget. We therefore predict that the importance of Bayesian component separation techniques is likely to increase with time for intensity mapping experiments, similar to what has happened in the CMB field, as observational techniques mature, and their overall sensitivity improves.

Improved distal distribution of n-butyl cyanoacrylate glue by simultaneous injection of dextrose 5% through the guiding catheter: technical note

International Nuclear Information System (INIS)

Moore, Carolyn; Murphy, Kieran; Gailloud, Philippe

2006-01-01

The use of n-butyl cyanoacrylate (NBCA) as an adhesive agent for embolization of high-flow intracranial and extracranial vascular lesions is well established. To be successful, the embolization of arteriovenous malformations and fistulas must achieve obliteration of the arteriovenous shunts themselves rather than simply occlude the feeders proximal to the lesion. However, the feeders cannot always be negotiated over their entire length. This is often the case with dural arteriovenous fistulas (DAVF), which are usually vascularized by long and intricate meningeal networks. In such situations, NBCA may not be able to reach the lesion itself, rendering the embolization ineffective. We present a new technique that improves distal distribution of NBCA glue. The technique described in this report consists of injecting dextrose 5% through the guiding catheter simultaneously with the superselective injection of NBCA glue into the targeted feeding branch. The technique is illustrated with three cases of posterior fossa DAVF. In the reported cases, flooding the territory of the targeted vessel with non-ionic dextrose 5% allowed deep progression of the glue by delaying contact with ionic substances. Excellent distribution of the NBCA glue reaching the site of the arteriovenous shunts was thus obtained despite suboptimal proximal microcatheter tip positions. (orig.)

Predicting Football Matches Results using Bayesian Networks for English Premier League (EPL)

Science.gov (United States)

Razali, Nazim; Mustapha, Aida; Yatim, Faiz Ahmad; Aziz, Ruhaya Ab

2017-08-01

The issues of modeling asscoiation football prediction model has become increasingly popular in the last few years and many different approaches of prediction models have been proposed with the point of evaluating the attributes that lead a football team to lose, draw or win the match. There are three types of approaches has been considered for predicting football matches results which include statistical approaches, machine learning approaches and Bayesian approaches. Lately, many studies regarding football prediction models has been produced using Bayesian approaches. This paper proposes a Bayesian Networks (BNs) to predict the results of football matches in term of home win (H), away win (A) and draw (D). The English Premier League (EPL) for three seasons of 2010-2011, 2011-2012 and 2012-2013 has been selected and reviewed. K-fold cross validation has been used for testing the accuracy of prediction model. The required information about the football data is sourced from a legitimate site at http://www.football-data.co.uk. BNs achieved predictive accuracy of 75.09% in average across three seasons. It is hoped that the results could be used as the benchmark output for future research in predicting football matches results.

Bayesian approach in the power electric systems study of reliability ...

African Journals Online (AJOL)

Subsequently, Bayesian methodologies are framed in an ampler problem list, based on the definition of an opportune "vector of state" and of a vector describing the system performances, aiming to the definition and the calculation or the estimation of system reliability. The purpose of our work is to establish a useful model ...

Management of an extrasphincteric fistula in an HIV-positive patient by using fibrin glue: a case report with tips and tricks