WorldWideScience

Sample records for 2-stage stochastic cancer

  1. Mode-of-Action Uncertainty for Dual-Mode Carcinogens: A Bounding Approach for Naphthalene-Induced Nasal Tumors in Rats Based on PBPK and 2-Stage Stochastic Cancer Risk Models

    Bogen, K T

    2007-05-11

    pharmacokinetic and 2 harmacokinetic 2-stage stochastic carcinogenesis modeling results all clearly indicate that naphthalene is a DMOA carcinogen. Plausibility bounds on rat rat-tumor tumor-type specific DMOA DMOA-related uncertainty were obtained using a 2-stage model adapted to reflec reflect the empirical link between genotoxic and cytotoxic effects of t the most potent identified genotoxic naphthalene metabolites, 1,2 1,2- and 1,4 1,4-naphthoquinone. Bound Bound-specific 'adjustment' factors were then used to reduce naphthalene risk estimated by linear ex extrapolation (under the default genotoxic MOA assumption), to account for the DMOA trapolation exhibited by this compound.

  2. Mode-of-Action Uncertainty for Dual-Mode Carcinogens:Lower Bounds for Naphthalene-Induced Nasal Tumors in Rats Implied byPBPK and 2-Stage Stochastic Cancer Risk Models

    Bogen, K T

    2007-01-30

    As reflected in the 2005 USEPA Guidelines for Cancer Risk Assessment, some chemical carcinogens may have a site-specific mode of action (MOA) that is dual, involving mutation in addition to cell-killing induced hyperplasia. Although genotoxicity may contribute to increased risk at all doses, the Guidelines imply that for dual MOA (DMOA) carcinogens, judgment be used to compare and assess results obtained using separate ''linear'' (genotoxic) vs. ''nonlinear'' (nongenotoxic) approaches to low-level risk extrapolation. However, the Guidelines allow the latter approach to be used only when evidence is sufficient to parameterize a biologically based model that reliably extrapolates risk to low levels of concern. The Guidelines thus effectively prevent MOA uncertainty from being characterized and addressed when data are insufficient to parameterize such a model, but otherwise clearly support a DMOA. A bounding factor approach--similar to that used in reference dose procedures for classic toxicity endpoints--can address MOA uncertainty in a way that avoids explicit modeling of low-dose risk as a function of administered or internal dose. Even when a ''nonlinear'' toxicokinetic model cannot be fully validated, implications of DMOA uncertainty on low-dose risk may be bounded with reasonable confidence when target tumor types happen to be extremely rare. This concept was illustrated for the rodent carcinogen naphthalene. Bioassay data, supplemental toxicokinetic data, and related physiologically based pharmacokinetic and 2-stage stochastic carcinogenesis modeling results all clearly indicate that naphthalene is a DMOA carcinogen. Plausibility bounds on rat-tumor-type specific DMOA-related uncertainty were obtained using a 2-stage model adapted to reflect the empirical link between genotoxic and cytotoxic effects of the most potent identified genotoxic naphthalene metabolites, 1,2- and 1,4-naphthoquinone. Resulting

  3. Stochastic dynamics of cancer initiation

    Most human cancer types result from the accumulation of multiple genetic and epigenetic alterations in a single cell. Once the first change (or changes) have arisen, tumorigenesis is initiated and the subsequent emergence of additional alterations drives progression to more aggressive and ultimately invasive phenotypes. Elucidation of the dynamics of cancer initiation is of importance for an understanding of tumor evolution and cancer incidence data. In this paper, we develop a novel mathematical framework to study the processes of cancer initiation. Cells at risk of accumulating oncogenic mutations are organized into small compartments of cells and proliferate according to a stochastic process. During each cell division, an (epi)genetic alteration may arise which leads to a random fitness change, drawn from a probability distribution. Cancer is initiated when a cell gains a fitness sufficiently high to escape from the homeostatic mechanisms of the cell compartment. To investigate cancer initiation during a human lifetime, a 'race' between this fitness process and the aging process of the patient is considered; the latter is modeled as a second stochastic Markov process in an aging dimension. This model allows us to investigate the dynamics of cancer initiation and its dependence on the mutational fitness distribution. Our framework also provides a methodology to assess the effects of different life expectancy distributions on lifetime cancer incidence. We apply this methodology to colorectal tumorigenesis while considering life expectancy data of the US population to inform the dynamics of the aging process. We study how the probability of cancer initiation prior to death, the time until cancer initiation, and the mutational profile of the cancer-initiating cell depends on the shape of the mutational fitness distribution and life expectancy of the population

  4. A stochastic model for immunotherapy of cancer.

    Baar, Martina; Coquille, Loren; Mayer, Hannah; Hölzel, Michael; Rogava, Meri; Tüting, Thomas; Bovier, Anton

    2016-01-01

    We propose an extension of a standard stochastic individual-based model in population dynamics which broadens the range of biological applications. Our primary motivation is modelling of immunotherapy of malignant tumours. In this context the different actors, T-cells, cytokines or cancer cells, are modelled as single particles (individuals) in the stochastic system. The main expansions of the model are distinguishing cancer cells by phenotype and genotype, including environment-dependent phenotypic plasticity that does not affect the genotype, taking into account the effects of therapy and introducing a competition term which lowers the reproduction rate of an individual in addition to the usual term that increases its death rate. We illustrate the new setup by using it to model various phenomena arising in immunotherapy. Our aim is twofold: on the one hand, we show that the interplay of genetic mutations and phenotypic switches on different timescales as well as the occurrence of metastability phenomena raise new mathematical challenges. On the other hand, we argue why understanding purely stochastic events (which cannot be obtained with deterministic models) may help to understand the resistance of tumours to therapeutic approaches and may have non-trivial consequences on tumour treatment protocols. This is supported through numerical simulations. PMID:27063839

  5. Gompertzian stochastic model with delay effect to cervical cancer growth

    In this paper, a Gompertzian stochastic model with time delay is introduced to describe the cervical cancer growth. The parameters values of the mathematical model are estimated via Levenberg-Marquardt optimization method of non-linear least squares. We apply Milstein scheme for solving the stochastic model numerically. The efficiency of mathematical model is measured by comparing the simulated result and the clinical data of cervical cancer growth. Low values of Mean-Square Error (MSE) of Gompertzian stochastic model with delay effect indicate good fits

  6. Gompertzian stochastic model with delay effect to cervical cancer growth

    Mazlan, Mazma Syahidatul Ayuni binti; Rosli, Norhayati binti [Faculty of Industrial Sciences and Technology, Universiti Malaysia Pahang, Lebuhraya Tun Razak, 26300 Gambang, Pahang (Malaysia); Bahar, Arifah [Department of Mathematical Sciences, Faculty of Science, Universiti Teknologi Malaysia, 81310 Johor Bahru, Johor and UTM Centre for Industrial and Applied Mathematics (UTM-CIAM), Universiti Teknologi Malaysia, 81310 Johor Bahru, Johor (Malaysia)

    2015-02-03

    In this paper, a Gompertzian stochastic model with time delay is introduced to describe the cervical cancer growth. The parameters values of the mathematical model are estimated via Levenberg-Marquardt optimization method of non-linear least squares. We apply Milstein scheme for solving the stochastic model numerically. The efficiency of mathematical model is measured by comparing the simulated result and the clinical data of cervical cancer growth. Low values of Mean-Square Error (MSE) of Gompertzian stochastic model with delay effect indicate good fits.

  7. RECENT DEVELOPMENTS ON TESTING IN CANCER RISK: A FRACTAL AND STOCHASTIC GEOMETRY

    M. Stehlík

    2012-01-01

    Full Text Available The aim of this paper is to discuss recent development on testing in cancer risk. Weconsider both area of fractal and stochastic geometry based cancer. We introduce the exactdistributions of the likelihood ratio tests of several recently used tests and discuss their properties.We also show possibility of testing for cancer using some stochastic geometry descriptors. Testsfor some new stochastic models in cancer risk are also given.

  8. STOCHASTIC MODELS FOR OPTIMAL DRUG ADMINISTRATION IN CANCER CHEMOTHERAPY

    Tirupathi Rao. P

    2010-05-01

    Full Text Available Stochastic models play a prominent role in analysis and designing of processes at various places like, biological, physical, medical and other areas. One of the important areas of concentration in medical sciences is developing optimal operating strategies for drug administration in chronic diseases like, Cancer, TB, Leprosy etc. In this paper, we develop a suitable stochastic model for optimal drug administration in cancer chemotherapy problem. The analysis for drug administration period is discussed. The sensitivity of the model with respect to the parameters is also studied with numerical llustrations. Average time for mutant cell duration in the tumor during the drug administration and absence of drug spells are derived. This study is very much useful for health care takers.Developing software and automation of these models will make this work more user friendly.

  9. A 2-Stage Genome-Wide Association Study to Identify Single Nucleotide Polymorphisms Associated With Development of Erectile Dysfunction Following Radiation Therapy for Prostate Cancer

    Purpose: To identify single nucleotide polymorphisms (SNPs) associated with development of erectile dysfunction (ED) among prostate cancer patients treated with radiation therapy. Methods and Materials: A 2-stage genome-wide association study was performed. Patients were split randomly into a stage I discovery cohort (132 cases, 103 controls) and a stage II replication cohort (128 cases, 102 controls). The discovery cohort was genotyped using Affymetrix 6.0 genome-wide arrays. The 940 top ranking SNPs selected from the discovery cohort were genotyped in the replication cohort using Illumina iSelect custom SNP arrays. Results: Twelve SNPs identified in the discovery cohort and validated in the replication cohort were associated with development of ED following radiation therapy (Fisher combined P values 2.1 × 10−5 to 6.2 × 10−4). Notably, these 12 SNPs lie in or near genes involved in erectile function or other normal cellular functions (adhesion and signaling) rather than DNA damage repair. In a multivariable model including nongenetic risk factors, the odds ratios for these SNPs ranged from 1.6 to 5.6 in the pooled cohort. There was a striking relationship between the cumulative number of SNP risk alleles an individual possessed and ED status (Sommers’ D P value = 1.7 × 10−29). A 1-allele increase in cumulative SNP score increased the odds for developing ED by a factor of 2.2 (P value = 2.1 × 10−19). The cumulative SNP score model had a sensitivity of 84% and specificity of 75% for prediction of developing ED at the radiation therapy planning stage. Conclusions: This genome-wide association study identified a set of SNPs that are associated with development of ED following radiation therapy. These candidate genetic predictors warrant more definitive validation in an independent cohort.

  10. A 2-Stage Genome-Wide Association Study to Identify Single Nucleotide Polymorphisms Associated With Development of Erectile Dysfunction Following Radiation Therapy for Prostate Cancer

    Kerns, Sarah L. [Department of Radiation Oncology, Mount Sinai School of Medicine, New York, New York (United States); Departments of Pathology and Genetics, Albert Einstein College of Medicine, Bronx, New York (United States); Stock, Richard [Department of Radiation Oncology, Mount Sinai School of Medicine, New York, New York (United States); Stone, Nelson [Department of Radiation Oncology, Mount Sinai School of Medicine, New York, New York (United States); Department of Urology, Mount Sinai School of Medicine, New York, New York (United States); Buckstein, Michael [Department of Radiation Oncology, Mount Sinai School of Medicine, New York, New York (United States); Shao, Yongzhao [Division of Biostatistics, New York University School of Medicine, New York, New York (United States); Campbell, Christopher [Departments of Pathology and Genetics, Albert Einstein College of Medicine, Bronx, New York (United States); Rath, Lynda [Department of Radiation Oncology, Mount Sinai School of Medicine, New York, New York (United States); De Ruysscher, Dirk; Lammering, Guido [Department of Radiation Oncology, Maastricht University Medical Center, Maastricht (Netherlands); Hixson, Rosetta; Cesaretti, Jamie; Terk, Mitchell [Florida Radiation Oncology Group, Jacksonville, Florida (United States); Ostrer, Harry [Departments of Pathology and Genetics, Albert Einstein College of Medicine, Bronx, New York (United States); Rosenstein, Barry S., E-mail: barry.rosenstein@mssm.edu [Department of Radiation Oncology, Mount Sinai School of Medicine, New York, New York (United States); Department of Radiation Oncology, New York University School of Medicine, New York, New York (United States); Departments of Dermatology and Preventive Medicine, Mount Sinai School of Medicine, New York, New York (United States)

    2013-01-01

    Purpose: To identify single nucleotide polymorphisms (SNPs) associated with development of erectile dysfunction (ED) among prostate cancer patients treated with radiation therapy. Methods and Materials: A 2-stage genome-wide association study was performed. Patients were split randomly into a stage I discovery cohort (132 cases, 103 controls) and a stage II replication cohort (128 cases, 102 controls). The discovery cohort was genotyped using Affymetrix 6.0 genome-wide arrays. The 940 top ranking SNPs selected from the discovery cohort were genotyped in the replication cohort using Illumina iSelect custom SNP arrays. Results: Twelve SNPs identified in the discovery cohort and validated in the replication cohort were associated with development of ED following radiation therapy (Fisher combined P values 2.1 Multiplication-Sign 10{sup -5} to 6.2 Multiplication-Sign 10{sup -4}). Notably, these 12 SNPs lie in or near genes involved in erectile function or other normal cellular functions (adhesion and signaling) rather than DNA damage repair. In a multivariable model including nongenetic risk factors, the odds ratios for these SNPs ranged from 1.6 to 5.6 in the pooled cohort. There was a striking relationship between the cumulative number of SNP risk alleles an individual possessed and ED status (Sommers' D P value = 1.7 Multiplication-Sign 10{sup -29}). A 1-allele increase in cumulative SNP score increased the odds for developing ED by a factor of 2.2 (P value = 2.1 Multiplication-Sign 10{sup -19}). The cumulative SNP score model had a sensitivity of 84% and specificity of 75% for prediction of developing ED at the radiation therapy planning stage. Conclusions: This genome-wide association study identified a set of SNPs that are associated with development of ED following radiation therapy. These candidate genetic predictors warrant more definitive validation in an independent cohort.

  11. Dynamics between cancer cell subpopulations reveals a model coordinating with both hierarchical and stochastic concepts.

    Wang, Weikang; Quan, Yi; Fu, Qibin; Liu, Yu; Liang, Ying; Wu, Jingwen; Yang, Gen; Luo, Chunxiong; Ouyang, Qi; Wang, Yugang

    2014-01-01

    Tumors are often heterogeneous in which tumor cells of different phenotypes have distinct properties. For scientific and clinical interests, it is of fundamental importance to understand their properties and the dynamic variations among different phenotypes, specifically under radio- and/or chemo-therapy. Currently there are two controversial models describing tumor heterogeneity, the cancer stem cell (CSC) model and the stochastic model. To clarify the controversy, we measured probabilities of different division types and transitions of cells via in situ immunofluorescence. Based on the experiment data, we constructed a model that combines the CSC with the stochastic concepts, showing the existence of both distinctive CSC subpopulations and the stochastic transitions from NSCCs to CSCs. The results showed that the dynamic variations between CSCs and non-stem cancer cells (NSCCs) can be simulated with the model. Further studies also showed that the model can be used to describe the dynamics of the two subpopulations after radiation treatment. More importantly, analysis demonstrated that the experimental detectable equilibrium CSC proportion can be achieved only when the stochastic transitions from NSCCs to CSCs occur, indicating that tumor heterogeneity may exist in a model coordinating with both the CSC and the stochastic concepts. The mathematic model based on experimental parameters may contribute to a better understanding of the tumor heterogeneity, and provide references on the dynamics of CSC subpopulation during radiotherapy. PMID:24416258

  12. Dynamics between cancer cell subpopulations reveals a model coordinating with both hierarchical and stochastic concepts.

    Weikang Wang

    Full Text Available Tumors are often heterogeneous in which tumor cells of different phenotypes have distinct properties. For scientific and clinical interests, it is of fundamental importance to understand their properties and the dynamic variations among different phenotypes, specifically under radio- and/or chemo-therapy. Currently there are two controversial models describing tumor heterogeneity, the cancer stem cell (CSC model and the stochastic model. To clarify the controversy, we measured probabilities of different division types and transitions of cells via in situ immunofluorescence. Based on the experiment data, we constructed a model that combines the CSC with the stochastic concepts, showing the existence of both distinctive CSC subpopulations and the stochastic transitions from NSCCs to CSCs. The results showed that the dynamic variations between CSCs and non-stem cancer cells (NSCCs can be simulated with the model. Further studies also showed that the model can be used to describe the dynamics of the two subpopulations after radiation treatment. More importantly, analysis demonstrated that the experimental detectable equilibrium CSC proportion can be achieved only when the stochastic transitions from NSCCs to CSCs occur, indicating that tumor heterogeneity may exist in a model coordinating with both the CSC and the stochastic concepts. The mathematic model based on experimental parameters may contribute to a better understanding of the tumor heterogeneity, and provide references on the dynamics of CSC subpopulation during radiotherapy.

  13. Stochastic model for computer simulation of the number of cancer cells and lymphocytes in homogeneous sections of cancer tumors

    Castellanos-Moreno, Arnulfo; Corella-Madueño, Adalberto; Gutiérrez-López, Sergio; Rosas-Burgos, Rodrigo

    2014-01-01

    We deal with a small enough tumor section to consider it homogeneous, such that populations of lymphocytes and cancer cells are independent of spatial coordinates. A stochastic model based in one step processes is developed to take into account natural birth and death rates. Other rates are also introduced to consider medical treatment: natural birth rate of lymphocytes and cancer cells; induced death rate of cancer cells due to self-competition, and other ones caused by the activated lymphocytes acting on cancer cells. Additionally, a death rate of cancer cells due to induced apoptosis is considered. Weakness due to the advance of sickness is considered by introducing a lymphocytes death rate proportional to proliferation of cancer cells. Simulation is developed considering different combinations of the parameters and its values, so that several strategies are taken into account to study the effect of anti-angiogenic drugs as well the self-competition between cancer cells. Immune response, with the presence ...

  14. Dynamics between Cancer Cell Subpopulations Reveals a Model Coordinating with Both Hierarchical and Stochastic Concepts

    Wang, Weikang; Quan, Yi; Fu, Qibin; Liu, Yu; Liang, Ying; Wu, Jingwen; Yang, Gen; Luo, Chunxiong; Ouyang, Qi; Wang, Yugang

    2014-01-01

    Tumors are often heterogeneous in which tumor cells of different phenotypes have distinct properties. For scientific and clinical interests, it is of fundamental importance to understand their properties and the dynamic variations among different phenotypes, specifically under radio- and/or chemo-therapy. Currently there are two controversial models describing tumor heterogeneity, the cancer stem cell (CSC) model and the stochastic model. To clarify the controversy, we measured probabilities ...

  15. Comparing Stochastic Differential Equations and Agent-Based Modelling and Simulation for Early-Stage Cancer

    Figueredo, Grazziela P.; Peer-Olaf Siebers; Owen, Markus R.; Jenna Reps; Uwe Aickelin

    2014-01-01

    There is great potential to be explored regarding the use of agent-based modelling and simulation as an alternative paradigm to investigate early-stage cancer interactions with the immune system. It does not suffer from some limitations of ordinary differential equation models, such as the lack of stochasticity, representation of individual behaviours rather than aggregates and individual memory. In this paper we investigate the potential contribution of agent-based modelling and simulation w...

  16. Stochastic Effects in Computational Biology of Space Radiation Cancer Risk

    Cucinotta, Francis A.; Pluth, Janis; Harper, Jane; O'Neill, Peter

    2007-01-01

    Estimating risk from space radiation poses important questions on the radiobiology of protons and heavy ions. We are considering systems biology models to study radiation induced repair foci (RIRF) at low doses, in which less than one-track on average transverses the cell, and the subsequent DNA damage processing and signal transduction events. Computational approaches for describing protein regulatory networks coupled to DNA and oxidative damage sites include systems of differential equations, stochastic equations, and Monte-Carlo simulations. We review recent developments in the mathematical description of protein regulatory networks and possible approaches to radiation effects simulation. These include robustness, which states that regulatory networks maintain their functions against external and internal perturbations due to compensating properties of redundancy and molecular feedback controls, and modularity, which leads to general theorems for considering molecules that interact through a regulatory mechanism without exchange of matter leading to a block diagonal reduction of the connecting pathways. Identifying rate-limiting steps, robustness, and modularity in pathways perturbed by radiation damage are shown to be valid techniques for reducing large molecular systems to realistic computer simulations. Other techniques studied are the use of steady-state analysis, and the introduction of composite molecules or rate-constants to represent small collections of reactants. Applications of these techniques to describe spatial and temporal distributions of RIRF and cell populations following low dose irradiation are described.

  17. Moderate stem-cell telomere shortening rate postpones cancer onset in a stochastic model

    Holbek, Simon; Bendtsen, Kristian Moss; Juul, Jeppe

    2013-10-01

    Mammalian cells are restricted from proliferating indefinitely. Telomeres at the end of each chromosome are shortened at cell division and when they reach a critical length, the cell will enter permanent cell cycle arrest—a state known as senescence. This mechanism is thought to be tumor suppressing, as it helps prevent precancerous cells from dividing uncontrollably. Stem cells express the enzyme telomerase, which elongates the telomeres, thereby postponing senescence. However, unlike germ cells and most types of cancer cells, stem cells only express telomerase at levels insufficient to fully maintain the length of their telomeres, leading to a slow decline in proliferation potential. It is not yet fully understood how this decline influences the risk of cancer and the longevity of the organism. We here develop a stochastic model to explore the role of telomere dynamics in relation to both senescence and cancer. The model describes the accumulation of cancerous mutations in a multicellular organism and creates a coherent theoretical framework for interpreting the results of several recent experiments on telomerase regulation. We demonstrate that the longest average cancer-free lifespan before cancer onset is obtained when stem cells start with relatively long telomeres that are shortened at a steady rate at cell division. Furthermore, the risk of cancer early in life can be reduced by having a short initial telomere length. Finally, our model suggests that evolution will favor a shorter than optimal average cancer-free lifespan in order to postpone cancer onset until late in life.

  18. Stochastic modeling and experimental analysis of phenotypic switching and survival of cancer cells under stress

    Zamani Dahaj, Seyed Alireza; Kumar, Niraj; Sundaram, Bala; Celli, Jonathan; Kulkarni, Rahul

    The phenotypic heterogeneity of cancer cells is critical to their survival under stress. A significant contribution to heterogeneity of cancer calls derives from the epithelial-mesenchymal transition (EMT), a conserved cellular program that is crucial for embryonic development. Several studies have investigated the role of EMT in growth of early stage tumors into invasive malignancies. Also, EMT has been closely associated with the acquisition of chemoresistance properties in cancer cells. Motivated by these studies, we analyze multi-phenotype stochastic models of the evolution of cancers cell populations under stress. We derive analytical results for time-dependent probability distributions that provide insights into the competing rates underlying phenotypic switching (e.g. during EMT) and the corresponding survival of cancer cells. Experimentally, we evaluate these model-based predictions by imaging human pancreatic cancer cell lines grown with and without cytotoxic agents and measure growth kinetics, survival, morphological changes and (terminal evaluation of) biomarkers with associated epithelial and mesenchymal phenotypes. The results derived suggest approaches for distinguishing between adaptation and selection scenarios for survival in the presence of external stresses.

  19. Stochasticity in Physiologically Based Kinetics Models : implications for cancer risk assessment

    Pery, Alexandre; Bois, Frédéric Y.

    2009-01-01

    International audience In case of low-dose exposure to a substance, its concentration in cells is likely to be stochastic. Assessing the consequences of this stochasticity in toxicological risk assessment requires the coupling of macroscopic dynamics models describing whole-body kinetics with microscopic tools designed to simulate stochasticity. In this article, we propose an approach to approximate stochastic cell concentration of butadiene in the cells of diverse organs. We adapted the d...

  20. A stochastic Markov chain model to describe lung cancer growth and metastasis.

    Paul K Newton

    Full Text Available A stochastic Markov chain model for metastatic progression is developed for primary lung cancer based on a network construction of metastatic sites with dynamics modeled as an ensemble of random walkers on the network. We calculate a transition matrix, with entries (transition probabilities interpreted as random variables, and use it to construct a circular bi-directional network of primary and metastatic locations based on postmortem tissue analysis of 3827 autopsies on untreated patients documenting all primary tumor locations and metastatic sites from this population. The resulting 50 potential metastatic sites are connected by directed edges with distributed weightings, where the site connections and weightings are obtained by calculating the entries of an ensemble of transition matrices so that the steady-state distribution obtained from the long-time limit of the Markov chain dynamical system corresponds to the ensemble metastatic distribution obtained from the autopsy data set. We condition our search for a transition matrix on an initial distribution of metastatic tumors obtained from the data set. Through an iterative numerical search procedure, we adjust the entries of a sequence of approximations until a transition matrix with the correct steady-state is found (up to a numerical threshold. Since this constrained linear optimization problem is underdetermined, we characterize the statistical variance of the ensemble of transition matrices calculated using the means and variances of their singular value distributions as a diagnostic tool. We interpret the ensemble averaged transition probabilities as (approximately normally distributed random variables. The model allows us to simulate and quantify disease progression pathways and timescales of progression from the lung position to other sites and we highlight several key findings based on the model.

  1. Identification problem for stochastic models with application to carcinogenesis, cancer detection and radiation biology

    L. G. Hanin

    2002-01-01

    Full Text Available A general framework for solving identification problem for a broad class of deterministic and stochastic models is discussed. This methodology allows for a unified approach to studying identifiability of various stochastic models arising in biology and medicine including models of spontaneous and induced Carcinogenesis, tumor progression and detection, and randomized hit and target models of irradiated cell survival. A variety of known results on parameter identification for stochastic models is reviewed and several new results are presented with an emphasis on rigorous mathematical development.

  2. Classification of spatial textures in benign and cancerous glandular tissues by stereology and stochastic geometry using artificial neural networks.

    Mattfeldt, T; Gottfried, H; Schmidt, V; Kestler, H A

    2000-05-01

    Stereology and stochastic geometry can be used as auxiliary tools for diagnostic purposes in tumour pathology. Whether first-order parameters or stochastic-geometric functions are more important for the classification of the texture of biological tissues is not known. In the present study, volume and surface area per unit reference volume, the pair correlation function and the centred quadratic contact density function of epithelium were estimated in three case series of benign and malignant lesions of glandular tissues. The information provided by the latter functions was summarized by the total absolute areas between the estimated curves and their horizontal reference lines. These areas are considered as indicators of deviation of the tissue texture from a completely uncorrelated volume process and from the Boolean model with convex grains, respectively. We used both areas and the first-order parameters for the classification of cases using artificial neural networks (ANNs). Learning vector quantization and multilayer feedforward networks with backpropagation were applied as neural paradigms. Applications included distinction between mastopathy and mammary cancer (40 cases), between benign prostatic hyperplasia and prostatic cancer (70 cases) and between chronic pancreatitis and pancreatic cancer (60 cases). The same data sets were also classified with linear discriminant analysis. The stereological estimates in combination with ANNs or discriminant analysis provided high accuracy in the classification of individual cases. The question of which category of estimator is the most informative cannot be answered globally, but must be explored empirically for each specific data set. Using learning vector quantization, better results could often be obtained than by multilayer feedforward networks with backpropagation. PMID:10810010

  3. Stochastic volatility and stochastic leverage

    Veraart, Almut; Veraart, Luitgard A. M.

    2009-01-01

    This paper proposes the new concept of stochastic leverage in stochastic volatility models.Stochastic leverage refers to a stochastic process which replaces the classical constant correlationparameter between the asset return and the stochastic volatility process. We provide a systematictreatment of stochastic leverage and propose to model the stochastic leverage effect explicitly,e.g. by means of a linear transformation of a Jacobi process. Such models are both analyticallytractable and allo...

  4. Stochastic volatility and stochastic leverage

    Veraart, Almut; Veraart, Luitgard A. M.

    models which allow for a stochastic leverage effect: the generalised Heston model and the generalised Barndorff-Nielsen & Shephard model. We investigate the impact of a stochastic leverage effect in the risk neutral world by focusing on implied volatilities generated by option prices derived from our new......This paper proposes the new concept of stochastic leverage in stochastic volatility models. Stochastic leverage refers to a stochastic process which replaces the classical constant correlation parameter between the asset return and the stochastic volatility process. We provide a systematic...... treatment of stochastic leverage and propose to model the stochastic leverage effect explicitly, e.g. by means of a linear transformation of a Jacobi process. Such models are both analytically tractable and allow for a direct economic interpretation. In particular, we propose two new stochastic volatility...

  5. Influences of noise correlation and time delay on stochastic resonance induced by multiplicative signal in a cancer growth system

    This paper investigates the stochastic resonance (SR) phenomenon induced by the multiplicative periodic signal in a cancer growth system with the cross-correlated noises and time delay. To describe the periodic change of the birth rate due to the periodic treatment, a multiplicative periodic signal is added to the system. Under the condition of small delay time, the analytical expression of the signal-to-noise ratio RSNR is derived in the adiabatic limit. By numerical calculation, the effects of the cross-correlation strength λ and the delay time τ on RSNR are respectively discussed. The existence of a peak in the curves of RSNR as a function of the noise intensities indicates the occurrence of the SR phenomenon. It is found that λ and τ play opposite role on the SR phenomenon, i.e., the SR is suppressed by increasing λ whereas it is enhanced with the increase of τ, which is different from the case where the periodic signal is additive. (general)

  6. A stochastic model for tumor geometry evolution during radiation therapy in cervical cancer

    Purpose: To develop mathematical models to predict the evolution of tumor geometry in cervical cancer undergoing radiation therapy. Methods: The authors develop two mathematical models to estimate tumor geometry change: a Markov model and an isomorphic shrinkage model. The Markov model describes tumor evolution by investigating the change in state (either tumor or nontumor) of voxels on the tumor surface. It assumes that the evolution follows a Markov process. Transition probabilities are obtained using maximum likelihood estimation and depend on the states of neighboring voxels. The isomorphic shrinkage model describes tumor shrinkage or growth in terms of layers of voxels on the tumor surface, instead of modeling individual voxels. The two proposed models were applied to data from 29 cervical cancer patients treated at Princess Margaret Cancer Centre and then compared to a constant volume approach. Model performance was measured using sensitivity and specificity. Results: The Markov model outperformed both the isomorphic shrinkage and constant volume models in terms of the trade-off between sensitivity (target coverage) and specificity (normal tissue sparing). Generally, the Markov model achieved a few percentage points in improvement in either sensitivity or specificity compared to the other models. The isomorphic shrinkage model was comparable to the Markov approach under certain parameter settings. Convex tumor shapes were easier to predict. Conclusions: By modeling tumor geometry change at the voxel level using a probabilistic model, improvements in target coverage and normal tissue sparing are possible. Our Markov model is flexible and has tunable parameters to adjust model performance to meet a range of criteria. Such a model may support the development of an adaptive paradigm for radiation therapy of cervical cancer

  7. Stochastic Time

    Ohira, Toru

    2006-01-01

    We present a simple dynamical model to address the question of introducing a stochastic nature in a time variable. This model includes noise in the time variable but not in the "space" variable, which is opposite to the normal description of stochastic dynamics. The notable feature is that these models can induce a "resonance" with varying noise strength in the time variable. Thus, they provide a different mechanism for stochastic resonance, which has been discussed within the normal context ...

  8. Stochastic processes

    Parzen, Emanuel

    2015-01-01

    Well-written and accessible, this classic introduction to stochastic processes and related mathematics is appropriate for advanced undergraduate students of mathematics with a knowledge of calculus and continuous probability theory. The treatment offers examples of the wide variety of empirical phenomena for which stochastic processes provide mathematical models, and it develops the methods of probability model-building.Chapter 1 presents precise definitions of the notions of a random variable and a stochastic process and introduces the Wiener and Poisson processes. Subsequent chapters examine

  9. Evaluation of 2-Stage Injection Technique in Children.

    Sandeep, Valasingam; Kumar, Manikya; Jyostna, P; Duggi, Vijay

    2016-01-01

    Effective pain control during local anesthetic injection is the cornerstone of behavior guidance in pediatric dentistry. The aim of this study was to evaluate the practical efficacy of a 2-stage injection technique in reducing injection pain in children. This was a split-mouth, randomized controlled crossover trial. One hundred cooperative children aged 7 to 13 years in need of bilateral local anesthetic injections (inferior alveolar nerve block, posterior superior alveolar nerve block, or maxillary and mandibular buccal infiltrations) for restorative, endodontic, and extraction treatments were recruited for the study. Children were randomly allocated to receive either the 2-stage injection technique or conventional technique at the first appointment. The other technique was used at the successive visit after 1 week. Subjective and objective evaluation of pain was done using the Wong-Baker FACES Pain Rating Scale (FPS) and Sound Eye Motor (SEM) scale, respectively. The comparison of pain scores was done by Wilcoxon sign-rank test. Both FPS and SEM scores were significantly lower when the 2-stage injection technique of local anesthetic nerve block/infiltration was used compared with the conventional technique. The 2-stage injection technique is a simple and effective means of reducing injection pain in children. PMID:26866405

  10. Stochastic Gravity

    Hu, B. L. (Bei-Lok)

    1999-01-01

    We give a summary of the status of current research in stochastic semiclassical gravity and suggest directions for further investigations. This theory generalizes the semiclassical Einstein equation to an Einstein-Langevin equation with a stochastic source term arising from the fluctuations of the energy-momentum tensor of quantum fields. We mention recent efforts in applying this theory to the study of black hole fluctuations and backreaction problems, linear response of hot flat space, and ...

  11. Stochastic optimization

    Schneider, Johannes J

    2007-01-01

    This book addresses stochastic optimization procedures in a broad manner. The first part offers an overview of relevant optimization philosophies; the second deals with benchmark problems in depth, by applying a selection of optimization procedures. Written primarily with scientists and students from the physical and engineering sciences in mind, this book addresses a larger community of all who wish to learn about stochastic optimization techniques and how to use them.

  12. Application of biomarkers in cancer risk management: evaluation from stochastic clonal evolutionary and dynamic system optimization points of view.

    Xiaohong Li

    2011-02-01

    Full Text Available Aside from primary prevention, early detection remains the most effective way to decrease mortality associated with the majority of solid cancers. Previous cancer screening models are largely based on classification of at-risk populations into three conceptually defined groups (normal, cancer without symptoms, and cancer with symptoms. Unfortunately, this approach has achieved limited successes in reducing cancer mortality. With advances in molecular biology and genomic technologies, many candidate somatic genetic and epigenetic "biomarkers" have been identified as potential predictors of cancer risk. However, none have yet been validated as robust predictors of progression to cancer or shown to reduce cancer mortality. In this Perspective, we first define the necessary and sufficient conditions for precise prediction of future cancer development and early cancer detection within a simple physical model framework. We then evaluate cancer risk prediction and early detection from a dynamic clonal evolution point of view, examining the implications of dynamic clonal evolution of biomarkers and the application of clonal evolution for cancer risk management in clinical practice. Finally, we propose a framework to guide future collaborative research between mathematical modelers and biomarker researchers to design studies to investigate and model dynamic clonal evolution. This approach will allow optimization of available resources for cancer control and intervention timing based on molecular biomarkers in predicting cancer among various risk subsets that dynamically evolve over time.

  13. 2–stage stochastic Runge–Kutta for stochastic delay differential equations

    Rosli, Norhayati; Jusoh Awang, Rahimah [Faculty of Industrial Science and Technology, Universiti Malaysia Pahang, Lebuhraya Tun Razak, 26300, Gambang, Pahang (Malaysia); Bahar, Arifah; Yeak, S. H. [Department of Mathematical Sciences, Faculty of Science, Universiti Teknologi Malaysia, 81310 Johor Bahru, Johor (Malaysia)

    2015-05-15

    This paper proposes a newly developed one-step derivative-free method, that is 2-stage stochastic Runge-Kutta (SRK2) to approximate the solution of stochastic delay differential equations (SDDEs) with a constant time lag, r > 0. General formulation of stochastic Runge-Kutta for SDDEs is introduced and Stratonovich Taylor series expansion for numerical solution of SRK2 is presented. Local truncation error of SRK2 is measured by comparing the Stratonovich Taylor expansion of the exact solution with the computed solution. Numerical experiment is performed to assure the validity of the method in simulating the strong solution of SDDEs.

  14. 2–stage stochastic Runge–Kutta for stochastic delay differential equations

    This paper proposes a newly developed one-step derivative-free method, that is 2-stage stochastic Runge-Kutta (SRK2) to approximate the solution of stochastic delay differential equations (SDDEs) with a constant time lag, r > 0. General formulation of stochastic Runge-Kutta for SDDEs is introduced and Stratonovich Taylor series expansion for numerical solution of SRK2 is presented. Local truncation error of SRK2 is measured by comparing the Stratonovich Taylor expansion of the exact solution with the computed solution. Numerical experiment is performed to assure the validity of the method in simulating the strong solution of SDDEs

  15. Quantum stochastics

    Chang, Mou-Hsiung

    2015-01-01

    The classical probability theory initiated by Kolmogorov and its quantum counterpart, pioneered by von Neumann, were created at about the same time in the 1930s, but development of the quantum theory has trailed far behind. Although highly appealing, the quantum theory has a steep learning curve, requiring tools from both probability and analysis and a facility for combining the two viewpoints. This book is a systematic, self-contained account of the core of quantum probability and quantum stochastic processes for graduate students and researchers. The only assumed background is knowledge of the basic theory of Hilbert spaces, bounded linear operators, and classical Markov processes. From there, the book introduces additional tools from analysis, and then builds the quantum probability framework needed to support applications to quantum control and quantum information and communication. These include quantum noise, quantum stochastic calculus, stochastic quantum differential equations, quantum Markov semigrou...

  16. Stochastic cooling

    Stochastic cooling is the damping of betatron oscillations and momentum spread of a particle beam by a feedback system. In its simplest form, a pickup electrode detects the transverse positions or momenta of particles in a storage ring, and the signal produced is amplified and applied downstream to a kicker. The time delay of the cable and electronics is designed to match the transit time of particles along the arc of the storage ring between the pickup and kicker so that an individual particle receives the amplified version of the signal it produced at the pick-up. If there were only a single particle in the ring, it is obvious that betatron oscillations and momentum offset could be damped. However, in addition to its own signal, a particle receives signals from other beam particles. In the limit of an infinite number of particles, no damping could be achieved; we have Liouville's theorem with constant density of the phase space fluid. For a finite, albeit large number of particles, there remains a residue of the single particle damping which is of practical use in accumulating low phase space density beams of particles such as antiprotons. It was the realization of this fact that led to the invention of stochastic cooling by S. van der Meer in 1968. Since its conception, stochastic cooling has been the subject of much theoretical and experimental work. The earliest experiments were performed at the ISR in 1974, with the subsequent ICE studies firmly establishing the stochastic cooling technique. This work directly led to the design and construction of the Antiproton Accumulator at CERN and the beginnings of p anti p colliding beam physics at the SPS. Experiments in stochastic cooling have been performed at Fermilab in collaboration with LBL, and a design is currently under development for a anti p accumulator for the Tevatron

  17. General considerations of the choice of dose limits, averaging areas and weighting factors for the skin in the light of revised skin cancer risk figures and experimental data on non-stochastic effects

    Recent biological data from man and pig on the non-stochastic effects following exposure with a range of β-emitters are combined with recent epidemiological analyses of skin cancer risks in man to form a basis for suggested improved protection criteria following whole- or partial-body skin exposures. Specific consideration is given to the choice of an organ weighting factor for evaluation of effective dose-equivalent. Since stochastic and non-stochastic end-points involve different cell types at different depths in the skin, the design of an ideal physical dosemeter may depend on the proportion of the body skin exposed and the radiation penetrating power. Possible choices of design parameters for skin dosemeters are discussed. Limitation of skin exposure from small radioactive sources ('hot particles') is addressed using animal data. (author)

  18. Stochastic thermodynamics

    Eichhorn, Ralf; Aurell, Erik

    2014-04-01

    'Stochastic thermodynamics as a conceptual framework combines the stochastic energetics approach introduced a decade ago by Sekimoto [1] with the idea that entropy can consistently be assigned to a single fluctuating trajectory [2]'. This quote, taken from Udo Seifert's [3] 2008 review, nicely summarizes the basic ideas behind stochastic thermodynamics: for small systems, driven by external forces and in contact with a heat bath at a well-defined temperature, stochastic energetics [4] defines the exchanged work and heat along a single fluctuating trajectory and connects them to changes in the internal (system) energy by an energy balance analogous to the first law of thermodynamics. Additionally, providing a consistent definition of trajectory-wise entropy production gives rise to second-law-like relations and forms the basis for a 'stochastic thermodynamics' along individual fluctuating trajectories. In order to construct meaningful concepts of work, heat and entropy production for single trajectories, their definitions are based on the stochastic equations of motion modeling the physical system of interest. Because of this, they are valid even for systems that are prevented from equilibrating with the thermal environment by external driving forces (or other sources of non-equilibrium). In that way, the central notions of equilibrium thermodynamics, such as heat, work and entropy, are consistently extended to the non-equilibrium realm. In the (non-equilibrium) ensemble, the trajectory-wise quantities acquire distributions. General statements derived within stochastic thermodynamics typically refer to properties of these distributions, and are valid in the non-equilibrium regime even beyond the linear response. The extension of statistical mechanics and of exact thermodynamic statements to the non-equilibrium realm has been discussed from the early days of statistical mechanics more than 100 years ago. This debate culminated in the development of linear response

  19. Stochastic Analysis 2010

    Crisan, Dan

    2011-01-01

    "Stochastic Analysis" aims to provide mathematical tools to describe and model high dimensional random systems. Such tools arise in the study of Stochastic Differential Equations and Stochastic Partial Differential Equations, Infinite Dimensional Stochastic Geometry, Random Media and Interacting Particle Systems, Super-processes, Stochastic Filtering, Mathematical Finance, etc. Stochastic Analysis has emerged as a core area of late 20th century Mathematics and is currently undergoing a rapid scientific development. The special volume "Stochastic Analysis 2010" provides a sa

  20. Stochastic volatility

    Shephard, Neil

    2005-01-01

    Stochastic volatility (SV) is the main concept used in the fields of financial economics and mathematical finance to deal with the endemic time-varying volatility and codependence found in financial markets. Such dependence has been known for a long time, early comments include Mandelbrot (1963) and Officer (1973). It was also clear to the founding fathers of modern continuous time finance that homogeneity was an unrealistic if convenient simplification, e.g. Black and Scholes (1972, p. 416) ...

  1. The 2-stage liver transplant: 3 clinical scenarios.

    Gedik, Ender; Bıçakçıoğlu, Murat; Otan, Emrah; İlksen Toprak, Hüseyin; Işık, Burak; Aydın, Cemalettin; Kayaalp, Cüneyt; Yılmaz, Sezai

    2015-04-01

    The main goal of 2-stage liver transplant is to provide time to obtain a new liver source. We describe our experience of 3 patients with 3 different clinical conditions. A 57-year-old man was retransplanted successfully with this technique due to hepatic artery thrombosis. However, a 38-year-old woman with fulminant toxic hepatitis and a 5-year-old-boy with abdominal trauma had poor outcome. This technique could serve as a rescue therapy for liver transplant patients who have toxic liver syndrome or abdominal trauma. These patients required intensive support during long anhepatic states. The transplant team should decide early whether to use this technique before irreversible conditions develop. PMID:25894175

  2. Stochastic Cooling

    Blaskiewicz, M.

    2011-01-01

    Stochastic Cooling was invented by Simon van der Meer and was demonstrated at the CERN ISR and ICE (Initial Cooling Experiment). Operational systems were developed at Fermilab and CERN. A complete theory of cooling of unbunched beams was developed, and was applied at CERN and Fermilab. Several new and existing rings employ coasting beam cooling. Bunched beam cooling was demonstrated in ICE and has been observed in several rings designed for coasting beam cooling. High energy bunched beams have proven more difficult. Signal suppression was achieved in the Tevatron, though operational cooling was not pursued at Fermilab. Longitudinal cooling was achieved in the RHIC collider. More recently a vertical cooling system in RHIC cooled both transverse dimensions via betatron coupling.

  3. STOCHASTIC FLOWS OF MAPPINGS

    2007-01-01

    In this paper, the stochastic flow of mappings generated by a Feller convolution semigroup on a compact metric space is studied. This kind of flow is the generalization of superprocesses of stochastic flows and stochastic diffeomorphism induced by the strong solutions of stochastic differential equations.

  4. Stochastic Averaging and Stochastic Extremum Seeking

    Liu, Shu-Jun

    2012-01-01

    Stochastic Averaging and Stochastic Extremum Seeking develops methods of mathematical analysis inspired by the interest in reverse engineering  and analysis of bacterial  convergence by chemotaxis and to apply similar stochastic optimization techniques in other environments. The first half of the text presents significant advances in stochastic averaging theory, necessitated by the fact that existing theorems are restricted to systems with linear growth, globally exponentially stable average models, vanishing stochastic perturbations, and prevent analysis over infinite time horizon. The second half of the text introduces stochastic extremum seeking algorithms for model-free optimization of systems in real time using stochastic perturbations for estimation of their gradients. Both gradient- and Newton-based algorithms are presented, offering the user the choice between the simplicity of implementation (gradient) and the ability to achieve a known, arbitrary convergence rate (Newton). The design of algorithms...

  5. New stochastic calculus

    Moawia Alghalith

    2012-01-01

    We present new stochastic differential equations, that are more general and simpler than the existing Ito-based stochastic differential equations. As an example, we apply our approach to the investment (portfolio) model.

  6. Stochastic approximation: invited paper

    Lai, Tze Leung

    2003-01-01

    Stochastic approximation, introduced by Robbins and Monro in 1951, has become an important and vibrant subject in optimization, control and signal processing. This paper reviews Robbins' contributions to stochastic approximation and gives an overview of several related developments.

  7. Stochastic tools in turbulence

    Lumey, John L

    2012-01-01

    Stochastic Tools in Turbulence discusses the available mathematical tools to describe stochastic vector fields to solve problems related to these fields. The book deals with the needs of turbulence in relation to stochastic vector fields, particularly, on three-dimensional aspects, linear problems, and stochastic model building. The text describes probability distributions and densities, including Lebesgue integration, conditional probabilities, conditional expectations, statistical independence, lack of correlation. The book also explains the significance of the moments, the properties of the

  8. Stochastic component mode synthesis

    Bah, Mamadou T.; Nair, Prasanth B.; Bhaskar, Atul; Keane, Andy J.

    2003-01-01

    In this paper, a stochastic component mode synthesis method is developed for the dynamic analysis of large-scale structures with parameter uncertainties. The main idea is to represent each component displacement using a subspace spanned by a set of stochastic basis vectors in the same fashion as in stochastic reduced basis methods [1, 2]. These vectors represent however stochastic modes in contrast to the deterministic modes used in conventional substructuring methods [3]. The Craig-Bampton r...

  9. A NOTE ON THE STOCHASTIC ROOTS OF STOCHASTIC MATRICES

    Qi-Ming HE; Eldon GUNN

    2003-01-01

    In this paper, we study the stochastic root matrices of stochastic matrices. All stochastic roots of 2×2 stochastic matrices are found explicitly. A method based on characteristic polynomial of matrix is developed to find all real root matrices that are functions of the original 3×3 matrix, including all possible (function) stochastic root matrices. In addition, we comment on some numerical methods for computing stochastic root matrices of stochastic matrices.

  10. Stochastic Lie group integrators

    Malham, Simon J A

    2007-01-01

    We present Lie group integrators for nonlinear stochastic differential equations with non-commutative vector fields whose solution evolves on a smooth finite dimensional manifold. Given a Lie group action that generates transport along the manifold, we pull back the stochastic flow on the manifold to the Lie group via the action, and subsequently pull back the flow to the corresponding Lie algebra via the exponential map. We construct an approximation to the stochastic flow in the Lie algebra via closed operations and then push back to the Lie group and then to the manifold, thus ensuring our approximation lies in the manifold. We call such schemes stochastic Munthe-Kaas methods after their deterministic counterparts. We also present stochastic Lie group integration schemes based on Castell--Gaines methods. These involve using an underlying ordinary differential integrator to approximate the flow generated by a truncated stochastic exponential Lie series. They become stochastic Lie group integrator schemes if...

  11. Ramsey Stochastic Model via Multistage Stochastic Programming

    Kaňková, Vlasta

    Vol. Part II. České Budějovice: University of South Bohemia in České Budějovice, Faculty of Economy , 2010 - (Houda, M.; Friebelová, J.), s. 328-333 ISBN 978-80-7394-218-2. [28th International Conference on Mathematical Methods in Economics 2010. České Budějovice (CZ), 08.09.2010-10.09.2010] R&D Projects: GA ČR GAP402/10/0956; GA ČR(CZ) GA402/08/0107; GA ČR GAP402/10/1610 Institutional research plan: CEZ:AV0Z10750506 Keywords : Ramsey stochastic model * Multistage stochastic programming * Confidence intervals * Autoregressive sequences * Stability * Empirical estimates Subject RIV: AH - Economics http://library.utia.cas.cz/separaty/2010/E/kankova-ramsey stochastic model via multistage stochastic programming.pdf

  12. A High-Payload Fraction, Pump-Fed, 2-Stage Nano Launch Vehicle Project

    National Aeronautics and Space Administration — Ventions proposes the development of a pump-fed, 2-stage nano launch vehicle for low-cost on-demand placement of cube and nano-satellites into LEO. The proposed...

  13. Stochastic Flutter Analysis

    Verhoosel, C.V.; Gutiérrez, M. A.; Hulshoff, S.J.

    2006-01-01

    The field of fluid-structure interaction is combined with the field of stochastics to perform a stochastic flutter analysis. Various methods to directly incorporate the effects of uncertainties in the flutter analysis are investigated. The panel problem with a supersonic fluid flowing over it is considered as a testcase. The stochastic moments (mean, standard deviation, etc.) of the flutter point are computed by an uncertainty analysis. Sensitivity-based methods are used to determine the stoc...

  14. Fluctuations as stochastic deformation

    Kazinski, P. O.

    2008-04-01

    A notion of stochastic deformation is introduced and the corresponding algebraic deformation procedure is developed. This procedure is analogous to the deformation of an algebra of observables like deformation quantization, but for an imaginary deformation parameter (the Planck constant). This method is demonstrated on diverse relativistic and nonrelativistic models with finite and infinite degrees of freedom. It is shown that under stochastic deformation the model of a nonrelativistic particle interacting with the electromagnetic field on a curved background passes into the stochastic model described by the Fokker-Planck equation with the diffusion tensor being the inverse metric tensor. The first stochastic correction to the Newton equations for this system is found. The Klein-Kramers equation is also derived as the stochastic deformation of a certain classical model. Relativistic generalizations of the Fokker-Planck and Klein-Kramers equations are obtained by applying the procedure of stochastic deformation to appropriate relativistic classical models. The analog of the Fokker-Planck equation associated with the stochastic Lorentz-Dirac equation is derived too. The stochastic deformation of the models of a free scalar field and an electromagnetic field is investigated. It turns out that in the latter case the obtained stochastic model describes a fluctuating electromagnetic field in a transparent medium.

  15. A Stochastic Employment Problem

    Wu, Teng

    2013-01-01

    The Stochastic Employment Problem(SEP) is a variation of the Stochastic Assignment Problem which analyzes the scenario that one assigns balls into boxes. Balls arrive sequentially with each one having a binary vector X = (X[subscript 1], X[subscript 2],...,X[subscript n]) attached, with the interpretation being that if X[subscript i] = 1 the ball…

  16. Stochastic Convection Parameterizations

    Teixeira, Joao; Reynolds, Carolyn; Suselj, Kay; Matheou, Georgios

    2012-01-01

    computational fluid dynamics, radiation, clouds, turbulence, convection, gravity waves, surface interaction, radiation interaction, cloud and aerosol microphysics, complexity (vegetation, biogeochemistry, radiation versus turbulence/convection stochastic approach, non-linearities, Monte Carlo, high resolutions, large-Eddy Simulations, cloud structure, plumes, saturation in tropics, forecasting, parameterizations, stochastic, radiation-clod interaction, hurricane forecasts

  17. Stochastic Market Efficiency

    Ole Peters; Alexander Adamou

    2011-01-01

    It is argued that the simple trading strategy of leveraging or deleveraging an investment in the market portfolio cannot outperform the market. Such stochastic market efficiency places strong constraints on the possible stochastic properties of the market. Historical data confirm the hypothesis.

  18. Stochastic and non-stochastic radiation effects

    Both the carcinogenic and the mutagenic effects of ionizing radiation are thought to be induced by 'stochastic' mechanisms of action. It is generally accepted that the number of carcinogenic injury is proportional to the radiation dose applied, and that there is no direct relationship between radiation dose and severity of induced injury, so that no threshold dose can be defined. However, the severity of mutagenic effects, resulting for example from cell death or leading to functional disorders or malformations, has been observed to be a function of the radiation dose, so that in principle threshold doses can be defined. These latter effects are called non-stochastic radiation effects. (orig./DG)

  19. Stochastic neuron models

    Greenwood, Priscilla E

    2016-01-01

    This book describes a large number of open problems in the theory of stochastic neural systems, with the aim of enticing probabilists to work on them. This includes problems arising from stochastic models of individual neurons as well as those arising from stochastic models of the activities of small and large networks of interconnected neurons. The necessary neuroscience background to these problems is outlined within the text, so readers can grasp the context in which they arise. This book will be useful for graduate students and instructors providing material and references for applying probability to stochastic neuron modeling. Methods and results are presented, but the emphasis is on questions where additional stochastic analysis may contribute neuroscience insight. An extensive bibliography is included. Dr. Priscilla E. Greenwood is a Professor Emerita in the Department of Mathematics at the University of British Columbia. Dr. Lawrence M. Ward is a Professor in the Department of Psychology and the Brain...

  20. Stochastic quantization and gravity

    We give a preliminary account of the application of stochastic quantization to the gravitational field. We start in Section I from Nelson's formulation of quantum mechanics as Newtonian stochastic mechanics and only then introduce the Parisi-Wu stochastic quantization scheme on which all the later discussion will be based. In Section II we present a generalization of the scheme that is applicable to fields in physical (i.e. Lorentzian) space-time and treat the free linearized gravitational field in this manner. The most remarkable result of this is the noncausal propagation of conformal gravitons. Moreover the concept of stochastic gauge-fixing is introduced and a complete discussion of all the covariant gauges is given. A special symmetry relating two classes of covariant gauges is exhibited. Finally Section III contains some preliminary remarks on full nonlinear gravity. In particular we argue that in contrast to gauge fields the stochastic gravitational field cannot be transformed to a Gaussian process. (Author)

  1. Stochastic volatility selected readings

    Shephard, Neil

    2005-01-01

    Neil Shephard has brought together a set of classic and central papers that have contributed to our understanding of financial volatility. They cover stocks, bonds and currencies and range from 1973 up to 2001. Shephard, a leading researcher in the field, provides a substantial introduction in which he discusses all major issues involved. General Introduction N. Shephard. Part I: Model Building. 1. A Subordinated Stochastic Process Model with Finite Variance for Speculative Prices, (P. K. Clark). 2. Financial Returns Modelled by the Product of Two Stochastic Processes: A Study of Daily Sugar Prices, 1961-7, S. J. Taylor. 3. The Behavior of Random Variables with Nonstationary Variance and the Distribution of Security Prices, B. Rosenberg. 4. The Pricing of Options on Assets with Stochastic Volatilities, J. Hull and A. White. 5. The Dynamics of Exchange Rate Volatility: A Multivariate Latent Factor ARCH Model, F. X. Diebold and M. Nerlove. 6. Multivariate Stochastic Variance Models. 7. Stochastic Autoregressive...

  2. Stochastic Processes in Electrochemistry.

    Singh, Pradyumna S; Lemay, Serge G

    2016-05-17

    Stochastic behavior becomes an increasingly dominant characteristic of electrochemical systems as we probe them on the smallest scales. Advances in the tools and techniques of nanoelectrochemistry dictate that stochastic phenomena will become more widely manifest in the future. In this Perspective, we outline the conceptual tools that are required to analyze and understand this behavior. We draw on examples from several specific electrochemical systems where important information is encoded in, and can be derived from, apparently random signals. This Perspective attempts to serve as an accessible introduction to understanding stochastic phenomena in electrochemical systems and outlines why they cannot be understood with conventional macroscopic descriptions. PMID:27120701

  3. Stochastic Schroedinger equations

    A derivation of Belavkin's stochastic Schroedinger equations is given using quantum filtering theory. We study an open system in contact with its environment, the electromagnetic field. Continuous observation of the field yields information on the system: it is possible to keep track in real time of the best estimate of the system's quantum state given the observations made. This estimate satisfies a stochastic Schroedinger equation, which can be derived from the quantum stochastic differential equation for the interaction picture evolution of system and field together. Throughout the paper we focus on the basic example of resonance fluorescence

  4. Sequential stochastic optimization

    Cairoli, Renzo

    1996-01-01

    Sequential Stochastic Optimization provides mathematicians and applied researchers with a well-developed framework in which stochastic optimization problems can be formulated and solved. Offering much material that is either new or has never before appeared in book form, it lucidly presents a unified theory of optimal stopping and optimal sequential control of stochastic processes. This book has been carefully organized so that little prior knowledge of the subject is assumed; its only prerequisites are a standard graduate course in probability theory and some familiarity with discrete-paramet

  5. Stochastic Physicochemical Dynamics

    Tsekov, Roumen

    2001-01-01

    The monograph considers thermodynamic relaxation in quantum systems, stochastic dynamics of gas molecules, fluctuation stability of thin liquid films, resonant diffusion in modulated solid structures and catalytic kinetics of chemical dissociation.

  6. Stochastic calculus with infinitesimals

    Herzberg, Frederik

    2013-01-01

    Stochastic analysis is not only a thriving area of pure mathematics with intriguing connections to partial differential equations and differential geometry. It also has numerous applications in the natural and social sciences (for instance in financial mathematics or theoretical quantum mechanics) and therefore appears in physics and economics curricula as well. However, existing approaches to stochastic analysis either presuppose various concepts from measure theory and functional analysis or lack full mathematical rigour. This short book proposes to solve the dilemma: By adopting E. Nelson's "radically elementary" theory of continuous-time stochastic processes, it is based on a demonstrably consistent use of infinitesimals and thus permits a radically simplified, yet perfectly rigorous approach to stochastic calculus and its fascinating applications, some of which (notably the Black-Scholes theory of option pricing and the Feynman path integral) are also discussed in the book.

  7. Stochastic processes inference theory

    Rao, Malempati M

    2014-01-01

    This is the revised and enlarged 2nd edition of the authors’ original text, which was intended to be a modest complement to Grenander's fundamental memoir on stochastic processes and related inference theory. The present volume gives a substantial account of regression analysis, both for stochastic processes and measures, and includes recent material on Ridge regression with some unexpected applications, for example in econometrics. The first three chapters can be used for a quarter or semester graduate course on inference on stochastic processes. The remaining chapters provide more advanced material on stochastic analysis suitable for graduate seminars and discussions, leading to dissertation or research work. In general, the book will be of interest to researchers in probability theory, mathematical statistics and electrical and information theory.

  8. Dynamics of Double Stochastic Operators

    Saburov, Mansoor

    2016-03-01

    A double stochastic operator is a generalization of a double stochastic matrix. In this paper, we study the dynamics of double stochastic operators. We give a criterion for a regularity of a double stochastic operator in terms of absences of its periodic points. We provide some examples to insure that, in general, a trajectory of a double stochastic operator may converge to any interior point of the simplex.

  9. Stochastic differential equations and applications

    Friedman, Avner

    2006-01-01

    This text develops the theory of systems of stochastic differential equations, and it presents applications in probability, partial differential equations, and stochastic control problems. Originally published in two volumes, it combines a book of basic theory and selected topics with a book of applications.The first part explores Markov processes and Brownian motion; the stochastic integral and stochastic differential equations; elliptic and parabolic partial differential equations and their relations to stochastic differential equations; the Cameron-Martin-Girsanov theorem; and asymptotic es

  10. BRST stochastic quantization

    After a brief review of the BRST formalism and of the Parisi-Wu stochastic quantization method we introduce the BRST stochastic quantization scheme. It allows the second quantization of constrained Hamiltonian systems in a manifestly gauge symmetry preserving way. The examples of the relativistic particle, the spinning particle and the bosonic string are worked out in detail. The paper is closed by a discussion on the interacting field theory associated to the relativistic point particle system. 58 refs. (Author)

  11. What is Stochastic Independence?

    Franz, Uwe

    2002-01-01

    The notion of a tensor product with projections or with inclusions is defined. It is shown that the definition of stochastic independence relies on such a structure and that independence can be defined in an arbitrary category with a tensor product with inclusions or projections. In this context, the classifications of quantum stochastic independence by Muraki, Ben Ghorbal, and Sch\\"urmann become classifications of the tensor products with inclusions for the categories of algebraic probabilit...

  12. Stochastic Processes in Finance

    Madan, Dilip B.

    2010-01-01

    Stochastic processes arising in the description of the risk-neutral evolution of equity prices are reviewed. Starting with Brownian motion, I review extensions to Lévy and Sato processes. These processes have independent increments; the former are homogeneous in time, whereas the latter are inhomogeneous. One-dimensional Markov processes such as local volatility and local Lévy are discussed next. Finally, I take up two forms of stochastic volatility that are due to either space scaling or tim...

  13. Transport in Stochastic Media

    Classical transport of neutral particles in a binary, scattering, stochastic media is discussed. It is assumed that the cross-sections of the constituent materials and their volume fractions are known. The inner structure of the media is stochastic, but there exist a statistical knowledge about the lump sizes, shapes and arrangement. The transmission through the composite media depends on the specific heterogeneous realization of the media. The current research focuses on the averaged transmission through an ensemble of realizations, frm which an effective cross-section for the media can be derived. The problem of one dimensional transport in stochastic media has been studied extensively [1]. In the one dimensional description of the problem, particles are transported along a line populated with alternating material segments of random lengths. The current work discusses transport in two-dimensional stochastic media. The phenomenon that is unique to the multi-dimensional description of the problem is obstacle bypassing. Obstacle bypassing tends to reduce the opacity of the media, thereby reducing its effective cross-section. The importance of this phenomenon depends on the manner in which the obstacles are arranged in the media. Results of transport simulations in multi-dimensional stochastic media are presented. Effective cross-sections derived from the simulations are compared against those obtained for the one-dimensional problem, and against those obtained from effective multi-dimensional models, which are partially based on a Markovian assumption

  14. Quantum Spontaneous Stochasticity

    Eyink, Gregory L

    2015-01-01

    The quantum wave-function of a massive particle with small initial uncertainties (consistent with the uncertainty relation) is believed to spread very slowly, so that the dynamics is deterministic. This assumes that the classical motions for given initial data are unique. In fluid turbulence non-uniqueness due to "roughness" of the advecting velocity field is known to lead to stochastic motion of classical particles. Vanishingly small random perturbations are magnified by Richardson diffusion in a "nearly rough" velocity field so that motion remains stochastic as the noise disappears, or classical spontaneous stochasticity, . Analogies between stochastic particle motion in turbulence and quantum evolution suggest that there should be quantum spontaneous stochasticity (QSS). We show this for 1D models of a particle in a repulsive potential that is "nearly rough" with $V(x) \\sim C|x|^{1+\\alpha}$ at distances $|x|\\gg \\ell$ , for some UV cut-off $\\ell$, and for initial Gaussian wave-packet centered at 0. We consi...

  15. Study on the Effects of End-bend Cantilevered Stator in a 2-stage Axial Compressor

    Songtao WANG; Xin DU; Zhongqi WANG

    2009-01-01

    Leading edge recambering is applied to the cantilevered stator vanes in a 2-stage compressor in this paper. Dif-ferent curving effects are produced when the end-bend stator vanes are stacked in different ways. Stacking on the leading edge induces a positive curving effect near the casing.When it is stacked on the centre of gravity, a nega-tive curving effect takes place. The numerical investigation shows that the flow field is redistributed when the end-bend stators with leading edge stacking are applied. The variations in the stage matching for the mainstream and near the hub have an impact on the performance of the 2-stage compressor. The isentropic efficiency and the total pressure ratio of the compressor are increased near the design condition. The compressor total pressure ratio is decreased near choke and near stall. The maximum flow rate is reduced and the stall margin is decreased.

  16. Stochastic dynamics and irreversibility

    Tomé, Tânia

    2015-01-01

    This textbook presents an exposition of stochastic dynamics and irreversibility. It comprises the principles of probability theory and the stochastic dynamics in continuous spaces, described by Langevin and Fokker-Planck equations, and in discrete spaces, described by Markov chains and master equations. Special concern is given to the study of irreversibility, both in systems that evolve to equilibrium and in nonequilibrium stationary states. Attention is also given to the study of models displaying phase transitions and critical phenomema both in thermodynamic equilibrium and out of equilibrium. These models include the linear Glauber model, the Glauber-Ising model, lattice models with absorbing states such as the contact process and those used in population dynamic and spreading of epidemic, probabilistic cellular automata, reaction-diffusion processes, random sequential adsorption and dynamic percolation. A stochastic approach to chemical reaction is also presented.The textbook is intended for students of ...

  17. Stochastic modelling of turbulence

    Sørensen, Emil Hedevang Lohse

    stochastic turbulence model based on ambit processes is proposed. It is shown how a prescribed isotropic covariance structure can be reproduced. Non-Gaussian turbulence models are obtained through non-Gaussian Lévy bases or through volatility modulation of Lévy bases. As opposed to spectral models operating......This thesis addresses stochastic modelling of turbulence with applications to wind energy in mind. The primary tool is ambit processes, a recently developed class of computationally tractable stochastic processes based on integration with respect to Lévy bases. The subject of ambit processes is...... still undergoing rapid development. Turbulence and wind energy are vast and complicated subjects. Turbulence has structures across a wide range of length and time scales, structures which cannot be captured by a Gaussian process that relies on only second order properties. Concerning wind energy, a wind...

  18. Stochastic Geometric Wave Equations

    Brzezniak, Z.; Ondreját, Martin

    Cham: Springer, 2015, s. 157-188. (Progress in Probability. 68). ISBN 978-3-0348-0908-5. ISSN 1050-6977. [Stochastic analysis and applications at the Centre Interfacultaire Bernoulli, Ecole Polytechnique Fédérale de Lausanne. Lausanne (CH), 09.01.2012-29.6.2012] R&D Projects: GA ČR GAP201/10/0752 Institutional research plan: CEZ:AV0Z10750506 Institutional support: RVO:67985556 Keywords : Stochastic wave equation * Riemannian manifold * homogeneous space Subject RIV: BA - General Mathematics http://library.utia.cas.cz/separaty/2015/SI/ondrejat-0447803.pdf

  19. Stochastic dynamics and control

    Sun, Jian-Qiao; Zaslavsky, George

    2006-01-01

    This book is a result of many years of author's research and teaching on random vibration and control. It was used as lecture notes for a graduate course. It provides a systematic review of theory of probability, stochastic processes, and stochastic calculus. The feedback control is also reviewed in the book. Random vibration analyses of SDOF, MDOF and continuous structural systems are presented in a pedagogical order. The application of the random vibration theory to reliability and fatigue analysis is also discussed. Recent research results on fatigue analysis of non-Gaussian stress proc

  20. Stochastic Electrochemical Kinetics

    Beruski, O

    2016-01-01

    A model enabling the extension of the Stochastic Simulation Algorithm to electrochemical systems is proposed. The physical justifications and constraints for the derivation of a chemical master equation are provided and discussed. The electrochemical driving forces are included in the mathematical framework, and equations are provided for the associated electric responses. The implementation for potentiostatic and galvanostatic systems is presented, with results pointing out the stochastic nature of the algorithm. The electric responses presented are in line with the expected results from the theory, providing a new tool for the modeling of electrochemical kinetics.

  1. Decentralized stochastic control

    Speyer, J. L.

    1980-01-01

    Decentralized stochastic control is characterized by being decentralized in that the information to one controller is not the same as information to another controller. The system including the information has a stochastic or uncertain component. This complicates the development of decision rules which one determines under the assumption that the system is deterministic. The system is dynamic which means the present decisions affect future system responses and the information in the system. This circumstance presents a complex problem where tools like dynamic programming are no longer applicable. These difficulties are discussed from an intuitive viewpoint. Particular assumptions are introduced which allow a limited theory which produces mechanizable affine decision rules.

  2. Stochastic models, estimation, and control

    Maybeck, Peter S

    1982-01-01

    This volume builds upon the foundations set in Volumes 1 and 2. Chapter 13 introduces the basic concepts of stochastic control and dynamic programming as the fundamental means of synthesizing optimal stochastic control laws.

  3. STOCHASTIC COOLING FOR BUNCHED BEAMS.

    BLASKIEWICZ, M.

    2005-05-16

    Problems associated with bunched beam stochastic cooling are reviewed. A longitudinal stochastic cooling system for RHIC is under construction and has been partially commissioned. The state of the system and future plans are discussed.

  4. Stochastic entrainment of a stochastic oscillator.

    Wang, Guanyu; Peskin, Charles S

    2015-11-01

    In this work, we consider a stochastic oscillator described by a discrete-state continuous-time Markov chain, in which the states are arranged in a circle, and there is a constant probability per unit time of jumping from one state to the next in a specified direction around the circle. At each of a sequence of equally spaced times, the oscillator has a specified probability of being reset to a particular state. The focus of this work is the entrainment of the oscillator by this periodic but stochastic stimulus. We consider a distinguished limit, in which (i) the number of states of the oscillator approaches infinity, as does the probability per unit time of jumping from one state to the next, so that the natural mean period of the oscillator remains constant, (ii) the resetting probability approaches zero, and (iii) the period of the resetting signal approaches a multiple, by a ratio of small integers, of the natural mean period of the oscillator. In this distinguished limit, we use analytic and numerical methods to study the extent to which entrainment occurs. PMID:26651734

  5. Stochastic Contraction in Riemannian Metrics

    Pham, Quang-Cuong; Slotine, Jean-Jacques

    2013-01-01

    Stochastic contraction analysis is a recently developed tool for studying the global stability properties of nonlinear stochastic systems, based on a differential analysis of convergence in an appropriate metric. To date, stochastic contraction results and sharp associated performance bounds have been established only in the specialized context of state-independent metrics, which restricts their applicability. This paper extends stochastic contraction analysis to the case of general time- and...

  6. Stochastic quantization: Stabilizing quantum models

    Osterwalder-Schrader positivity is shown to be fulfilled for stochastically quantized lattice gauge theories, spin models and P(phi) interactions bounded from below. Problems arising are discussed. A stochastic equation is derived to stabilize the quantum Einstein gravity. It is shown that the stochastic quantization of the Yang-Mills theory leads to a well-defined semiclassical expansion. (orig.)

  7. Stochastic quantization of Proca field

    We discuss the complications that arise in the application of Nelson's stochastic quantization scheme to classical Proca field. One consistent way to obtain spin-one massive stochastic field is given. It is found that the result of Guerra et al on the connection between ground state stochastic field and the corresponding Euclidean-Markov field extends to the spin-one case. (author)

  8. The stochastic quality calculus

    Zeng, Kebin; Nielson, Flemming; Nielson, Hanne Riis

    We introduce the Stochastic Quality Calculus in order to model and reason about distributed processes that rely on each other in order to achieve their overall behaviour. The calculus supports broadcast communication in a truly concurrent setting. Generally distributed delays are associated with...

  9. Stochastic Control - External Models

    Poulsen, Niels Kjølstad

    2005-01-01

    This note is devoted to control of stochastic systems described in discrete time. We are concerned with external descriptions or transfer function model, where we have a dynamic model for the input output relation only (i.e.. no direct internal information). The methods are based on LTI systems and...

  10. Stochastic nonlinear beam equations

    Brzezniak, Z.; Maslowski, Bohdan; Seidler, Jan

    2005-01-01

    Roč. 132, č. 1 (2005), s. 119-149. ISSN 0178-8051 R&D Projects: GA ČR(CZ) GA201/01/1197 Institutional research plan: CEZ:AV0Z10190503 Keywords : stochastic beam equation * stability Subject RIV: BA - General Mathematics Impact factor: 0.896, year: 2005

  11. The stochastic quality calculus

    Zeng, Kebin; Nielson, Flemming; Nielson, Hanne Riis

    2014-01-01

    We introduce the Stochastic Quality Calculus in order to model and reason about distributed processes that rely on each other in order to achieve their overall behaviour. The calculus supports broadcast communication in a truly concurrent setting. Generally distributed delays are associated...

  12. Elementary stochastic cooling

    Major headings in this review include: proton sources; antiproton production; antiproton sources and Liouville, the role of the Debuncher; transverse stochastic cooling, time domain; the accumulator; frequency domain; pickups and kickers; Fokker-Planck equation; calculation of constants in the Fokker-Planck equation; and beam feedback

  13. Stochastic network calculus

    Jiang, Yuming

    2009-01-01

    Network calculus, a theory dealing with queuing systems found in computer networks, focuses on performance guarantees. This title presents a comprehensive treatment for the stochastic service-guarantee analysis research and provides basic introductory material on the subject, as well as discusses the various researches in the area.

  14. Focus on stochastic thermodynamics

    Van den Broeck, Christian; Sasa, Shin-ichi; Seifert, Udo

    2016-02-01

    We introduce the thirty papers collected in this ‘focus on’ issue. The contributions explore conceptual issues within and around stochastic thermodynamics, use this framework for the theoretical modeling and experimental investigation of specific systems, and provide further perspectives on and for this active field.

  15. Stochastic biophysical modeling of irradiated cells

    Fornalski, Krzysztof Wojciech

    2014-01-01

    The paper presents a computational stochastic model of virtual cells irradiation, based on Quasi-Markov Chain Monte Carlo method and using biophysical input. The model is based on a stochastic tree of probabilities for each cell of the entire colony. Biophysics of the cells is described by probabilities and probability distributions provided as the input. The adaptation of nucleation and catastrophe theories, well known in physics, yields sigmoidal relationships for carcinogenic risk as a function of the irradiation. Adaptive response and bystander effect, incorporated into the model, improves its application. The results show that behavior of virtual cells can be successfully modeled, e.g. cancer transformation, creation of mutations, radioadaptation or radiotherapy. The used methodology makes the model universal and practical for simulations of general processes. Potential biophysical curves and relationships are also widely discussed in the paper. However, the presented theoretical model does not describe ...

  16. Adaptive stochastic cellular automata: Applications

    Qian, S.; Lee, Y. C.; Jones, R. D.; Barnes, C. W.; Flake, G. W.; O'Rourke, M. K.; Lee, K.; Chen, H. H.; Sun, G. Z.; Zhang, Y. Q.; Chen, D.; Giles, C. L.

    1990-09-01

    The stochastic learning cellular automata model has been applied to the problem of controlling unstable systems. Two example unstable systems studied are controlled by an adaptive stochastic cellular automata algorithm with an adaptive critic. The reinforcement learning algorithm and the architecture of the stochastic CA controller are presented. Learning to balance a single pole is discussed in detail. Balancing an inverted double pendulum highlights the power of the stochastic CA approach. The stochastic CA model is compared to conventional adaptive control and artificial neural network approaches.

  17. New 2-stage ion microprobes and a move to higher energies

    Legge, G.J.F.; Dymnikov, A.; Moloney, G.; Saint, A. [Melbourne Univ., Parkville, VIC (Australia). School of Physics; Cohen, D. [Australian Nuclear Science and Technology Organisation, Lucas Heights, NSW (Australia)

    1996-12-31

    Recent moves in Ion Beam Microanalysis towards the use of a rapidly growing number of very high resolution, low current and single ion techniques has led to the need for high demagnification and greatly improved beam quality. There is also a move to apply Microbeams at higher energies and with heavier ions. This also puts demands on the focusing system and beam control. This paper describes the recent development of 2-stage lens systems to be applied here and overseas, both at very high resolution and at high energies with heavy ions. It looks at new ion beam analysis applications of such ion microprobes. 8 refs., 1 tab., 1 fig.

  18. Stochastic processes in cell biology

    Bressloff, Paul C

    2014-01-01

    This book develops the theory of continuous and discrete stochastic processes within the context of cell biology.  A wide range of biological topics are covered including normal and anomalous diffusion in complex cellular environments, stochastic ion channels and excitable systems, stochastic calcium signaling, molecular motors, intracellular transport, signal transduction, bacterial chemotaxis, robustness in gene networks, genetic switches and oscillators, cell polarization, polymerization, cellular length control, and branching processes. The book also provides a pedagogical introduction to the theory of stochastic process – Fokker Planck equations, stochastic differential equations, master equations and jump Markov processes, diffusion approximations and the system size expansion, first passage time problems, stochastic hybrid systems, reaction-diffusion equations, exclusion processes, WKB methods, martingales and branching processes, stochastic calculus, and numerical methods.   This text is primarily...

  19. Stochastic response surface methodology: A study in the human health area

    Oliveira, Teresa A., E-mail: teresa.oliveira@uab.pt; Oliveira, Amílcar, E-mail: amilcar.oliveira@uab.pt [Departamento de Ciências e Tecnologia, Universidade Aberta (Portugal); Centro de Estatística e Aplicações, Universidade de Lisboa (Portugal); Leal, Conceição, E-mail: conceicao.leal2010@gmail.com [Departamento de Ciências e Tecnologia, Universidade Aberta (Portugal)

    2015-03-10

    In this paper we review Stochastic Response Surface Methodology as a tool for modeling uncertainty in the context of Risk Analysis. An application in the survival analysis in the breast cancer context is implemented with R software.

  20. A stochastic control problem

    William Margulies

    2004-11-01

    Full Text Available In this paper, we study a specific stochastic differential equation depending on a parameter and obtain a representation of its probability density function in terms of Jacobi Functions. The equation arose in a control problem with a quadratic performance criteria. The quadratic performance is used to eliminate the control in the standard Hamilton-Jacobi variational technique. The resulting stochastic differential equation has a noise amplitude which complicates the solution. We then solve Kolmogorov's partial differential equation for the probability density function by using Jacobi Functions. A particular value of the parameter makes the solution a Martingale and in this case we prove that the solution goes to zero almost surely as time tends to infinity.

  1. Multistage stochastic optimization

    Pflug, Georg Ch

    2014-01-01

    Multistage stochastic optimization problems appear in many ways in finance, insurance, energy production and trading, logistics and transportation, among other areas. They describe decision situations under uncertainty and with a longer planning horizon. This book contains a comprehensive treatment of today’s state of the art in multistage stochastic optimization.  It covers the mathematical backgrounds of approximation theory as well as numerous practical algorithms and examples for the generation and handling of scenario trees. A special emphasis is put on estimation and bounding of the modeling error using novel distance concepts, on time consistency and the role of model ambiguity in the decision process. An extensive treatment of examples from electricity production, asset liability management and inventory control concludes the book

  2. Stochastic speculative price.

    Samuelson, P A

    1971-02-01

    Because a commodity like wheat can be carried forward from one period to the next, speculative arbitrage serves to link its prices at different points of time. Since, however, the size of the harvest depends on complicated probability processes impossible to forecast with certainty, the minimal model for understanding market behavior must involve stochastic processes. The present study, on the basis of the axiom that it is the expected rather than the known-for-certain prices which enter into all arbitrage relations and carryover decisions, determines the behavior of price as the solution to a stochastic-dynamic-programming problem. The resulting stationary time series possesses an ergodic state and normative properties like those often observed for real-world bourses. PMID:16591903

  3. Stochastic calculus and applications

    Cohen, Samuel N

    2015-01-01

    Completely revised and greatly expanded, the new edition of this text takes readers who have been exposed to only basic courses in analysis through the modern general theory of random processes and stochastic integrals as used by systems theorists, electronic engineers and, more recently, those working in quantitative and mathematical finance. Building upon the original release of this title, this text will be of great interest to research mathematicians and graduate students working in those fields, as well as quants in the finance industry. New features of this edition include: End of chapter exercises; New chapters on basic measure theory and Backward SDEs; Reworked proofs, examples and explanatory material; Increased focus on motivating the mathematics; Extensive topical index. "Such a self-contained and complete exposition of stochastic calculus and applications fills an existing gap in the literature. The book can be recommended for first-year graduate studies. It will be useful for all who intend to wo...

  4. Stochastic flights of propellers

    Pan, Margaret; Chiang, Eugene; Evans, Steven N

    2012-01-01

    Kilometer-sized moonlets in Saturn's A ring create S-shaped wakes called "propellers" in surrounding material. The Cassini spacecraft has tracked the motions of propellers for several years and finds that they deviate from Keplerian orbits having constant semimajor axes. The inferred orbital migration is known to switch sign. We show using a statistical test that the time series of orbital longitudes of the propeller Bl\\'eriot is consistent with that of a time-integrated Gaussian random walk. That is, Bl\\'eriot's observed migration pattern is consistent with being stochastic. We further show, using a combination of analytic estimates and collisional N-body simulations, that stochastic migration of the right magnitude to explain the Cassini observations can be driven by encounters with ring particles 10-20 m in radius. That the local ring mass is concentrated in decameter-sized particles is supported on independent grounds by occultation analyses.

  5. Dynamic stochastic optimization

    Ermoliev, Yuri; Pflug, Georg

    2004-01-01

    Uncertainties and changes are pervasive characteristics of modern systems involving interactions between humans, economics, nature and technology. These systems are often too complex to allow for precise evaluations and, as a result, the lack of proper management (control) may create significant risks. In order to develop robust strategies we need approaches which explic­ itly deal with uncertainties, risks and changing conditions. One rather general approach is to characterize (explicitly or implicitly) uncertainties by objec­ tive or subjective probabilities (measures of confidence or belief). This leads us to stochastic optimization problems which can rarely be solved by using the standard deterministic optimization and optimal control methods. In the stochastic optimization the accent is on problems with a large number of deci­ sion and random variables, and consequently the focus ofattention is directed to efficient solution procedures rather than to (analytical) closed-form solu­ tions. Objective an...

  6. Dynamics of stochastic systems

    Klyatskin, Valery I

    2005-01-01

    Fluctuating parameters appear in a variety of physical systems and phenomena. They typically come either as random forces/sources, or advecting velocities, or media (material) parameters, like refraction index, conductivity, diffusivity, etc. The well known example of Brownian particle suspended in fluid and subjected to random molecular bombardment laid the foundation for modern stochastic calculus and statistical physics. Other important examples include turbulent transport and diffusion of particle-tracers (pollutants), or continuous densities (''''oil slicks''''), wave propagation and scattering in randomly inhomogeneous media, for instance light or sound propagating in the turbulent atmosphere.Such models naturally render to statistical description, where the input parameters and solutions are expressed by random processes and fields.The fundamental problem of stochastic dynamics is to identify the essential characteristics of system (its state and evolution), and relate those to the input parameters of ...

  7. Stochastic Volatility Demand Systems

    Apostolos Serletis; Maksim Isakin

    2014-01-01

    We address the estimation of stochastic volatility demand systems. In particular, we relax the homoscedasticity assumption and instead assume that the covariance matrix of the errors of demand systems is time-varying. Since most economic and fiÂ…nancial time series are nonlinear, we achieve superior modeling using parametric nonlinear demand systems in which the unconditional variance is constant but the conditional variance, like the conditional mean, is also a random variable depending on c...

  8. Stochastic Overall Equipment Effectiveness

    Zammori, Francesco Aldo; Braglia, Marcello; Frosolini, Marco

    2011-01-01

    Abstract This paper focuses on the Overall Equipment Effectiveness (OEE), a key performance indicator typically adopted to support Lean Manufacturing and Total Productive Maintenance. Unfortunately, being a deterministic metric, the OEE only provides a static representation of a process, but fails to capture the real variability of manufacturing performances. To take into account the stochastic nature of the OEE, an approximated procedure based on the application of the Central Lim...

  9. Stochastic resonance and computation

    Torres, José-Leonel; Trainor, Lynn

    1997-09-01

    Stochastic resonance (SR) occurs in bistable nonlinear systems subject to noise, as the entrainment of their output by a weak periodic modulation added to the input. Electronic computation involves switching of memory elements between two states that correspond to 1 and 0, respectively. The possibility of switching errors due to SR in memory elements is considered, showing that it represents a negligible danger to reliable computation.

  10. Stochastic reconstruction of sandstones

    Manwart, C.; Torquato, S.; Hilfer, R.

    2000-01-01

    A simulated annealing algorithm is employed to generate a stochastic model for a Berea and a Fontainebleau sandstone with prescribed two-point probability function, lineal path function, and ``pore size'' distribution function, respectively. We find that the temperature decrease of the annealing has to be rather quick to yield isotropic and percolating configurations. A comparison of simple morphological quantities indicates good agreement between the reconstructions and the original sandston...

  11. Identifiability in stochastic models

    1992-01-01

    The problem of identifiability is basic to all statistical methods and data analysis, occurring in such diverse areas as Reliability Theory, Survival Analysis, and Econometrics, where stochastic modeling is widely used. Mathematics dealing with identifiability per se is closely related to the so-called branch of ""characterization problems"" in Probability Theory. This book brings together relevant material on identifiability as it occurs in these diverse fields.

  12. Decentralized stochastic control

    Mahajan, Aditya; Mannan, Mehnaz

    2013-01-01

    Decentralized stochastic control refers to the multi-stage optimization of a dynamical system by multiple controllers that have access to different information. Decentralization of information gives rise to new conceptual challenges that require new solution approaches. In this expository paper, we use the notion of an \\emph{information-state} to explain the two commonly used solution approaches to decentralized control: the person-by-person approach and the common-information approach.

  13. Stochastic Weighted Fractal Networks

    Carletti, Timoteo

    2010-01-01

    In this paper we introduce new models of complex weighted networks sharing several properties with fractal sets: the deterministic non-homogeneous weighted fractal networks and the stochastic weighted fractal networks. Networks of both classes can be completely analytically characterized in terms of the involved parameters. The proposed algorithms improve and extend the framework of weighted fractal networks recently proposed in (T. Carletti & S. Righi, in press Physica A, 2010)

  14. Stochastic wave growth

    Evolution of waves subject to a randomly varying growth rate is considered and the statistical properties of the waves are calculated in terms of the mean, variance, and correlation time of the growth rate. This enables stochastic growth to be studied without needing full knowledge of the microphysics. However, where the microphysics is understood, this approach also allows it to be easily incorporated into studies of larger-scale phenomena involving stochastic growth. Stochastic differential equations and Fokker--Planck equations are obtained, which describe the wave evolution in the presence of a variety of linear and nonlinear processes and boundary conditions, and it is shown that these phenomena can be diagnosed observationally through their effects on the statistical distribution of the wave field strengths. The results are particularly useful for waves with small dispersion, where they explain the strong wave clumping often observed in nature and emphasize the role of marginal stability in setting the level about which fluctuations occur and in determining their magnitude. Application to type III solar radio bursts illustrates many of the main results and verifies and generalizes earlier conclusions reached using a less rigorous approach. In particular, a new condition for marginally stable propagation of type III solar electron beams is found. copyright 1995 American Institute of Physics

  15. Stochasticity Modeling in Memristors

    Naous, Rawan

    2015-10-26

    Diverse models have been proposed over the past years to explain the exhibiting behavior of memristors, the fourth fundamental circuit element. The models varied in complexity ranging from a description of physical mechanisms to a more generalized mathematical modeling. Nonetheless, stochasticity, a widespread observed phenomenon, has been immensely overlooked from the modeling perspective. This inherent variability within the operation of the memristor is a vital feature for the integration of this nonlinear device into the stochastic electronics realm of study. In this paper, experimentally observed innate stochasticity is modeled in a circuit compatible format. The model proposed is generic and could be incorporated into variants of threshold-based memristor models in which apparent variations in the output hysteresis convey the switching threshold shift. Further application as a noise injection alternative paves the way for novel approaches in the fields of neuromorphic engineering circuits design. On the other hand, extra caution needs to be paid to variability intolerant digital designs based on non-deterministic memristor logic.

  16. Comparative Risks of Cancer from Drywall Finishing Based on Stochastic Modeling of Cumulative Exposures to Respirable Dusts and Chrysotile Asbestos Fibers.

    Boelter, Fred W; Xia, Yulin; Dell, Linda

    2015-05-01

    Sanding joint compounds is a dusty activity and exposures are not well characterized. Until the mid 1970s, asbestos-containing joint compounds were used by some people such that sanding could emit dust and asbestos fibers. We estimated the distribution of 8-h TWA concentrations and cumulative exposures to respirable dusts and chrysotile asbestos fibers for four worker groups: (1) drywall specialists, (2) generalists, (3) tradespersons who are bystanders to drywall finishing, and (4) do-it-yourselfers (DIYers). Data collected through a survey of experienced contractors, direct field observations, and literature were used to develop prototypical exposure scenarios for each worker group. To these exposure scenarios, we applied a previously developed semi-empirical mathematical model that predicts area as well as personal breathing zone respirable dust concentrations. An empirical factor was used to estimate chrysotile fiber concentrations from respirable dust concentrations. On a task basis, we found mean 8-h TWA concentrations of respirable dust and chrysotile fibers are numerically highest for specialists, followed by generalists, DIYers, and bystander tradespersons; these concentrations are estimated to be in excess of the respective current but not historical Threshold Limit Values. Due to differences in frequency of activities, annual cumulative exposures are highest for specialists, followed by generalists, bystander tradespersons, and DIYers. Cumulative exposure estimates for chrysotile fibers from drywall finishing are expected to result in few, if any, mesothelioma or excess lung cancer deaths according to recently published risk assessments. Given the dustiness of drywall finishing, we recommend diligence in the use of readily available source controls. PMID:25428276

  17. Stochastic and non-stochastic effects - a conceptual analysis

    The attempt to divide radiation effects into stochastic and non-stochastic effects is discussed. It is argued that radiation or toxicological effects are contingently related to radiation or chemical exposure. Biological effects in general can be described by general laws but these laws never represent a necessary connection. Actually stochastic effects express contingent, or empirical, connections while non-stochastic effects represent semantic and non-factual connections. These two expressions stem from two different levels of discourse. The consequence of this analysis for radiation biology and radiation protection is discussed. (author)

  18. Radiotracer study on the efficiency of a cylindrical 2-stage anaerobic sludge digester

    Radiotracer experiments were carried out on a cylindrical 2-stage anaerobic sludge digester in order to investigate the improvement of their efficiency by means of RTD (residence time distribution) measurements before and after cleaning up the inside of the digester. The tracer was scandium in an EDTA solution which forms such a stable complex compound to keep the isotope form being adsorbed onto the surface of the pipelines or the wall. It was injected into the digester by pressurized nitrogen gas and its movement was monitored by NaI(Tl) scintillation detectors installed around the digester and recorded for a month by a 24-channel data acquisition system specially developed for radiotracer experiments by the Korea Tracer Group of KAERI. The experimental data was analysed for the MRT (mean residence time) and other parameters characterizing the flow behaviour. (author)

  19. Qualitatively stability of nonstandard 2-stage explicit Runge-Kutta methods of order two

    Khalsaraei, M. M.; Khodadosti, F.

    2016-02-01

    When one solves differential equations, modeling physical phenomena, it is of great importance to take physical constraints into account. More precisely, numerical schemes have to be designed such that discrete solutions satisfy the same constraints as exact solutions. Nonstandard finite differences (NSFDs) schemes can improve the accuracy and reduce computational costs of traditional finite difference schemes. In addition NSFDs produce numerical solutions which also exhibit essential properties of solution. In this paper, a class of nonstandard 2-stage Runge-Kutta methods of order two (we call it nonstandard RK2) is considered. The preservation of some qualitative properties by this class of methods are discussed. In order to illustrate our results, we provide some numerical examples.

  20. Stochastic Nature in Cellular Processes

    刘波; 刘圣君; 王祺; 晏世伟; 耿轶钊; SAKATA Fumihiko; GAO Xing-Fa

    2011-01-01

    The importance of stochasticity in cellular processes is increasingly recognized in both theoretical and experimental studies. General features of stochasticity in gene regulation and expression are briefly reviewed in this article, which include the main experimental phenomena, classification, quantization and regulation of noises. The correlation and transmission of noise in cascade networks are analyzed further and the stochastic simulation methods that can capture effects of intrinsic and extrinsic noise are described.

  1. Stochastic Analysis of Cylindrical Shell

    Grzywiński Maksym

    2014-06-01

    Full Text Available The paper deals with some chosen aspects of stochastic structural analysis and its application in the engineering practice. The main aim of the study is to apply the generalized stochastic perturbation techniques based on classical Taylor expansion with a single random variable for solution of stochastic problems in structural mechanics. The study is illustrated by numerical results concerning an industrial thin shell structure modeled as a 3-D structure.

  2. Some stochastic aspects of quantization

    Ichiro Ohba

    2002-08-01

    From the advent of quantum mechanics, various types of stochastic-dynamical approach to quantum mechanics have been tried. We discuss how to utilize Nelson’s stochastic quantum mechanics to analyze the tunneling phenomena, how to derive relativistic field equations via the Poisson process and how to describe a quantum dynamics of open systems by the use of quantum state diffusion, or the stochastic Schrödinger equation.

  3. Stochastic Analysis with Financial Applications

    Kohatsu-Higa, Arturo; Sheu, Shuenn-Jyi

    2011-01-01

    Stochastic analysis has a variety of applications to biological systems as well as physical and engineering problems, and its applications to finance and insurance have bloomed exponentially in recent times. The goal of this book is to present a broad overview of the range of applications of stochastic analysis and some of its recent theoretical developments. This includes numerical simulation, error analysis, parameter estimation, as well as control and robustness properties for stochastic equations. This book also covers the areas of backward stochastic differential equations via the (non-li

  4. A recurrent stochastic binary network

    赵杰煜

    2001-01-01

    Stochastic neural networks are usually built by introducing random fluctuations into the network. A natural method is to use stochastic connections rather than stochastic activation functions. We propose a new model in which each neuron has very simple functionality but all the connections are stochastic. It is shown that the stationary distribution of the network uniquely exists and it is approximately a Boltzmann-Gibbs distribution. The relationship between the model and the Markov random field is discussed. New techniques to implement simulated annealing and Boltzmann learning are proposed. Simulation results on the graph bisection problem and image recognition show that the network is powerful enough to solve real world problems.

  5. Stochastic conditional intensity processes

    Bauwens, Luc; Hautsch, Nikolaus

    2006-01-01

    model allows for a wide range of (cross-)autocorrelation structures in multivariate point processes. The model is estimated by simulated maximum likelihood (SML) using the efficient importance sampling (EIS) technique. By modeling price intensities based on NYSE trading, we provide significant evidence......In this article, we introduce the so-called stochastic conditional intensity (SCI) model by extending Russell’s (1999) autoregressive conditional intensity (ACI) model by a latent common dynamic factor that jointly drives the individual intensity components. We show by simulations that the proposed...... for a joint latent factor and show that its inclusion allows for an improved and more parsimonious specification of the multivariate intensity process...

  6. Structured Stochastic Linear Bandits

    Johnson, Nicholas; Sivakumar, Vidyashankar; Banerjee, Arindam

    2016-01-01

    The stochastic linear bandit problem proceeds in rounds where at each round the algorithm selects a vector from a decision set after which it receives a noisy linear loss parameterized by an unknown vector. The goal in such a problem is to minimize the (pseudo) regret which is the difference between the total expected loss of the algorithm and the total expected loss of the best fixed vector in hindsight. In this paper, we consider settings where the unknown parameter has structure, e.g., spa...

  7. Stochastic thermodynamics of resetting

    Fuchs, Jaco; Goldt, Sebastian; Seifert, Udo

    2016-03-01

    Stochastic dynamics with random resetting leads to a non-equilibrium steady state. Here, we consider the thermodynamics of resetting by deriving the first and second law for resetting processes far from equilibrium. We identify the contributions to the entropy production of the system which arise due to resetting and show that they correspond to the rate with which information is either erased or created. Using Landauer's principle, we derive a bound on the amount of work that is required to maintain a resetting process. We discuss different regimes of resetting, including a Maxwell demon scenario where heat is extracted from a bath at constant temperature.

  8. Stochastic cooling for beginners

    These two lectures have been prepared to give a simple introduction to the principles. In Part I we try to explain stochastic cooling using the time-domain picture which starts from the pulse response of the system. In Part II the discussion is repeated, looking more closely at the frequency-domain response. An attempt is made to familiarize the beginners with some of the elementary cooling equations, from the 'single particle case' up to equations which describe the evolution of the particle distribution. (orig.)

  9. Stochastic ontogenetic growth model

    West, B. J.; West, D.

    2012-02-01

    An ontogenetic growth model (OGM) for a thermodynamically closed system is generalized to satisfy both the first and second law of thermodynamics. The hypothesized stochastic ontogenetic growth model (SOGM) is shown to entail the interspecies allometry relation by explicitly averaging the basal metabolic rate and the total body mass over the steady-state probability density for the total body mass (TBM). This is the first derivation of the interspecies metabolic allometric relation from a dynamical model and the asymptotic steady-state distribution of the TBM is fit to data and shown to be inverse power law.

  10. Stochastic multi-stage optimization at the crossroads between discrete time stochastic control and stochastic programming

    Carpentier, Pierre; Cohen, Guy; De Lara, Michel

    2015-01-01

    The focus of the present volume is stochastic optimization of dynamical systems in discrete time where - by concentrating on the role of information regarding optimization problems - it discusses the related discretization issues. There is a growing need to tackle uncertainty in applications of optimization. For example the massive introduction of renewable energies in power systems challenges traditional ways to manage them. This book lays out basic and advanced tools to handle and numerically solve such problems and thereby is building a bridge between Stochastic Programming and Stochastic Control. It is intended for graduates readers and scholars in optimization or stochastic control, as well as engineers with a background in applied mathematics.

  11. Stochastic Runge-Kutta Software Package for Stochastic Differential Equations

    Gevorkyan, M N; Korolkova, A V; Kulyabov, D S; Sevastyanov, L A

    2016-01-01

    As a result of the application of a technique of multistep processes stochastic models construction the range of models, implemented as a self-consistent differential equations, was obtained. These are partial differential equations (master equation, the Fokker--Planck equation) and stochastic differential equations (Langevin equation). However, analytical methods do not always allow to research these equations adequately. It is proposed to use the combined analytical and numerical approach studying these equations. For this purpose the numerical part is realized within the framework of symbolic computation. It is recommended to apply stochastic Runge--Kutta methods for numerical study of stochastic differential equations in the form of the Langevin. Under this approach, a program complex on the basis of analytical calculations metasystem Sage is developed. For model verification logarithmic walks and Black--Scholes two-dimensional model are used. To illustrate the stochastic "predator--prey" type model is us...

  12. Stochastic power flow modeling

    1980-06-01

    The stochastic nature of customer demand and equipment failure on large interconnected electric power networks has produced a keen interest in the accurate modeling and analysis of the effects of probabilistic behavior on steady state power system operation. The principle avenue of approach has been to obtain a solution to the steady state network flow equations which adhere both to Kirchhoff's Laws and probabilistic laws, using either combinatorial or functional approximation techniques. Clearly the need of the present is to develop sound techniques for producing meaningful data to serve as input. This research has addressed this end and serves to bridge the gap between electric demand modeling, equipment failure analysis, etc., and the area of algorithm development. Therefore, the scope of this work lies squarely on developing an efficient means of producing sensible input information in the form of probability distributions for the many types of solution algorithms that have been developed. Two major areas of development are described in detail: a decomposition of stochastic processes which gives hope of stationarity, ergodicity, and perhaps even normality; and a powerful surrogate probability approach using proportions of time which allows the calculation of joint events from one dimensional probability spaces.

  13. Stochastic Blind Motion Deblurring

    Xiao, Lei

    2015-05-13

    Blind motion deblurring from a single image is a highly under-constrained problem with many degenerate solutions. A good approximation of the intrinsic image can therefore only be obtained with the help of prior information in the form of (often non-convex) regularization terms for both the intrinsic image and the kernel. While the best choice of image priors is still a topic of ongoing investigation, this research is made more complicated by the fact that historically each new prior requires the development of a custom optimization method. In this paper, we develop a stochastic optimization method for blind deconvolution. Since this stochastic solver does not require the explicit computation of the gradient of the objective function and uses only efficient local evaluation of the objective, new priors can be implemented and tested very quickly. We demonstrate that this framework, in combination with different image priors produces results with PSNR values that match or exceed the results obtained by much more complex state-of-the-art blind motion deblurring algorithms.

  14. AA, stochastic precooling pickup

    1980-01-01

    The freshly injected antiprotons were subjected to fast stochastic "precooling". In this picture of a precooling pickup, the injection orbit is to the left, the stack orbit to the far right. After several seconds of precooling with the system's kickers (in momentum and in the vertical plane), the precooled antiprotons were transferred, by means of RF, to the stack tail, where they were subjected to further stochastic cooling in momentum and in both transverse planes, until they ended up, deeply cooled, in the stack core. During precooling, a shutter near the central orbit shielded the pickups from the signals emanating from the stack-core, whilst the stack-core was shielded from the violent action of the precooling kickers by a shutter on these. All shutters were opened briefly during transfer of the precooled antiprotons to the stack tail. Here, the shutter is not yet mounted. Precooling pickups and kickers had the same design, except that the kickers had cooling circuits and the pickups had none. Peering th...

  15. Non Monotone Stochastic Evolution Equations

    Kenneth L. Kuttler; Li, Ji

    2013-01-01

    An approach to stochastic evolution equations based on a simple generalization of known embedding theorems is presented. It allows for the inclusion of problems which have nonlinear non monotone operators. This is used to discuss the existence of strong solutions to a stochastic Navier Stokes problem in dimension less than four.

  16. On stochastic finite difference schemes

    Gyongy, Istvan

    2013-01-01

    Finite difference schemes in the spatial variable for degenerate stochastic parabolic PDEs are investigated. Sharp results on the rate of $L_p$ and almost sure convergence of the finite difference approximations are presented and results on Richardson extrapolation are established for stochastic parabolic schemes under smoothness assumptions.

  17. Stochastic Pi-calculus Revisited

    Cardelli, Luca; Mardare, Radu Iulian

    2013-01-01

    We develop a version of stochastic Pi-calculus with a semantics based on measure theory. We dene the behaviour of a process in a rate environment using measures over the measurable space of processes induced by structural congruence. We extend the stochastic bisimulation to include the concept of...

  18. Alternative Asymmetric Stochastic Volatility Models

    M. Asai (Manabu); M.J. McAleer (Michael)

    2010-01-01

    textabstractThe stochastic volatility model usually incorporates asymmetric effects by introducing the negative correlation between the innovations in returns and volatility. In this paper, we propose a new asymmetric stochastic volatility model, based on the leverage and size effects. The model is

  19. Anticipated backward stochastic differential equations

    Peng, Shige; Yang, Zhe

    2009-01-01

    In this paper we discuss new types of differential equations which we call anticipated backward stochastic differential equations (anticipated BSDEs). In these equations the generator includes not only the values of solutions of the present but also the future. We show that these anticipated BSDEs have unique solutions, a comparison theorem for their solutions, and a duality between them and stochastic differential delay equations.

  20. Evolutionary computation in stochastic environments

    Schmidt, Christian

    2007-01-01

    This book develops efficient methods for the application of Evolutionary Algorithms on stochastic problems. To achieve this, procedures for statistical selection are systematically analyzed with respect to different measures and significantly improved. It is shown how to adapt one of the best procedures for the needs of Evolutionary Algorithms and Evolutionary operators for efficient implementation in stochastic environments are identified.

  1. Brownian motion and stochastic calculus

    Karatzas, Ioannis

    1998-01-01

    This book is designed as a text for graduate courses in stochastic processes. It is written for readers familiar with measure-theoretic probability and discrete-time processes who wish to explore stochastic processes in continuous time. The vehicle chosen for this exposition is Brownian motion, which is presented as the canonical example of both a martingale and a Markov process with continuous paths. In this context, the theory of stochastic integration and stochastic calculus is developed. The power of this calculus is illustrated by results concerning representations of martingales and change of measure on Wiener space, and these in turn permit a presentation of recent advances in financial economics (option pricing and consumption/investment optimization). This book contains a detailed discussion of weak and strong solutions of stochastic differential equations and a study of local time for semimartingales, with special emphasis on the theory of Brownian local time. The text is complemented by a large num...

  2. Variance decomposition in stochastic simulators

    This work aims at the development of a mathematical and computational approach that enables quantification of the inherent sources of stochasticity and of the corresponding sensitivities in stochastic simulations of chemical reaction networks. The approach is based on reformulating the system dynamics as being generated by independent standardized Poisson processes. This reformulation affords a straightforward identification of individual realizations for the stochastic dynamics of each reaction channel, and consequently a quantitative characterization of the inherent sources of stochasticity in the system. By relying on the Sobol-Hoeffding decomposition, the reformulation enables us to perform an orthogonal decomposition of the solution variance. Thus, by judiciously exploiting the inherent stochasticity of the system, one is able to quantify the variance-based sensitivities associated with individual reaction channels, as well as the importance of channel interactions. Implementation of the algorithms is illustrated in light of simulations of simplified systems, including the birth-death, Schlögl, and Michaelis-Menten models

  3. Variance decomposition in stochastic simulators

    Le Maître, O. P.

    2015-06-28

    This work aims at the development of a mathematical and computational approach that enables quantification of the inherent sources of stochasticity and of the corresponding sensitivities in stochastic simulations of chemical reaction networks. The approach is based on reformulating the system dynamics as being generated by independent standardized Poisson processes. This reformulation affords a straightforward identification of individual realizations for the stochastic dynamics of each reaction channel, and consequently a quantitative characterization of the inherent sources of stochasticity in the system. By relying on the Sobol-Hoeffding decomposition, the reformulation enables us to perform an orthogonal decomposition of the solution variance. Thus, by judiciously exploiting the inherent stochasticity of the system, one is able to quantify the variance-based sensitivities associated with individual reaction channels, as well as the importance of channel interactions. Implementation of the algorithms is illustrated in light of simulations of simplified systems, including the birth-death, Schlögl, and Michaelis-Menten models.

  4. Variance decomposition in stochastic simulators

    Le Maître, O. P.; Knio, O. M.; Moraes, A.

    2015-06-01

    This work aims at the development of a mathematical and computational approach that enables quantification of the inherent sources of stochasticity and of the corresponding sensitivities in stochastic simulations of chemical reaction networks. The approach is based on reformulating the system dynamics as being generated by independent standardized Poisson processes. This reformulation affords a straightforward identification of individual realizations for the stochastic dynamics of each reaction channel, and consequently a quantitative characterization of the inherent sources of stochasticity in the system. By relying on the Sobol-Hoeffding decomposition, the reformulation enables us to perform an orthogonal decomposition of the solution variance. Thus, by judiciously exploiting the inherent stochasticity of the system, one is able to quantify the variance-based sensitivities associated with individual reaction channels, as well as the importance of channel interactions. Implementation of the algorithms is illustrated in light of simulations of simplified systems, including the birth-death, Schlögl, and Michaelis-Menten models.

  5. A 2-stage strategy updating rule promotes cooperation in the prisoner's dilemma game

    Fang, Xiang-Sheng; Zhu, Ping; Liu, Run-Ran; Liu, En-Yu; Wei, Gui-Yi

    2012-10-01

    In this study, we propose a spatial prisoner's dilemma game model with a 2-stage strategy updating rule, and focus on the cooperation behavior of the system. In the first stage, i.e., the pre-learning stage, a focal player decides whether to update his strategy according to the pre-learning factor β and the payoff difference between himself and the average of his neighbors. If the player makes up his mind to update, he enters into the second stage, i.e., the learning stage, and adopts a strategy of a randomly selected neighbor according to the standard Fermi updating rule. The simulation results show that the cooperation level has a non-trivial dependence on the pre-learning factor. Generally, the cooperation frequency decreases as the pre-learning factor increases; but a high cooperation level can be obtained in the intermediate region of -3 learning and learning. Our results may sharpen the understanding of the influence of the strategy updating rule on evolutionary games.

  6. Unsteady Aero Computation of a 1 1/2 Stage Large Scale Rotating Turbine

    To, Wai-Ming

    2012-01-01

    This report is the documentation of the work performed for the Subsonic Rotary Wing Project under the NASA s Fundamental Aeronautics Program. It was funded through Task Number NNC10E420T under GESS-2 Contract NNC06BA07B in the period of 10/1/2010 to 8/31/2011. The objective of the task is to provide support for the development of variable speed power turbine technology through application of computational fluid dynamics analyses. This includes work elements in mesh generation, multistage URANS simulations, and post-processing of the simulation results for comparison with the experimental data. The unsteady CFD calculations were performed with the TURBO code running in multistage single passage (phase lag) mode. Meshes for the blade rows were generated with the NASA developed TCGRID code. The CFD performance is assessed and improvements are recommended for future research in this area. For that, the United Technologies Research Center's 1 1/2 stage Large Scale Rotating Turbine was selected to be the candidate engine configuration for this computational effort because of the completeness and availability of the data.

  7. A 2-stage strategy updating rule promotes cooperation in the prisoner's dilemma game

    Fang Xiang-Sheng; Zhu Ping; Liu Run-Ran; Liu En-Yu; Wei Gui-Yi

    2012-01-01

    In this study,we propose a spatial prisoner's dilemma game model with a 2-stage strategy updating rule,and focus on the cooperation behavior of the system.In the first stage,i.e.,the pre-learning stage,a focal player decides whether to update his strategy according to the pre-learning factor β and the payoff difference between himself and the average of his neighbors.If the player makes up his mind to update,he enters into the second stage,i.e.,the learning stage,and adopts a strategy of a randomly selected neighbor according to the standard Fermi updating rule. The simulation results show that the cooperation level has a non-trivial dependence on the pre-learning factor.Generally,the cooperation frequency decreases as the pre-learning factor increases; but a high cooperation level can be obtained in the intermediate region of -3 < β < -1.We then give some explanations via studying the co-action of pre-learning and learning.Our results may sharpen the understanding of the influence of the strategy updating rule on evolutionary games.

  8. Classical and spatial stochastic processes with applications to biology

    Schinazi, Rinaldo B

    2014-01-01

    The revised and expanded edition of this textbook presents the concepts and applications of random processes with the same illuminating simplicity as its first edition, but with the notable addition of substantial modern material on biological modeling. While still treating many important problems in fields such as engineering and mathematical physics, the book also focuses on the highly relevant topics of cancerous mutations, influenza evolution, drug resistance, and immune response. The models used elegantly apply various classical stochastic models presented earlier in the text, and exercises are included throughout to reinforce essential concepts. The second edition of Classical and Spatial Stochastic Processes is suitable as a textbook for courses in stochastic processes at the advanced-undergraduate and graduate levels, or as a self-study resource for researchers and practitioners in mathematics, engineering, physics, and mathematical biology. Reviews of the first edition: An appetizing textbook for a f...

  9. Cancer

    ... Blood tests (which look for chemicals such as tumor markers) Bone marrow biopsy (for lymphoma or leukemia) Chest ... the case with skin cancers , as well as cancers of the lung, breast, and colon. If the tumor has spread ...

  10. Cancer

    Cancer begins in your cells, which are the building blocks of your body. Normally, your body forms ... be benign or malignant. Benign tumors aren't cancer while malignant ones are. Cells from malignant tumors ...

  11. Bunched beam stochastic cooling

    Wei, Jie

    1992-09-01

    The scaling laws for bunched-beam stochastic cooling has been derived in terms of the optimum cooling rate and the mixing condition. In the case that particles occupy the entire sinusoidal rf bucket, the optimum cooling rate of the bunched beam is shown to be similar to that predicted from the coasting-beam theory using a beam of the same average density and mixing factor. However, in the case that particles occupy only the center of the bucket, the optimum rate decrease in proportion to the ratio of the bunch area to the bucket area. The cooling efficiency can be significantly improved if the synchrotron side-band spectrum is effectively broadened, e.g. by the transverse tune spread or by using a double rf system.

  12. Bunched beam stochastic cooling

    Wei, Jie.

    1992-01-01

    The scaling laws for bunched-beam stochastic cooling has been derived in terms of the optimum cooling rate and the mixing condition. In the case that particles occupy the entire sinusoidal rf bucket, the optimum cooling rate of the bunched beam is shown to be similar to that predicted from the coasting-beam theory using a beam of the same average density and mixing factor. However, in the case that particles occupy only the center of the bucket, the optimum rate decrease in proportion to the ratio of the bunch area to the bucket area. The cooling efficiency can be significantly improved if the synchrotron side-band spectrum is effectively broadened, e.g. by the transverse tune spread or by using a double rf system.

  13. Stochastic Programming with Probability

    Andrieu, Laetitia; Vázquez-Abad, Felisa

    2007-01-01

    In this work we study optimization problems subject to a failure constraint. This constraint is expressed in terms of a condition that causes failure, representing a physical or technical breakdown. We formulate the problem in terms of a probability constraint, where the level of "confidence" is a modelling parameter and has the interpretation that the probability of failure should not exceed that level. Application of the stochastic Arrow-Hurwicz algorithm poses two difficulties: one is structural and arises from the lack of convexity of the probability constraint, and the other is the estimation of the gradient of the probability constraint. We develop two gradient estimators with decreasing bias via a convolution method and a finite difference technique, respectively, and we provide a full analysis of convergence of the algorithms. Convergence results are used to tune the parameters of the numerical algorithms in order to achieve best convergence rates, and numerical results are included via an example of ...

  14. Stochastic reconstruction of sandstones

    A simulated annealing algorithm is employed to generate a stochastic model for a Berea sandstone and a Fontainebleau sandstone, with each a prescribed two-point probability function, lineal-path function, and ''pore size'' distribution function, respectively. We find that the temperature decrease of the annealing has to be rather quick to yield isotropic and percolating configurations. A comparison of simple morphological quantities indicates good agreement between the reconstructions and the original sandstones. Also, the mean survival time of a random walker in the pore space is reproduced with good accuracy. However, a more detailed investigation by means of local porosity theory shows that there may be significant differences of the geometrical connectivity between the reconstructed and the experimental samples. (c) 2000 The American Physical Society

  15. Stochastic population theories

    Ludwig, Donald

    1974-01-01

    These notes serve as an introduction to stochastic theories which are useful in population biology; they are based on a course given at the Courant Institute, New York, in the Spring of 1974. In order to make the material. accessible to a wide audience, it is assumed that the reader has only a slight acquaintance with probability theory and differential equations. The more sophisticated topics, such as the qualitative behavior of nonlinear models, are approached through a succession of simpler problems. Emphasis is placed upon intuitive interpretations, rather than upon formal proofs. In most cases, the reader is referred elsewhere for a rigorous development. On the other hand, an attempt has been made to treat simple, useful models in some detail. Thus these notes complement the existing mathematical literature, and there appears to be little duplication of existing works. The authors are indebted to Miss Jeanette Figueroa for her beautiful and speedy typing of this work. The research was supported by the Na...

  16. Stochastic reconstruction of sandstones

    Manwart; Torquato; Hilfer

    2000-07-01

    A simulated annealing algorithm is employed to generate a stochastic model for a Berea sandstone and a Fontainebleau sandstone, with each a prescribed two-point probability function, lineal-path function, and "pore size" distribution function, respectively. We find that the temperature decrease of the annealing has to be rather quick to yield isotropic and percolating configurations. A comparison of simple morphological quantities indicates good agreement between the reconstructions and the original sandstones. Also, the mean survival time of a random walker in the pore space is reproduced with good accuracy. However, a more detailed investigation by means of local porosity theory shows that there may be significant differences of the geometrical connectivity between the reconstructed and the experimental samples. PMID:11088546

  17. Backward stochastic differential equations and its application to stochastic control

    Veverka, Petr

    Praha : Nakladatelství ČVUT - výroba, 2010 - (Hobza, T.), s. 181-189 ISBN 978-80-01-04641-8. [Stochastic and Physical Monitoring Systems 2010. Děčín (CZ), 27.06.2010-03.07.2010] R&D Projects: GA ČR GD402/09/H045; GA ČR GAP402/10/1610 Institutional research plan: CEZ:AV0Z10750506 Keywords : BSDE * Stochastic control Subject RIV: BA - General Mathematics http://library.utia.cas.cz/separaty/2010/E/veverka-backward%20stochastic%20differential%20equations%20and%20its%20application%20to%20stochastic%20control.pdf

  18. Stochasticity in the Josephson map

    The Josephson map describes nonlinear dynamics of systems characterized by standard map with the uniform external bias superposed. The intricate structures of the phase space portrait of the Josephson map are examined on the basis of the tangent map associated with the Josephson map. Numerical observation of the stochastic diffusion in the Josephson map is examined in comparison with the renormalized diffusion coefficient calculated by the method of characteristic function. The global stochasticity of the Josephson map occurs at the values of far smaller stochastic parameter than the case of the standard map. (author)

  19. Introduction to stochastic dynamic programming

    Ross, Sheldon M; Lukacs, E

    1983-01-01

    Introduction to Stochastic Dynamic Programming presents the basic theory and examines the scope of applications of stochastic dynamic programming. The book begins with a chapter on various finite-stage models, illustrating the wide range of applications of stochastic dynamic programming. Subsequent chapters study infinite-stage models: discounting future returns, minimizing nonnegative costs, maximizing nonnegative returns, and maximizing the long-run average return. Each of these chapters first considers whether an optimal policy need exist-providing counterexamples where appropriate-and the

  20. Linear stochastic neutron transport theory

    A new and direct derivation of the Bell-Pal fundamental equation for (low power) neutron stochastic behaviour in the Boltzmann continuum model is given. The development includes correlation of particle emission direction in induced and spontaneous fission. This leads to generalizations of the backward and forward equations for the mean and variance of neutron behaviour. The stochastic importance for neutron transport theory is introduced and related to the conventional deterministic importance. Defining equations and moment equations are derived and shown to be related to the backward fundamental equation with the detector distribution of the operational definition of stochastic importance playing the role of an adjoint source. (author)

  1. Stochastic dynamic equations on general time scales

    Martin Bohner; Olexandr M. Stanzhytskyi; Anastasiia O. Bratochkina

    2013-01-01

    In this article, we construct stochastic integral and stochastic differential equations on general time scales. We call these equations stochastic dynamic equations. We provide the existence and uniqueness theorem for solutions of stochastic dynamic equations. The crucial tool of our construction is a result about a connection between the time scales Lebesgue integral and the Lebesgue integral in the common sense.

  2. An introduction to probability and stochastic processes

    Melsa, James L

    2013-01-01

    Geared toward college seniors and first-year graduate students, this text is designed for a one-semester course in probability and stochastic processes. Topics covered in detail include probability theory, random variables and their functions, stochastic processes, linear system response to stochastic processes, Gaussian and Markov processes, and stochastic differential equations. 1973 edition.

  3. Stochastic analysis of laminated composite plate considering stochastic homogenization problem

    S. SAKATA; K. OKUDA; K. IKEDA

    2015-01-01

    This paper discusses a multiscale stochastic analysis of a laminated composite plate consisting of unidirectional fiber reinforced composite laminae. In particular, influence of a microscopic random variation of the elastic properties of component materials on mechanical properties of the laminated plate is investigated. Laminated composites are widely used in civil engineering, and therefore multiscale stochastic analysis of laminated composites should be performed for reliability evaluation of a composite civil structure. This study deals with the stochastic response of a laminated composite plate against the microscopic random variation in addition to a random variation of fiber orientation in each lamina, and stochastic properties of the mechanical responses of the laminated plate is investigated. Halpin-Tsai formula and the homogenization theory-based finite element analysis are employed for estimation of effective elastic properties of lamina, and the classical laminate theory is employed for analysis of a laminated plate. The Monte-Carlo simulation and the first-order second moment method with sensitivity analysis are employed for the stochastic analysis. From the numerical results, importance of the multiscale stochastic analysis for reliability evaluation of a laminated composite structure and applicability of the sensitivity-based approach are discussed.

  4. [Cancer].

    de la Peña-López, Roberto; Remolina-Bonilla, Yuly Andrea

    2016-09-01

    Cancer is a group of diseases which represents a significant public health problem in Mexico and worldwide. In Mexico neoplasms are the second leading cause of death. An increased morbidity and mortality are expected in the next decades. Several preventable risk factors for cancer development have been identified, the most relevant including tobacco use, which accounts for 30% of the cancer cases; and obesity, associated to another 30%. These factors, in turn, are related to sedentarism, alcohol abuse and imbalanced diets. Some agents are well knokn to cause cancer such as ionizing radiation, viruses such as the papilloma virus (HPV) and hepatitis virus (B and C), and more recently environmental pollution exposure and red meat consumption have been pointed out as carcinogens by the International Agency for Research in Cancer (IARC). The scientific evidence currently available is insufficient to consider milk either as a risk factor or protective factor against different types of cancer. PMID:27603890

  5. Detecting Stochastic Information of Electrocardiograms

    Gutíerrez, R M; Guti'errez, Rafael M.; Sandoval, Luis A.

    2003-01-01

    In this work we present a method to detect, identify and characterize stochastic information contained in an electrocardiogram (ECG). We assume, as it is well known, that the ECG has information corresponding to many different processes related to the cardiac activity. We analyze scaling and Markov processes properties of the detected stochastic information using the power spectrum of the ECG and the Fokker-Planck equation respectively. The detected stochastic information is then characterized by three measures. First, the slope of the power spectrum in a particular range of frequencies as a scaling parameter. Second, an empirical estimation of the drift and diffusion coefficients of the Fokker-Planck equation through the Kramers-Moyal coefficients which define the evolution of the probability distribution of the detected stochastic information.

  6. Nucleon structure from stochastic estimators

    Bali, Gunnar S; Gläßle, Benjamin; Göckeler, Meinulf; Najjar, Johannes; Rödl, Rudolf; Schäfer, Andreas; Sternbeck, André; Söldner, Wolfgang

    2013-01-01

    Using stochastic estimators for connected meson and baryon three-point functions has successfully been tried in the past years. Compared to the standard sequential source method we trade the freedom to compute the current-to-sink propagator independently of the hadron sink for additional stochastic noise in our observables. In the case of the nucleon we can use this freedom to compute many different sink-momentum/polarization combinations, which grants access to more virtualities. We will present preliminary results on the scalar, electro-magnetic and axial form factors of the nucleon in $N_f=2+1$ lattice QCD and contrast the performance of the stochastic method to the sequential source method. We find the stochastic method to be competitive in terms of errors at fixed cost.

  7. Stochastic Climate Theory and Modelling

    Franzke, Christian L E; Berner, Judith; Williams, Paul D; Lucarini, Valerio

    2014-01-01

    Stochastic methods are a crucial area in contemporary climate research and are increasingly being used in comprehensive weather and climate prediction models as well as reduced order climate models. Stochastic methods are used as subgrid-scale parameterizations as well as for model error representation, uncertainty quantification, data assimilation and ensemble prediction. The need to use stochastic approaches in weather and climate models arises because we still cannot resolve all necessary processes and scales in comprehensive numerical weather and climate prediction models. In many practical applications one is mainly interested in the largest and potentially predictable scales and not necessarily in the small and fast scales. For instance, reduced order models can simulate and predict large scale modes. Statistical mechanics and dynamical systems theory suggest that in reduced order models the impact of unresolved degrees of freedom can be represented by suitable combinations of deterministic and stochast...

  8. Optimization of stochastic database cracking

    Bhardwaj, Meenesh

    2013-01-01

    Variant Stochastic cracking is a significantly more resilient approach to adaptive indexing. It showed [1]that Stochastic cracking uses each query as a hint on how to reorganize data, but not blindly so; it gains resilience and avoids performance bottlenecks by deliberately applying certain arbitrary choices in its decision making. Therefore bring, adaptive indexing forward to a mature formulation that confers the workload-robustness that previous approaches lacked. Original cracking relies o...

  9. Pathwise construction of stochastic integrals

    Nutz, Marcel

    2012-01-01

    We propose a method to construct the stochastic integral simultaneously under a non-dominated family of probability measures. Path-by-path, and without referring to a probability measure, we construct a sequence of Lebesgue-Stieltjes integrals whose medial limit coincides with the usual stochastic integral under essentially any probability measure such that the integrator is a semimartingale. This method applies to any predictable integrand.

  10. On solving stochastic MADM problems

    Văduva Ion; Resteanu Cornel

    2009-01-01

    The paper examines a MADM problem with stochastic attributes. The transformation of a stochastic MADM problem into a cardinal problem is done by the standardization of the probability distribution of each attribute X and calculating the information of each attribute as Shannon's entropy or Onicescu's informational energy. Some well known (performant) methods to solve a cardinal MADM problem are presented and a method for combining results of several methods to give a final MADM solution is di...

  11. Stochastic superparameterization in quasigeostrophic turbulence

    In this article we expand and develop the authors' recent proposed methodology for efficient stochastic superparameterization algorithms for geophysical turbulence. Geophysical turbulence is characterized by significant intermittent cascades of energy from the unresolved to the resolved scales resulting in complex patterns of waves, jets, and vortices. Conventional superparameterization simulates large scale dynamics on a coarse grid in a physical domain, and couples these dynamics to high-resolution simulations on periodic domains embedded in the coarse grid. Stochastic superparameterization replaces the nonlinear, deterministic eddy equations on periodic embedded domains by quasilinear stochastic approximations on formally infinite embedded domains. The result is a seamless algorithm which never uses a small scale grid and is far cheaper than conventional SP, but with significant success in difficult test problems. Various design choices in the algorithm are investigated in detail here, including decoupling the timescale of evolution on the embedded domains from the length of the time step used on the coarse grid, and sensitivity to certain assumed properties of the eddies (e.g. the shape of the assumed eddy energy spectrum). We present four closures based on stochastic superparameterization which elucidate the properties of the underlying framework: a ‘null hypothesis’ stochastic closure that uncouples the eddies from the mean, a stochastic closure with nonlinearly coupled eddies and mean, a nonlinear deterministic closure, and a stochastic closure based on energy conservation. The different algorithms are compared and contrasted on a stringent test suite for quasigeostrophic turbulence involving two-layer dynamics on a β-plane forced by an imposed background shear. The success of the algorithms developed here suggests that they may be fruitfully applied to more realistic situations. They are expected to be particularly useful in providing accurate and

  12. The dynamics of stochastic processes

    Basse-O'Connor, Andreas

    In the present thesis the dynamics of stochastic processes is studied with a special attention to the semimartingale property. This is mainly motivated by the fact that semimartingales provide the class of the processes for which it is possible to define a reasonable stochastic calculus due to the...... result is obtained and applied. Moreover, martingales indexed by the whole real line is studied and characterized and the thesis is concluded with a study of stationary solutions of the Langevin equation....

  13. Abstractions of stochastic hybrid systems

    Bujorianu, L.M.; Lygeros, J.; Bujorianu, M.C.

    2005-01-01

    Many control systems have large, infinite state space that can not be easily abstracted. One method to analyse and verify these systems is reachability analysis. It is frequently used for air traffic control and power plants. Because of lack of complete information about the environment or unpredicted changes, the stochastic approach is a viable alternative. In this paper, different ways of introducing rechability under uncertainty are presented. A new concept of stochastic bisimulation is in...

  14. Stochastic Analysis and Related Topics

    Ustunel, Ali

    1988-01-01

    The Silvri Workshop was divided into a short summer school and a working conference, producing lectures and research papers on recent developments in stochastic analysis on Wiener space. The topics treated in the lectures relate to the Malliavin calculus, the Skorohod integral and nonlinear functionals of white noise. Most of the research papers are applications of these subjects. This volume addresses researchers and graduate students in stochastic processes and theoretical physics.

  15. Stochastic Geometric Partial Differential Equations

    Brzezniak, Z.; Goldys, B.; Ondreját, Martin

    1. Singapore : World Scientific Publishing Company, 2011 - (Zhao, H.; Truman, A.), s. 1-32 ISBN 978-981-4360-91-3. - (Interdisciplinary Mathematical Sciences. 12) R&D Projects: GA ČR GAP201/10/0752 Institutional support: RVO:67985556 Keywords : stochastic geometric * partial differential equations Subject RIV: BA - General Mathematics http://library.utia.cas.cz/separaty/2012/SI/ondrejat-stochastic geometric partial differential equations. pdf

  16. Stochastic optimization: beyond mathematical programming

    CERN. Geneva

    2015-01-01

    Stochastic optimization, among which bio-inspired algorithms, is gaining momentum in areas where more classical optimization algorithms fail to deliver satisfactory results, or simply cannot be directly applied. This presentation will introduce baseline stochastic optimization algorithms, and illustrate their efficiency in different domains, from continuous non-convex problems to combinatorial optimization problem, to problems for which a non-parametric formulation can help exploring unforeseen possible solution spaces.

  17. Markovian Projection of Stochastic Processes

    Bentata, Amel

    2012-01-01

    This PhD thesis studies various mathematical aspects of problems related to the Markovian projection of stochastic processes, and explores some ap- plications of the results obtained to mathematical finance, in the context of semimartingale models. Given a stochastic process ξ, modeled as a semimartingale, our aim is to build a Markov process X whose marginal laws are the same as ξ. This construction allows us to use analytical tools such as integro-differential equa- tions to explore or comp...

  18. Stochastic quantization and gauge theories

    Stochastic quantization is presented taking the Flutuation-Dissipation Theorem as a guide. It is shown that the original approach of Parisi and Wu to gauge theories fails to give the right results to gauge invariant quantities when dimensional regularization is used. Although there is a simple solution in an abelian theory, in the non-abelian case it is probably necessary to start from a BRST invariant action instead of a gauge invariant one. Stochastic regularizations are also discussed. (author)

  19. Stochastic comparisons of nonhomogeneous processes

    Belzunce, Félix; Lillo, Rosa E.; Ruiz, José M.; Shaked, Moshe

    2000-01-01

    The purpose of this paper is to describe various conditions on the parameters of pairs of nonhomogeneous Poisson or birth processes under which the corresponding epoch or inter-epoch times are stochastically ordered in various senses. We derive results involving the usual stochastic order, the multivariate hazard rate order, the multivariate likelihood ratio order, and the multivariate mean residual life order. A sample of applications involving generalized Yule processes, load-sharing models...

  20. Foliated stochastic calculus: Harmonic measures

    Catuogno, Pedro J.; Ledesma, Diego S.; Ruffino, Paulo R

    2010-01-01

    In this article we present an intrinsec construction of foliated Brownian motion via stochastic calculus adapted to foliation. The stochastic approach together with a proposed foliated vector calculus provide a natural method to work on harmonic measures. Other results include a decomposition of the Laplacian in terms of the foliated and basic Laplacians, a characterization of totally invariant measures and a differential equation for the density of harmonic measures.

  1. Stochastic quantization and gauge invariance

    A survey of the fundamental ideas about Parisi-Wu's Stochastic Quantization Method, with applications to Scalar, Gauge and Fermionic theories, is done. In particular, the Analytic Stochastic Regularization Scheme is used to calculate the polarization tensor for Quantum Electrodynamics with Dirac bosons or Fermions. The regularization influence is studied for both theories and an extension of this method for some supersymmetrical models is suggested. (author)

  2. Stochastic Methods in Biology

    Kallianpur, Gopinath; Hida, Takeyuki

    1987-01-01

    The use of probabilistic methods in the biological sciences has been so well established by now that mathematical biology is regarded by many as a distinct dis­ cipline with its own repertoire of techniques. The purpose of the Workshop on sto­ chastic methods in biology held at Nagoya University during the week of July 8-12, 1985, was to enable biologists and probabilists from Japan and the U. S. to discuss the latest developments in their respective fields and to exchange ideas on the ap­ plicability of the more recent developments in stochastic process theory to problems in biology. Eighteen papers were presented at the Workshop and have been grouped under the following headings: I. Population genetics (five papers) II. Measure valued diffusion processes related to population genetics (three papers) III. Neurophysiology (two papers) IV. Fluctuation in living cells (two papers) V. Mathematical methods related to other problems in biology, epidemiology, population dynamics, etc. (six papers) An important f...

  3. AA, stochastic precooling kicker

    1980-01-01

    The freshly injected antiprotons were subjected to fast stochastic "precooling", while a shutter shielded the deeply cooled antiproton stack from the violent action of the precooling kicker. In this picture, the injection orbit is to the left, the stack orbit to the far right, the separating shutter is in open position. After several seconds of precooling (in momentum and in the vertical plane), the shutter was opened briefly, so that by means of RF the precooled antiprotons could be transferred to the stack tail, where they were subjected to further cooling in momentum and both transverse planes, until they ended up, deeply cooled, in the stack core. The fast shutter, which had to open and close in a fraction of a second was an essential item of the cooling scheme and a mechanical masterpiece. Here the shutter is in the open position. The precooling pickups were of the same design, with the difference that the kickers had cooling circuits and the pickups not. 8401150 shows a precooling pickup with the shutte...

  4. Adaptation in stochastic environments

    Clark, Colib

    1993-01-01

    The classical theory of natural selection, as developed by Fisher, Haldane, and 'Wright, and their followers, is in a sense a statistical theory. By and large the classical theory assumes that the underlying environment in which evolution transpires is both constant and stable - the theory is in this sense deterministic. In reality, on the other hand, nature is almost always changing and unstable. We do not yet possess a complete theory of natural selection in stochastic environ­ ments. Perhaps it has been thought that such a theory is unimportant, or that it would be too difficult. Our own view is that the time is now ripe for the development of a probabilistic theory of natural selection. The present volume is an attempt to provide an elementary introduction to this probabilistic theory. Each author was asked to con­ tribute a simple, basic introduction to his or her specialty, including lively discussions and speculation. We hope that the book contributes further to the understanding of the roles of "Cha...

  5. Stacking with Stochastic Cooling

    Caspers, Friedhelm

    2004-01-01

    Accumulation of large stacks of antiprotons or ions with the aid of stochastic cooling is more delicate than cooling a constant intensity beam. Basically the difficulty stems from the fact that the optimized gain and the cooling rate are inversely proportional to the number of particles seen by the cooling system. Therefore, to maintain fast stacking, the newly injected batch has to be strongly protected from the Schottky noise of the stack. Vice versa the stack has to be efficiently shielded against the high gain cooling system for the injected beam. In the antiproton accumulators with stacking ratios up to 105, the problem is solved by radial separation of the injection and the stack orbits in a region of large dispersion. An array of several tapered cooling systems with a matched gain profile provides a continuous particle flux towards the high-density stack core. Shielding of the different systems from each other is obtained both through the spatial separation and via the revolution frequencies (filters)....

  6. Stacking with stochastic cooling

    Accumulation of large stacks of antiprotons or ions with the aid of stochastic cooling is more delicate than cooling a constant intensity beam. Basically the difficulty stems from the fact that the optimized gain and the cooling rate are inversely proportional to the number of particles 'seen' by the cooling system. Therefore, to maintain fast stacking, the newly injected batch has to be strongly 'protected' from the Schottky noise of the stack. Vice versa the stack has to be efficiently 'shielded' against the high gain cooling system for the injected beam. In the antiproton accumulators with stacking ratios up to 105 the problem is solved by radial separation of the injection and the stack orbits in a region of large dispersion. An array of several tapered cooling systems with a matched gain profile provides a continuous particle flux towards the high-density stack core. Shielding of the different systems from each other is obtained both through the spatial separation and via the revolution frequencies (filters). In the 'old AA', where the antiproton collection and stacking was done in one single ring, the injected beam was further shielded during cooling by means of a movable shutter. The complexity of these systems is very high. For more modest stacking ratios, one might use azimuthal rather than radial separation of stack and injected beam. Schematically half of the circumference would be used to accept and cool new beam and the remainder to house the stack. Fast gating is then required between the high gain cooling of the injected beam and the low gain stack cooling. RF-gymnastics are used to merge the pre-cooled batch with the stack, to re-create free space for the next injection, and to capture the new batch. This scheme is less demanding for the storage ring lattice, but at the expense of some reduction in stacking rate. The talk reviews the 'radial' separation schemes and also gives some considerations to the 'azimuthal' schemes

  7. Stability Analysis for Stochastic Optimization Problems

    2007-01-01

    Stochastic optimization offers a means of considering the objectives and constrains with stochastic parameters. However, it is generally difficult to solve the stochastic optimization problem by employing conventional methods for nonlinear programming when the number of random variables involved is very large. Neural network models and algorithms were applied to solve the stochastic optimization problem on the basis of the stability theory. Stability for stochastic programs was discussed. If random vector sequence converges to the random vector in the original problem in distribution, the optimal value of the corresponding approximation problems converges to the optimal value of the original stochastic optimization problem.

  8. The El Nino Stochastic Oscillator

    Burgers, G

    1997-01-01

    Anomalies during an El Nino are dominated by a single, irregularly oscillating, mode. Equatorial dynamics has been linked to delayed-oscillator models of this mode. Usually, the El Nino mode is regarded as an unstable mode of the coupled atmosphere system and the irregularity is attributed to noise and possibly chaos. Here a variation on the delayed oscillator is explored. In this stochastic-oscillator view, El Nino is a stable mode excited by noise. It is shown that the autocorrelation function of the observed NINO3.4 index is that of a stochastic oscillator, within the measurement uncertainty. Decadal variations as would occur in a stochastic oscillator are shown to be comparable to those observed, only the increase in the long-term mean around 1980 is rather large. The observed dependence of the seasonal cycle on the variance and the correlation is so large that it can not be attributed to the natural variability of a stationary stochastic oscillator. So the El Niño stochastic-oscillator parameters must d...

  9. Stochastic quantization and topological theories

    In the last two years topological quantum field theories (TQFT) have attached much attention. This paper reports that from the very beginning it was realized that due to a peculiar BRST-like symmetry these models admitted so-called Nicolai mapping: the Nicolai variables, in terms of which actions of the theories become gaussian, are nothing but (anti-) selfduality conditions or their generalizations. This fact became a starting point in the quest of possible stochastic interpretation to topological field theories. The reasons behind were quite simple and included, in particular, the well-known relations between stochastic processes and supersymmetry. The main goal would have been achieved, if it were possible to construct stochastic processes governed by Langevin or Fokker-Planck equations in a real Euclidean time leading to TQFT's path integrals (equivalently: to reformulate TQFTs as non-equilibrium phase dynamics of stochastic processes). Further on, if it would appear that these processes correspond to the stochastic quantization of theories of some definite kind, one could expect (d + 1)-dimensional TQFTs to share some common properties with d-dimensional ones

  10. Stochastic models: theory and simulation.

    Field, Richard V., Jr.

    2008-03-01

    Many problems in applied science and engineering involve physical phenomena that behave randomly in time and/or space. Examples are diverse and include turbulent flow over an aircraft wing, Earth climatology, material microstructure, and the financial markets. Mathematical models for these random phenomena are referred to as stochastic processes and/or random fields, and Monte Carlo simulation is the only general-purpose tool for solving problems of this type. The use of Monte Carlo simulation requires methods and algorithms to generate samples of the appropriate stochastic model; these samples then become inputs and/or boundary conditions to established deterministic simulation codes. While numerous algorithms and tools currently exist to generate samples of simple random variables and vectors, no cohesive simulation tool yet exists for generating samples of stochastic processes and/or random fields. There are two objectives of this report. First, we provide some theoretical background on stochastic processes and random fields that can be used to model phenomena that are random in space and/or time. Second, we provide simple algorithms that can be used to generate independent samples of general stochastic models. The theory and simulation of random variables and vectors is also reviewed for completeness.

  11. Transcriptional stochasticity in gene expression.

    Lipniacki, Tomasz; Paszek, Pawel; Marciniak-Czochra, Anna; Brasier, Allan R; Kimmel, Marek

    2006-01-21

    Due to the small number of copies of molecular species involved, such as DNA, mRNA and regulatory proteins, gene expression is a stochastic phenomenon. In eukaryotic cells, the stochastic effects primarily originate in regulation of gene activity. Transcription can be initiated by a single transcription factor binding to a specific regulatory site in the target gene. Stochasticity of transcription factor binding and dissociation is then amplified by transcription and translation, since target gene activation results in a burst of mRNA molecules, and each mRNA copy serves as a template for translating numerous protein molecules. In the present paper, we explore a mathematical approach to stochastic modeling. In this approach, the ordinary differential equations with a stochastic component for mRNA and protein levels in a single cells yield a system of first-order partial differential equations (PDEs) for two-dimensional probability density functions (pdf). We consider the following examples: Regulation of a single auto-repressing gene, and regulation of a system of two mutual repressors and of an activator-repressor system. The resulting PDEs are approximated by a system of many ordinary equations, which are then numerically solved. PMID:16039671

  12. Stochastic Model Checking of the Stochastic Quality Calculus

    Nielson, Flemming; Nielson, Hanne Riis; Zeng, Kebin

    2015-01-01

    The Quality Calculus uses quality binders for input to express strategies for continuing the computation even when the desired input has not been received. The Stochastic Quality Calculus adds generally distributed delays for output actions and real-time constraints on the quality binders for inp...... based on stochastic model checking and we compute closed form solutions for a number of interesting scenarios. The analyses are applied to the design of an intelligent smart electrical meter of the kind to be installed in European households by 2020....

  13. Stacking with stochastic cooling

    Caspers, Fritz E-mail: Fritz.Caspers@cern.ch; Moehl, Dieter

    2004-10-11

    Accumulation of large stacks of antiprotons or ions with the aid of stochastic cooling is more delicate than cooling a constant intensity beam. Basically the difficulty stems from the fact that the optimized gain and the cooling rate are inversely proportional to the number of particles 'seen' by the cooling system. Therefore, to maintain fast stacking, the newly injected batch has to be strongly 'protected' from the Schottky noise of the stack. Vice versa the stack has to be efficiently 'shielded' against the high gain cooling system for the injected beam. In the antiproton accumulators with stacking ratios up to 10{sup 5} the problem is solved by radial separation of the injection and the stack orbits in a region of large dispersion. An array of several tapered cooling systems with a matched gain profile provides a continuous particle flux towards the high-density stack core. Shielding of the different systems from each other is obtained both through the spatial separation and via the revolution frequencies (filters). In the 'old AA', where the antiproton collection and stacking was done in one single ring, the injected beam was further shielded during cooling by means of a movable shutter. The complexity of these systems is very high. For more modest stacking ratios, one might use azimuthal rather than radial separation of stack and injected beam. Schematically half of the circumference would be used to accept and cool new beam and the remainder to house the stack. Fast gating is then required between the high gain cooling of the injected beam and the low gain stack cooling. RF-gymnastics are used to merge the pre-cooled batch with the stack, to re-create free space for the next injection, and to capture the new batch. This scheme is less demanding for the storage ring lattice, but at the expense of some reduction in stacking rate. The talk reviews the 'radial' separation schemes and also gives some

  14. A stochastic approach to microphysics

    The presently widespread idea of ''vacuum population'', together with the quantum concept of vacuum fluctuations leads to assume a random level below that of matter. This stochastic approach starts by a reminder of the author's previous work, first on the relation of diffusion laws with the foundations of microphysics, and then on hadron spectrum. Following the latter, a random quark model is advanced; it gives to quark pairs properties similar to those of a harmonic oscillator or an elastic string, imagined as an explanation to their asymptotic freedom and their confinement. The stochastic study of such interactions as electron-nucleon, jets in e+e- collisions, or pp → π0 + X, gives form factors closely consistent with experiment. The conclusion is an epistemological comment (complementarity between stochastic and quantum domains, E.P.R. paradox, etc...)

  15. Correlation functions in stochastic inflation

    Combining the stochastic and δ N formalisms, we derive non-perturbative analytical expressions for all correlation functions of scalar perturbations in single-field, slow-roll inflation. The standard, classical formulas are recovered as saddle-point limits of the full results. This yields a classicality criterion that shows that stochastic effects are small only if the potential is sub-Planckian and not too flat. The saddle-point approximation also provides an expansion scheme for calculating stochastic corrections to observable quantities perturbatively in this regime. In the opposite regime, we show that a strong suppression in the power spectrum is generically obtained, and we comment on the physical implications of this effect. (orig.)

  16. Applied probability and stochastic processes

    Sumita, Ushio

    1999-01-01

    Applied Probability and Stochastic Processes is an edited work written in honor of Julien Keilson. This volume has attracted a host of scholars in applied probability, who have made major contributions to the field, and have written survey and state-of-the-art papers on a variety of applied probability topics, including, but not limited to: perturbation method, time reversible Markov chains, Poisson processes, Brownian techniques, Bayesian probability, optimal quality control, Markov decision processes, random matrices, queueing theory and a variety of applications of stochastic processes. The book has a mixture of theoretical, algorithmic, and application chapters providing examples of the cutting-edge work that Professor Keilson has done or influenced over the course of his highly-productive and energetic career in applied probability and stochastic processes. The book will be of interest to academic researchers, students, and industrial practitioners who seek to use the mathematics of applied probability i...

  17. Stochastic approach to quantum mechanics

    A critical review of the Copenhagen interpretation of quantum mechanics, both in its early and later versions, shows that, as a model for the individual system, it suffers from severe conceptual difficulties, particularly with regard to the problem of measurement. Nor are the most common hidden variable interpretations free from complications in providing a physical explanation for the properties of the quantum system. A promising alternative interpretation is the stochastic approach, which presumes that every physical system is continually interacting with a hidden thermostat. This thermostat has a randomizing effect on the position variable and necessitates a probabilistic description of the path of the system. If the concepts of both backward and forward diffusion are utilized, it is possible to construct a straightforward stochastic rule for computing the mean value of any observable which has a classical operational definition. Estimates based upon this rule agree with standard quantum predictions up to second order in momentum but disagree in higher order terms. The rule also predicts the average measurement results for observables which cannot, in general, be estimated by the usual operator formalism. The Schroedinger equation is derivable by the stochastic approach if Newton's Second Law is assumed. Also, quantization of energy and the Heisenberg uncertainty principle are readily explained through the stochastic model, but spatial quantization is not predicted by this method. If the magnitude of the free velocity of the relativistic stochastic particle is assumed to be the speed of light, as in Dirac's relativistic theory of the electron, it is possible to provide a relativistic extension of the stochastic approach and to derive (from this extension) the Klein-Gordon equation

  18. Probability, Statistics, and Stochastic Processes

    Olofsson, Peter

    2011-01-01

    A mathematical and intuitive approach to probability, statistics, and stochastic processes This textbook provides a unique, balanced approach to probability, statistics, and stochastic processes. Readers gain a solid foundation in all three fields that serves as a stepping stone to more advanced investigations into each area. This text combines a rigorous, calculus-based development of theory with a more intuitive approach that appeals to readers' sense of reason and logic, an approach developed through the author's many years of classroom experience. The text begins with three chapters that d

  19. Stochastic vehicle routing with recourse

    Gørtz, Inge Li; Nagarajan, Viswanath; Saket, Rishi

    We study the classic Vehicle Routing Problem in the setting of stochastic optimization with recourse. StochVRP is a two-stage problem, where demand is satisfied using two routes: fixed and recourse. The fixed route is computed using only a demand distribution. Then after observing the demand...... instantiations, a recourse route is computed - but costs here become more expensive by a factor λ. We present an O(log2n ·log(nλ))-approximation algorithm for this stochastic routing problem, under arbitrary distributions. The main idea in this result is relating StochVRP to a special case of submodular...

  20. Stochastic geometry and its applications

    Chiu, Sung Nok; Kendall, Wilfrid S; Mecke, Joseph

    2013-01-01

    An extensive update to a classic text Stochastic geometry and spatial statistics play a fundamental role in many modern branches of physics, materials sciences, engineering, biology and environmental sciences. They offer successful models for the description of random two- and three-dimensional micro and macro structures and statistical methods for their analysis. The previous edition of this book has served as the key reference in its field for over 18 years and is regarded as the best treatment of the subject of stochastic geometry, both as a subject with vital a

  1. Stochastic Optimization of Complex Systems

    Birge, John R. [University of Chicago

    2014-03-20

    This project focused on methodologies for the solution of stochastic optimization problems based on relaxation and penalty methods, Monte Carlo simulation, parallel processing, and inverse optimization. The main results of the project were the development of a convergent method for the solution of models that include expectation constraints as in equilibrium models, improvement of Monte Carlo convergence through the use of a new method of sample batch optimization, the development of new parallel processing methods for stochastic unit commitment models, and the development of improved methods in combination with parallel processing for incorporating automatic differentiation methods into optimization.

  2. Stochastic and infinite dimensional analysis

    Carpio-Bernido, Maria; Grothaus, Martin; Kuna, Tobias; Oliveira, Maria; Silva, José

    2016-01-01

    This volume presents a collection of papers covering applications from a wide range of systems with infinitely many degrees of freedom studied using techniques from stochastic and infinite dimensional analysis, e.g. Feynman path integrals, the statistical mechanics of polymer chains, complex networks, and quantum field theory. Systems of infinitely many degrees of freedom create their particular mathematical challenges which have been addressed by different mathematical theories, namely in the theories of stochastic processes, Malliavin calculus, and especially white noise analysis. These proceedings are inspired by a conference held on the occasion of Prof. Ludwig Streit’s 75th birthday and celebrate his pioneering and ongoing work in these fields.

  3. QB1 - Stochastic Gene Regulation

    Munsky, Brian [Los Alamos National Laboratory

    2012-07-23

    Summaries of this presentation are: (1) Stochastic fluctuations or 'noise' is present in the cell - Random motion and competition between reactants, Low copy, quantization of reactants, Upstream processes; (2) Fluctuations may be very important - Cell-to-cell variability, Cell fate decisions (switches), Signal amplification or damping, stochastic resonances; and (3) Some tools are available to mode these - Kinetic Monte Carlo simulations (SSA and variants), Moment approximation methods, Finite State Projection. We will see how modeling these reactions can tell us more about the underlying processes of gene regulation.

  4. Stochastic Modelling of Hydrologic Systems

    Jonsdottir, Harpa

    2007-01-01

    introduction and an overview of the papers published. Then an introduction to basic concepts in hydrology along with a description of hydrological data is given. Finally an introduction to stochastic modelling is given. The second part contains the research papers. In the research papers the stochastic methods...... are described, as at the time of publication these methods represent new contribution to hydrology. The second part also contains additional description of software used and a brief introduction to stiff systems. The system in one of the papers is stiff....

  5. Stochastic modeling of Lagrangian accelerations

    Reynolds, Andy

    2002-11-01

    It is shown how Sawford's second-order Lagrangian stochastic model (Phys. Fluids A 3, 1577-1586, 1991) for fluid-particle accelerations can be combined with a model for the evolution of the dissipation rate (Pope and Chen, Phys. Fluids A 2, 1437-1449, 1990) to produce a Lagrangian stochastic model that is consistent with both the measured distribution of Lagrangian accelerations (La Porta et al., Nature 409, 1017-1019, 2001) and Kolmogorov's similarity theory. The later condition is found not to be satisfied when a constant dissipation rate is employed and consistency with prescribed acceleration statistics is enforced through fulfilment of a well-mixed condition.

  6. Stochastic cooling of bunched beams

    Numerical simulation studies are presented for transverse and longitudinal stochastic cooling of bunched particle beams. Radio frequency buckets of various shapes (e.g. rectangular, parabolic well, single sinusoidal waveform) are used to investigate the enhancement of phase space cooling by nonlinearities of synchrotron motion. The connection between the notions of Landau damping for instabilities and mixing for stochastic cooling are discussed. In particular, the need for synchrotron frequency spread for both Landau damping and good mixing is seen to be comparable for bunched beams

  7. Work fluctuations and stochastic resonance

    We study Brownian particle motion in a double-well potential driven by an ac force. This system exhibits the phenomenon of stochastic resonance. Distribution of work done on the system over a drive period in the time asymptotic regime has been calculated. We show that fluctuations in the input energy or work done dominate the mean value. The mean value of work done over a period as a function of noise strength can also be used to characterize stochastic resonance in the system. We also discuss the validity of steady state fluctuation theorems in this particular system

  8. Schwinger Mechanism with Stochastic Quantization

    Fukushima, Kenji

    2014-01-01

    We prescribe a formulation of the particle production with real-time Stochastic Quantization. To construct the retarded and the time-ordered propagators we decompose the stochastic variables into positive- and negative-energy parts. In this way we demonstrate how to derive the Schwinger mechanism under a time-dependent electric field. We also discuss a physical interpretation with help of numerical simulations and develop an analogue to the one-dimensional scattering with the non-relativistic Schroedinger equation. We can then reformulate the Schwinger mechanism as the high-energy quantum reflection problem rather than tunneling.

  9. Stochastic Simulation of Turing Patterns

    FU Zheng-Ping; XU Xin-Hang; WANG Hong-Li; OUYANG Qi

    2008-01-01

    @@ We investigate the effects of intrinsic noise on Turing pattern formation near the onset of bifurcation from the homogeneous state to Turing pattern in the reaction-diffusion Brusselator. By performing stochastic simulations of the master equation and using Gillespie's algorithm, we check the spatiotemporal behaviour influenced by internal noises. We demonstrate that the patterns of occurrence frequency for the reaction and diffusion processes are also spatially ordered and temporally stable. Turing patterns are found to be robust against intrinsic fluctuations. Stochastic simulations also reveal that under the influence of intrinsic noises, the onset of Turing instability is advanced in comparison to that predicted deterministically.

  10. Stochastic epidemic models: a survey

    Britton, Tom

    2009-01-01

    This paper is a survey paper on stochastic epidemic models. A simple stochastic epidemic model is defined and exact and asymptotic model properties (relying on a large community) are presented. The purpose of modelling is illustrated by studying effects of vaccination and also in terms of inference procedures for important parameters, such as the basic reproduction number and the critical vaccination coverage. Several generalizations towards realism, e.g. multitype and household epidemic models, are also presented, as is a model for endemic diseases.

  11. Stochastic model in microwave propagation

    Further experimental results of delay time in microwave propagation are reported in the presence of a lossy medium (wood). The measurements show that the presence of a lossy medium makes the propagation slightly superluminal. The results are interpreted on the basis of a stochastic (or path integral) model, showing how this model is able to describe each kind of physical system in which multi-path trajectories are present. -- Highlights: ► We present new experimental results on electromagnetic “anomalous” propagation. ► We apply a path integral theoretical model to wave propagation. ► Stochastic processes and multi-path trajectories in propagation are considered.

  12. Stochastic heating of cooling flows

    Pavlovski, Georgi

    2009-01-01

    It is generally accepted that the heating of gas in clusters of galaxies by active galactic nuclei (AGN) is a form of feedback. Feedback is required to ensure a long term, sustainable balance between heating and cooling. This work investigates the impact of proportional stochastic feedback on the energy balance in the intracluster medium. Using a generalised analytical model for a cluster atmosphere, it is shown that an energy equilibrium can be reached exponentially quickly. Applying the tools of stochastic calculus it is demonstrated that the result is robust with regard to the model parameters, even though they affect the amount of variability in the system.

  13. Stochastic geometry for image analysis

    Descombes, Xavier

    2013-01-01

    This book develops the stochastic geometry framework for image analysis purpose. Two main frameworks are  described: marked point process and random closed sets models. We derive the main issues for defining an appropriate model. The algorithms for sampling and optimizing the models as well as for estimating parameters are reviewed.  Numerous applications, covering remote sensing images, biological and medical imaging, are detailed.  This book provides all the necessary tools for developing an image analysis application based on modern stochastic modeling.

  14. Elimination of stochasticity in stellarators

    A method for optimizing stellarator vacuum magnetic fields is introduced. Application of this method shows that the stochasticity of vacuum magnetic fields can be made negligible by proper choice of the coil configuration. This optimization is shown to increase the equilibrium β-limit by factors of two or more over that of the simple, straight coil winding law. This method is general and ought to be applicable to other systems in which stochasticity: (1) is a problem, yet (2) is affected by the design parameters

  15. Exact Algorithms for Solving Stochastic Games

    Hansen, Kristoffer Arnsfelt; Koucky, Michal; Lauritzen, Niels;

    2012-01-01

    Shapley's discounted stochastic games, Everett's recursive games and Gillette's undiscounted stochastic games are classical models of game theory describing two-player zero-sum games of potentially infinite duration. We describe algorithms for exactly solving these games....

  16. Theory, technology, and technique of stochastic cooling

    The theory and technological implementation of stochastic cooling is described. Theoretical and technological limitations are discussed. Data from existing stochastic cooling systems are shown to illustrate some useful techniques

  17. When greediness fails: Examples from stochastic scheduling

    Uetz, Marc

    2002-01-01

    The purpose of this paper is to present examples which show that deterministic and stochastic scheduling problems often have a surprisingly different behavior. In particular, it demonstrates some seemingly counterintuitive properties of optimal scheduling policies for stochastic machine scheduling problems.

  18. Enhancing stochastic kriging metamodels with gradient estimators

    Chen, X.; Ankenman, B. E.; Nelson, B. L.

    2013-01-01

    Stochastic kriging is a new metamodeling technique for effectively representing the mean response surface implied by a stochastic simulation; it takes into account both stochastic simulation noise and uncertainty about the underlying response surface of interest. We show theoretically, through some simplified models, that incorporating gradient estimators into stochastic kriging tends to significantly improve surface prediction. To address the issue of which type of gradient estimator to use,...

  19. Stochastic modeling and analysis of telecoms networks

    Decreusefond, Laurent

    2012-01-01

    This book addresses the stochastic modeling of telecommunication networks, introducing the main mathematical tools for that purpose, such as Markov processes, real and spatial point processes and stochastic recursions, and presenting a wide list of results on stability, performances and comparison of systems.The authors propose a comprehensive mathematical construction of the foundations of stochastic network theory: Markov chains, continuous time Markov chains are extensively studied using an original martingale-based approach. A complete presentation of stochastic recursions from an

  20. Quantum stochastic calculus with maximal operator domains

    Lindsay, J Martin; Attal, Stéphane

    2004-01-01

    Quantum stochastic calculus is extended in a new formulation in which its stochastic integrals achieve their natural and maximal domains. Operator adaptedness, conditional expectations and stochastic integrals are all defined simply in terms of the orthogonal projections of the time filtration of Fock space, together with sections of the adapted gradient operator. Free from exponential vector domains, our stochastic integrals may be satisfactorily composed yielding quantum Itô formulas for op...

  1. Transport in a stochastic magnetic field

    White, R.B.; Wu, Yanlin [Princeton Univ., NJ (United States). Plasma Physics Lab.; Rax, J.M. [Association Euratom-CEA, Centre d`Etudes Nucleaires de Cadarache, 13 -Saint-Paul-lez-Durance (France). Dept. de Recherches sur la Fusion Controlee

    1992-09-01

    Collisional heat transport in a stochastic magnetic field configuration is investigated. Well above stochastic threshold, a numerical solution of a Chirikov-Taylor model shows a short-time nonlocal regime, but at large time the Rechester-Rosenbluth effective diffusion is confirmed. Near stochastic threshold, subdiffusive behavior is observed for short mean free paths. The nature of this subdiffusive behavior is understood in terms of the spectrum of islands in the stochastic sea.

  2. Stability of stochastic switched SIRS models

    Meng, Xiaoying; Liu, Xinzhi; Deng, Feiqi

    2011-11-01

    Stochastic stability problems of a stochastic switched SIRS model with or without distributed time delay are considered. By utilizing the Lyapunov methods, sufficient stability conditions of the disease-free equilibrium are established. Stability conditions about the subsystem of the stochastic switched SIRS systems are also obtained.

  3. Models and algorithms for stochastic online scheduling

    Megow, N.; Uetz, M.J.; Vredeveld, T.

    2006-01-01

    We consider a model for scheduling under uncertainty. In this model, we combine the main characteristics of online and stochastic scheduling in a simple and natural way. Job processing times are assumed to be stochastic, but in contrast to traditional stochastic scheduling models, we assume that job

  4. On physical diffusion and stochastic diffusion

    Narasimhan, T.N.

    2010-01-01

    Although the same mathematical expression is used to describe physical diffusion and stochastic diffusion, there are intrinsic similarities and differences in their nature. A comparative study shows that characteristic terms of physical and stochastic diffusion cannot be placed exactly in one-to-one correspondence. Therefore, judgment needs to be exercised in transferring ideas between physical and stochastic diffusion.

  5. Information theory in stochastic process

    Methods for calculating the Kolmogorov-Sinai disorder from Gaussian Probability density functions and by using information theory for chaotic dynamical systems are suggested. The autoregressive models may prove stochastic deterministic both for chaotic and non-chaotic dynamical systems. (author)

  6. The bicriterion stochastic knapsack problem

    Andersen, Kim Allan

    We discuss the bicriterion stochastic knapsack problem. It is described as follows. We have a known capacity of some resource, and a finite set of projects. Each project requires some units of the resource which is not known in advance, but given by a discrete probability distribution with a finite...

  7. Stochastic Subspace Modelling of Turbulence

    Sichani, Mahdi Teimouri; Pedersen, B. J.; Nielsen, Søren R.K.

    Turbulence of the incoming wind field is of paramount importance to the dynamic response of civil engineering structures. Hence reliable stochastic models of the turbulence should be available from which time series can be generated for dynamic response and structural safety analysis. In the paper...

  8. Stochastic Processes in Epidemic Theory

    Lefèvre, Claude; Picard, Philippe

    1990-01-01

    This collection of papers gives a representative cross-selectional view of recent developments in the field. After a survey paper by C. Lefèvre, 17 other research papers look at stochastic modeling of epidemics, both from a theoretical and a statistical point of view. Some look more specifically at a particular disease such as AIDS, malaria, schistosomiasis and diabetes.

  9. Stochastic resin transfer molding process

    Park, M

    2016-01-01

    We consider one-dimensional and two-dimensional models of stochastic resin transfer molding process, which are formulated as random moving boundary problems. We study their properties, analytically in the one-dimensional case and numerically in the two-dimensional case. We show how variability of time to fill depends on correlation lengths and smoothness of a random permeability field.

  10. Stochastic Modelling of River Geometry

    Sørensen, John Dalsgaard; Schaarup-Jensen, K.

    1996-01-01

    Numerical hydrodynamic river models are used in a large number of applications to estimate critical events for rivers. These estimates are subject to a number of uncertainties. In this paper, the problem to evaluate these estimates using probabilistic methods is considered. Stochastic models for...... river geometries are formulated and a coupling between hydraulic computational methods and numerical reliability methods is presented....

  11. Algorithmic advances in stochastic programming

    Morton, D.P.

    1993-07-01

    Practical planning problems with deterministic forecasts of inherently uncertain parameters often yield unsatisfactory solutions. Stochastic programming formulations allow uncertain parameters to be modeled as random variables with known distributions, but the size of the resulting mathematical programs can be formidable. Decomposition-based algorithms take advantage of special structure and provide an attractive approach to such problems. We consider two classes of decomposition-based stochastic programming algorithms. The first type of algorithm addresses problems with a ``manageable`` number of scenarios. The second class incorporates Monte Carlo sampling within a decomposition algorithm. We develop and empirically study an enhanced Benders decomposition algorithm for solving multistage stochastic linear programs within a prespecified tolerance. The enhancements include warm start basis selection, preliminary cut generation, the multicut procedure, and decision tree traversing strategies. Computational results are presented for a collection of ``real-world`` multistage stochastic hydroelectric scheduling problems. Recently, there has been an increased focus on decomposition-based algorithms that use sampling within the optimization framework. These approaches hold much promise for solving stochastic programs with many scenarios. A critical component of such algorithms is a stopping criterion to ensure the quality of the solution. With this as motivation, we develop a stopping rule theory for algorithms in which bounds on the optimal objective function value are estimated by sampling. Rules are provided for selecting sample sizes and terminating the algorithm under which asymptotic validity of confidence interval statements for the quality of the proposed solution can be verified. Issues associated with the application of this theory to two sampling-based algorithms are considered, and preliminary empirical coverage results are presented.

  12. Stochastic Analysis : A Series of Lectures

    Dozzi, Marco; Flandoli, Franco; Russo, Francesco

    2015-01-01

    This book presents in thirteen refereed survey articles an overview of modern activity in stochastic analysis, written by leading international experts. The topics addressed include stochastic fluid dynamics and regularization by noise of deterministic dynamical systems; stochastic partial differential equations driven by Gaussian or Lévy noise, including the relationship between parabolic equations and particle systems, and wave equations in a geometric framework; Malliavin calculus and applications to stochastic numerics; stochastic integration in Banach spaces; porous media-type equations; stochastic deformations of classical mechanics and Feynman integrals and stochastic differential equations with reflection. The articles are based on short courses given at the Centre Interfacultaire Bernoulli of the Ecole Polytechnique Fédérale de Lausanne, Switzerland, from January to June 2012. They offer a valuable resource not only for specialists, but also for other researchers and Ph.D. students in the fields o...

  13. Stochastic Reachability Analysis of Hybrid Systems

    Bujorianu, Luminita Manuela

    2012-01-01

    Stochastic reachability analysis (SRA) is a method of analyzing the behavior of control systems which mix discrete and continuous dynamics. For probabilistic discrete systems it has been shown to be a practical verification method but for stochastic hybrid systems it can be rather more. As a verification technique SRA can assess the safety and performance of, for example, autonomous systems, robot and aircraft path planning and multi-agent coordination but it can also be used for the adaptive control of such systems. Stochastic Reachability Analysis of Hybrid Systems is a self-contained and accessible introduction to this novel topic in the analysis and development of stochastic hybrid systems. Beginning with the relevant aspects of Markov models and introducing stochastic hybrid systems, the book then moves on to coverage of reachability analysis for stochastic hybrid systems. Following this build up, the core of the text first formally defines the concept of reachability in the stochastic framework and then...

  14. CANCER

    N. Kavoussi

    1973-09-01

    Full Text Available There are many carcinogenetic elements in industry and it is for this reason that study and research concerning the effect of these materials is carried out on a national and international level. The establishment and growth of cancer are affected by different factors in two main areas:-1 The nature of the human or animal including sex, age, point and method of entry, fat metabolism, place of agglomeration of carcinogenetic material, amount of material absorbed by the body and the immunity of the body.2 The different nature of the carcinogenetic material e.g. physical, chemical quality, degree of solvency in fat and purity of impurity of the element. As the development of cancer is dependent upon so many factors, it is extremely difficult to determine whether a causative element is principle or contributory. Some materials are not carcinogenetic when they are pure but become so when they combine with other elements. All of this creates an industrial health problem in that it is almost impossible to plan an adequate prevention and safety program. The body through its system of immunity protects itself against small amounts of carcinogens but when this amount increases and reaches a certain level the body is not longer able to defend itself. ILO advises an effective protection campaign against cancer based on the Well –equipped laboratories, Well-educated personnel, the establishment of industrial hygiene within factories, the regular control of safety systems, and the implementation of industrial health principles and research programs.

  15. Self-Organising Stochastic Encoders

    Luttrell, Stephen

    2010-01-01

    The processing of mega-dimensional data, such as images, scales linearly with image size only if fixed size processing windows are used. It would be very useful to be able to automate the process of sizing and interconnecting the processing windows. A stochastic encoder that is an extension of the standard Linde-Buzo-Gray vector quantiser, called a stochastic vector quantiser (SVQ), includes this required behaviour amongst its emergent properties, because it automatically splits the input space into statistically independent subspaces, which it then separately encodes. Various optimal SVQs have been obtained, both analytically and numerically. Analytic solutions which demonstrate how the input space is split into independent subspaces may be obtained when an SVQ is used to encode data that lives on a 2-torus (e.g. the superposition of a pair of uncorrelated sinusoids). Many numerical solutions have also been obtained, using both SVQs and chains of linked SVQs: (1) images of multiple independent targets (encod...

  16. Stochastic stability of traffic maps

    We study the ergodic properties of a family of traffic maps acting in the space of bi-infinite sequences of real numbers. The corresponding dynamics mimics the motion of vehicles in a simple traffic flow, which explains the name. Using connections to topological Markov chains we obtain nontrivial invariant measures, prove their stochastic stability and calculate the topological entropy. Technically these results in the deterministic setting are related to the construction of measures of maximal entropy via measures uniformly distributed on periodic points of a given period, while in the random setting we construct (spatially) Markov invariant measures directly. In distinction to conventional results the limiting measures in the non-lattice case are non-ergodic. The average velocity of individual ‘vehicles’ as a function of their density and its stochastic stability is studied as well. (paper)

  17. Stochastic problems in population genetics

    Maruyama, Takeo

    1977-01-01

    These are" notes based on courses in Theoretical Population Genetics given at the University of Texas at Houston during the winter quarter, 1974, and at the University of Wisconsin during the fall semester, 1976. These notes explore problems of population genetics and evolution involving stochastic processes. Biological models and various mathematical techniques are discussed. Special emphasis is given to the diffusion method and an attempt is made to emphasize the underlying unity of various problems based on the Kolmogorov backward equation. A particular effort was made to make the subject accessible to biology students who are not familiar with stochastic processes. The references are not exhaustive but were chosen to provide a starting point for the reader interested in pursuing the subject further. Acknowledgement I would like to use this opportunity to express my thanks to Drs. J. F. Crow, M. Nei and W. J. Schull for their hospitality during my stays at their universities. I am indebted to Dr. M. Kimura...

  18. Stochastic models of cell motility

    Gradinaru, Cristian

    2012-01-01

    interdisciplinary field focusing on the study of biological processes at the nanoscale level, with a range of technological applications in medicine and biological research, has emerged. The work presented in this thesis is at the interface of cell biology, image processing, and stochastic modeling. The stochastic...... coefficient. By adding a persistence component to simple random motion I introduce the standard Ornstein-Uhlenbeck process. I build on this commonly used cell motility model to address the challenges of working with real-life data: positional (centroid coordinate measuring) error and time discretization (due...... generalizing the Orstein-Uhlenbeck process to study cell motility on anisotropic substrates. I apply the general model to analyze cell motility on a series of anisotropic substrates and discuss the implications of our observations. This work is potentially useful to cell biologists by addressing their need for...

  19. Limits for Stochastic Reaction Networks

    Cappelletti, Daniele

    Reaction systems have been introduced in the 70s to model biochemical systems. Nowadays their range of applications has increased and they are fruitfully used in dierent elds. The concept is simple: some chemical species react, the set of chemical reactions form a graph and a rate function is...... associated with each reaction. Such functions describe the speed of the dierent reactions, or their propensities. Two modelling regimes are then available: the evolution of the dierent species concentrations can be deterministically modelled through a system of ODE, while the counts of the dierent species at...... a certain time are stochastically modelled by means of a continuous-time Markov chain. Our work concerns primarily stochastic reaction systems, and their asymptotic properties. In Paper I, we consider a reaction system with intermediate species, i.e. species that are produced and fast degraded along...

  20. Stochastic Modeling of Soil Salinity

    Suweis, S; Van der Zee, S E A T M; Daly, E; Maritan, A; Porporato, A; 10.1029/2010GL042495

    2012-01-01

    A minimalist stochastic model of primary soil salinity is proposed, in which the rate of soil salinization is determined by the balance between dry and wet salt deposition and the intermittent leaching events caused by rainfall events. The long term probability density functions of salt mass and concentration are found by reducing the coupled soil moisture and salt mass balance equation to a single stochastic differential equation driven by multiplicative Poisson noise. The novel analytical solutions provide insight on the interplay of the main soil, plant and climate parameters responsible for long-term soil salinization. In particular, they show the existence of two distinct regimes, one where the mean salt mass remains nearly constant (or decreases) with increasing rainfall frequency, and another where mean salt content increases markedly with increasing rainfall frequency. As a result, relatively small reductions of rainfall in drier climates may entail dramatic shifts in long-term soil salinization trend...

  1. Stochastic quantization in general relativity

    The stochastic quantization method of Parisi and Wu is briefly reviewed stressing its formal resemblance to the Einstein-Smoluchowski theory of Brownian motion. In order to make it applicable in the context of General Relativity, we present a generalization of the method to the case of Lorentzian signature of the space-time metric. It is shown that this approach has non-trivial implications even for linear quantum fields in curved space-time, where it introduces preferred quantum states characterized by the analyticity of the Feynman propagator in the mass parameter. Finally we propose a stochastic quantization scheme for the full nonlinear Einstein theory of gravitation. It employs the concept of a metric in field configuration space and is based mathematically on Ito's calculus. Non-trivial implications for the gravitational path integral measure and for perturbation theory are pointed out. (Author)

  2. Concentration Bounds for Stochastic Approximations

    Frikha, Noufel

    2012-01-01

    We obtain non asymptotic concentration bounds for two kinds of stochastic approximations. We first consider the deviations between the expectation of a given function of the Euler scheme of some diffusion process at a fixed deterministic time and its empirical mean obtained by the Monte-Carlo procedure. We then give some estimates concerning the deviation between the value at a given time-step of a stochastic approximation algorithm and its target. Under suitable assumptions both concentration bounds turn out to be Gaussian. The key tool consists in exploiting accurately the concentration properties of the increments of the schemes. For the first case, as opposed to the previous work of Lemaire and Menozzi (EJP, 2010), we do not have any systematic bias in our estimates. Also, no specific non-degeneracy conditions are assumed.

  3. Fourier analysis and stochastic processes

    Brémaud, Pierre

    2014-01-01

    This work is unique as it provides a uniform treatment of the Fourier theories of functions (Fourier transforms and series, z-transforms), finite measures (characteristic functions, convergence in distribution), and stochastic processes (including arma series and point processes). It emphasises the links between these three themes. The chapter on the Fourier theory of point processes and signals structured by point processes is a novel addition to the literature on Fourier analysis of stochastic processes. It also connects the theory with recent lines of research such as biological spike signals and ultrawide-band communications. Although the treatment is mathematically rigorous, the convivial style makes the book accessible to a large audience. In particular, it will be interesting to anyone working in electrical engineering and communications, biology (point process signals) and econometrics (arma models). A careful review of the prerequisites (integration and probability theory in the appendix, Hilbert spa...

  4. Stochastic integration and differential equations

    Protter, Philip E

    2003-01-01

    It has been 15 years since the first edition of Stochastic Integration and Differential Equations, A New Approach appeared, and in those years many other texts on the same subject have been published, often with connections to applications, especially mathematical finance. Yet in spite of the apparent simplicity of approach, none of these books has used the functional analytic method of presenting semimartingales and stochastic integration. Thus a 2nd edition seems worthwhile and timely, though it is no longer appropriate to call it "a new approach". The new edition has several significant changes, most prominently the addition of exercises for solution. These are intended to supplement the text, but lemmas needed in a proof are never relegated to the exercises. Many of the exercises have been tested by graduate students at Purdue and Cornell Universities. Chapter 3 has been completely redone, with a new, more intuitive and simultaneously elementary proof of the fundamental Doob-Meyer decomposition theorem, t...

  5. Stochastic models for atmospheric dispersion

    Ditlevsen, Ove Dalager

    2003-01-01

    Simple stochastic differential equation models have been applied by several researchers to describe the dispersion of tracer particles in the planetary atmospheric boundary layer and to form the basis for computer simulations of particle paths. To obtain the drift coefficient, empirical vertical...... variation by height is adopted. A particular problem for simulation studies with finite time steps is the construction of a reflection rule different from the rule of perfect reflection at the boundaries such that the rule complies with the imposed skewness of the velocity distribution for particle...... positions close to the boundaries. Different rules have been suggested in the literature with justifications based on simulation studies. Herein the relevant stochastic differential equation model is formulated in a particular way. The formulation is based on the marginal transformation of the position...

  6. The analysis of stochastic stability of stochastic models that describe tumor-immune systems

    Chis, O; Opris, D

    2009-01-01

    In this paper we investigate some stochastic models for tumor-immune systems. To describe these models, we used a Wiener process, as the noise has a stabilization effect. Their dynamics are studied in terms of stochastic stability in the equilibrium points, by constructing the Lyapunov exponent, depending on the parameters that describe the model. Stochastic stability was also proved by constructing a Lyapunov function. We have studied and and analyzed a Kuznetsov-Taylor like stochastic model and a Bell stochastic model for tumor-immune systems. These stochastic models are studied from stability point of view and they were represented using the second Euler scheme and Maple 12 software.

  7. Stochastic modelling of animal movement

    Smouse, Peter E.; Focardi, Stefano; Moorcroft, Paul R.; Kie, John G.; Forester, James D.; Morales, Juan M.

    2010-01-01

    Modern animal movement modelling derives from two traditions. Lagrangian models, based on random walk behaviour, are useful for multi-step trajectories of single animals. Continuous Eulerian models describe expected behaviour, averaged over stochastic realizations, and are usefully applied to ensembles of individuals. We illustrate three modern research arenas. (i) Models of home-range formation describe the process of an animal ‘settling down’, accomplished by including one or more focal poi...

  8. Stochastic Blockmodeling for Online Advertising

    Chen, Li; Patton, Matthew

    2014-01-01

    Online advertising is an important and huge industry. Having knowledge of the website attributes can contribute greatly to business strategies for ad-targeting, content display, inventory purchase or revenue prediction. Classical inferences on users and sites impose challenge, because the data is voluminous, sparse, high-dimensional and noisy. In this paper, we introduce a stochastic blockmodeling for the website relations induced by the event of online user visitation. We propose two cluster...

  9. Stochastic Volatility and DSGE Models

    Andreasen, Martin Møller

    This paper argues that a specification of stochastic volatility commonly used to analyze the Great Moderation in DSGE models may not be appropriate, because the level of a process with this specification does not have conditional or unconditional moments. This is unfortunate because agents may as a...... result expect productivity and hence consumption to be inifinite in all future periods. This observation is followed by three ways to overcome the problem....

  10. Stochastic control of traffic patterns

    Gaididei, Yuri B.; Gorria, Carlos; Berkemer, Rainer;

    2013-01-01

    A stochastic modulation of the safety distance can reduce traffic jams. It is found that the effect of random modulation on congestive flow formation depends on the spatial correlation of the noise. Jam creation is suppressed for highly correlated noise. The results demonstrate the advantage of...... heterogeneous performance of the drivers in time as well as individually. This opens the possibility for the construction of technical tools to control traffic jam formation....

  11. Stochastic Annealing for Variational Inference

    Gultekin, San; Zhang, Aonan; Paisley, John

    2015-01-01

    We empirically evaluate a stochastic annealing strategy for Bayesian posterior optimization with variational inference. Variational inference is a deterministic approach to approximate posterior inference in Bayesian models in which a typically non-convex objective function is locally optimized over the parameters of the approximating distribution. We investigate an annealing method for optimizing this objective with the aim of finding a better local optimal solution and compare with determin...

  12. Lower Hybrid Wavepacket Stochasticity Revisited

    Fuchs, Vladimír; Laqua, H.P.; Krlín, Ladislav; Pánek, Radomír; Preinhaelter, Josef; Seidl, Jakub; Urban, Jakub

    Vol. 1580. Melville : American Institute of Physics, 2014, s. 442-445. ISBN 978-0-7354-1210-1. ISSN 0094-243X. - (AIP Publishing. 1580). [Topical conference on radio frequency power in plasmas/20./. Sorrento (IT), 25.06.2013-28.06.2013] Institutional support: RVO:61389021 Keywords : stellarator * lower hybrid * current drive * Hamiltonian * electron * stochastic Subject RIV: BL - Plasma and Gas Discharge Physics http://scitation.aip.org/content/aip/proceeding/aipcp/10.1063/1.4864583

  13. Optimal Advertising with Stochastic Demand

    George E. Monahan

    1983-01-01

    A stochastic, sequential model is developed to determine optimal advertising expenditures as a function of product maturity and past advertising. Random demand for the product depends upon an aggregate measure of current and past advertising called "goodwill," and the position of the product in its life cycle measured by sales-to-date. Conditions on the parameters of the model are established that insure that it is optimal to advertise less as the product matures. Additional characteristics o...

  14. Verification of Stochastic Process Calculi

    Skrypnyuk, Nataliya

    performed with the purpose to verify the system. In this dissertation it is argued that the verification techniques that have their origin in the analysis of programming code with the purpose to deduce the properties of the code's execution, i.e. Static Analysis techniques, are transferable to stochastic...... description of a system. The presented methods have a clear application in the areas of embedded systems, (randomised) protocols run between a fixed number of parties etc....

  15. Stochastic control of traffic patterns

    Gaididei, Yuri B.; Gorria, Carlos; Berkemer, Rainer; Christiansen, Peter L.; Kawamoto, Atsushi; Sørensen, Mads Peter; Starke, Jens

    2013-01-01

    A stochastic modulation of the safety distance can reduce traffic jams. It is found that the effect of random modulation on congestive flow formation depends on the spatial correlation of the noise. Jam creation is suppressed for highly correlated noise. The results demonstrate the advantage of heterogeneous performance of the drivers in time as well as individually. This opens the possibility for the construction of technical tools to control traffic jam formation.

  16. Normalized Convergence in Stochastic Optimization

    Y.M. Ermoliev; V.I. Norkin

    1989-01-01

    A new concept of (normalized) convergence of random variables is introduced. The normalized convergence in preserved under Lipschitz transformations. This convergence follows from the convergence in mean and itself implies the convergence in probability. If a sequence of random variables satisfies a Limit theorem then it in a normalized convergent sequence. The introduced concept is applied to the convergence rate study of a statistical approach in stochastic optimization.

  17. Stochastic approximation algorithms and applications

    Kushner, Harold J

    1997-01-01

    In recent years algorithms of the stochastic approximation type have found applications in new and diverse areas, and new techniques have been developed for proofs of convergence and rate of convergence. The actual and potential applications in signal processing have exploded. New challenges have arisen in applications to adaptive control. This book presents a thorough coverage of the ODE method used to analyze these algorithms.

  18. Stochastic processes and filtering theory

    Jazwinski, Andrew H

    2007-01-01

    This unified treatment of linear and nonlinear filtering theory presents material previously available only in journals, and in terms accessible to engineering students. Its sole prerequisites are advanced calculus, the theory of ordinary differential equations, and matrix analysis. Although theory is emphasized, the text discusses numerous practical applications as well.Taking the state-space approach to filtering, this text models dynamical systems by finite-dimensional Markov processes, outputs of stochastic difference, and differential equations. Starting with background material on probab

  19. Industry Dynamics with Stochastic Demand

    James Bergin; Dan Bernhardt

    2006-01-01

    We study the dynamics of an industry subject to aggregate demand shocks where the productivity of a firm's technology evolves stochastically over time. Each period, each firm, given the aggregate demand shock, the productivity of its technology, and the distribution of technology productivities in the economy, (i) chooses whether to remain in the industry or to exit to sell its resources to an entrant; and (ii) an active firm chooses how much capital and labor to employ, and hence output to p...

  20. Stochastic Gravity: Theory and Applications

    Hu Bei Lok

    2008-05-01

    Full Text Available Whereas semiclassical gravity is based on the semiclassical Einstein equation with sources given by the expectation value of the stress-energy tensor of quantum fields, stochastic semiclassical gravity is based on the Einstein–Langevin equation, which has, in addition, sources due to the noise kernel. The noise kernel is the vacuum expectation value of the (operator-valued stress-energy bitensor, which describes the fluctuations of quantum-matter fields in curved spacetimes. A new improved criterion for the validity of semiclassical gravity may also be formulated from the viewpoint of this theory. In the first part of this review we describe the fundamentals of this new theory via two approaches: the axiomatic and the functional. The axiomatic approach is useful to see the structure of the theory from the framework of semiclassical gravity, showing the link from the mean value of the stress-energy tensor to the correlation functions. The functional approach uses the Feynman–Vernon influence functional and the Schwinger–Keldysh closed-time-path effective action methods. In the second part, we describe three applications of stochastic gravity. First, we consider metric perturbations in a Minkowski spacetime, compute the two-point correlation functions of these perturbations and prove that Minkowski spacetime is a stable solution of semiclassical gravity. Second, we discuss structure formation from the stochastic-gravity viewpoint, which can go beyond the standard treatment by incorporating the full quantum effect of the inflaton fluctuations. Third, using the Einstein–Langevin equation, we discuss the backreaction of Hawking radiation and the behavior of metric fluctuations for both the quasi-equilibrium condition of a black-hole in a box and the fully nonequilibrium condition of an evaporating black hole spacetime. Finally, we briefly discuss the theoretical structure of stochastic gravity in relation to quantum gravity and point out

  1. Multiple fields in stochastic inflation

    Assadullahi, Hooshyar; Firouzjahi, Hassan; Noorbala, Mahdiyar; Vennin, Vincent; Wands, David

    2016-06-01

    Stochastic effects in multi-field inflationary scenarios are investigated. A hierarchy of diffusion equations is derived, the solutions of which yield moments of the numbers of inflationary e-folds. Solving the resulting partial differential equations in multi-dimensional field space is more challenging than the single-field case. A few tractable examples are discussed, which show that the number of fields is, in general, a critical parameter. When more than two fields are present for instance, the probability to explore arbitrarily large-field regions of the potential, otherwise inaccessible to single-field dynamics, becomes non-zero. In some configurations, this gives rise to an infinite mean number of e-folds, regardless of the initial conditions. Another difference with respect to single-field scenarios is that multi-field stochastic effects can be large even at sub-Planckian energy. This opens interesting new possibilities for probing quantum effects in inflationary dynamics, since the moments of the numbers of e-folds can be used to calculate the distribution of primordial density perturbations in the stochastic-δ N formalism.

  2. Mechanical Autonomous Stochastic Heat Engine

    Serra-Garcia, Marc; Foehr, André; Molerón, Miguel; Lydon, Joseph; Chong, Christopher; Daraio, Chiara

    2016-07-01

    Stochastic heat engines are devices that generate work from random thermal motion using a small number of highly fluctuating degrees of freedom. Proposals for such devices have existed for more than a century and include the Maxwell demon and the Feynman ratchet. Only recently have they been demonstrated experimentally, using, e.g., thermal cycles implemented in optical traps. However, recent experimental demonstrations of classical stochastic heat engines are nonautonomous, since they require an external control system that prescribes a heating and cooling cycle and consume more energy than they produce. We present a heat engine consisting of three coupled mechanical resonators (two ribbons and a cantilever) subject to a stochastic drive. The engine uses geometric nonlinearities in the resonating ribbons to autonomously convert a random excitation into a low-entropy, nonpassive oscillation of the cantilever. The engine presents the anomalous heat transport property of negative thermal conductivity, consisting in the ability to passively transfer energy from a cold reservoir to a hot reservoir.

  3. Turbulence, Spontaneous Stochasticity and Climate

    Eyink, Gregory

    Turbulence is well-recognized as important in the physics of climate. Turbulent mixing plays a crucial role in the global ocean circulation. Turbulence also provides a natural source of variability, which bedevils our ability to predict climate. I shall review here a recently discovered turbulence phenomenon, called ``spontaneous stochasticity'', which makes classical dynamical systems as intrinsically random as quantum mechanics. Turbulent dissipation and mixing of scalars (passive or active) is now understood to require Lagrangian spontaneous stochasticity, which can be expressed by an exact ``fluctuation-dissipation relation'' for scalar turbulence (joint work with Theo Drivas). Path-integral methods such as developed for quantum mechanics become necessary to the description. There can also be Eulerian spontaneous stochasticity of the flow fields themselves, which is intimately related to the work of Kraichnan and Leith on unpredictability of turbulent flows. This leads to problems similar to those encountered in quantum field theory. To quantify uncertainty in forecasts (or hindcasts), we can borrow from quantum field-theory the concept of ``effective actions'', which characterize climate averages by a variational principle and variances by functional derivatives. I discuss some work with Tom Haine (JHU) and Santha Akella (NASA-Goddard) to make this a practical predictive tool. More ambitious application of the effective action is possible using Rayleigh-Ritz schemes.

  4. Stochastic analysis of biochemical systems

    Anderson, David F

    2015-01-01

    This book focuses on counting processes and continuous-time Markov chains motivated by examples and applications drawn from chemical networks in systems biology.  The book should serve well as a supplement for courses in probability and stochastic processes.  While the material is presented in a manner most suitable for students who have studied stochastic processes up to and including martingales in continuous time, much of the necessary background material is summarized in the Appendix. Students and Researchers with a solid understanding of calculus, differential equations, and elementary probability and who are well-motivated by the applications will find this book of interest.    David F. Anderson is Associate Professor in the Department of Mathematics at the University of Wisconsin and Thomas G. Kurtz is Emeritus Professor in the Departments of Mathematics and Statistics at that university. Their research is focused on probability and stochastic processes with applications in biology and other ar...

  5. Stochastic derivative and heat type PDEs

    Udriste, Constantin; Tevy, Ionel

    2011-01-01

    In this paper we address again the problem of the connection between multitime Brownian sheet and heat type PDEs. The main results include: the volumetric character of the solutions of the forward (backward) diffusion-like PDEs; the forward mean value of a Brownian process as the solution of the forward heat PDE; the backward mean value of a Brownian process as the solution of the backward heat PDE; the multitime stochastic processes with volumetric dependence; the stochastic partial derivative of a stochastic process with respect to a multitime Wiener process; Hermite polynomials stochastic processes; union of Tzitzeica hypersurfaces (constant level sets of multitime stochastic processes with volumetric dependence). The original results permit to extend the complete integrability theory to multitime stochastic differential systems, using path independent curvilinear integrals and volumetric dependence.

  6. Trajectory averaging for stochastic approximation MCMC algorithms

    Liang, Faming

    2010-01-01

    The subject of stochastic approximation was founded by Robbins and Monro [Ann. Math. Statist. 22 (1951) 400--407]. After five decades of continual development, it has developed into an important area in systems control and optimization, and it has also served as a prototype for the development of adaptive algorithms for on-line estimation and control of stochastic systems. Recently, it has been used in statistics with Markov chain Monte Carlo for solving maximum likelihood estimation problems and for general simulation and optimizations. In this paper, we first show that the trajectory averaging estimator is asymptotically efficient for the stochastic approximation MCMC (SAMCMC) algorithm under mild conditions, and then apply this result to the stochastic approximation Monte Carlo algorithm [Liang, Liu and Carroll J. Amer. Statist. Assoc. 102 (2007) 305--320]. The application of the trajectory averaging estimator to other stochastic approximation MCMC algorithms, for example, a stochastic approximation MLE al...

  7. Brownian motion, martingales, and stochastic calculus

    Le Gall, Jean-François

    2016-01-01

    This book offers a rigorous and self-contained presentation of stochastic integration and stochastic calculus within the general framework of continuous semimartingales. The main tools of stochastic calculus, including Itô’s formula, the optional stopping theorem and Girsanov’s theorem, are treated in detail alongside many illustrative examples. The book also contains an introduction to Markov processes, with applications to solutions of stochastic differential equations and to connections between Brownian motion and partial differential equations. The theory of local times of semimartingales is discussed in the last chapter. Since its invention by Itô, stochastic calculus has proven to be one of the most important techniques of modern probability theory, and has been used in the most recent theoretical advances as well as in applications to other fields such as mathematical finance. Brownian Motion, Martingales, and Stochastic Calculus provides a strong theoretical background to the reader interested i...

  8. The Robustness of Stochastic Switching Networks

    Loh, Po-Ling; Zhou, Hongchao; Bruck, Jehoshua

    2009-01-01

    Many natural systems, including chemical and biological systems, can be modeled using stochastic switching circuits. These circuits consist of stochastic switches, called pswitches, which operate with a fixed probability of being open or closed. We study the effect caused by introducing an error of size ∈ to each pswitch in a stochastic circuit. We analyze two constructions – simple series-parallel and general series-parallel circuits – and prove that simple series-parallel circuits are robus...

  9. Stochastic vehicle routing: from theory to practice

    Weyland, Dennis; Gambardella, Luca Maria; Montemanni, Roberto

    2013-01-01

    In this thesis we discuss practical and theoretical aspects of various stochastic vehicle routing problems. These are combinatorial optimization problems related to the field of transportation and logistics in which input data is (partially) represented in a stochastic way. More in detail, we focus on two-stage stochastic vehicle routing problems and in particular on so-called a priori optimization problems. The results are divided into a theoretical part and a practical part. In fact, the ...

  10. Applications of Stochastic Ordering to Wireless Communications

    Tepedelenlioglu, Cihan; Rajan, Adithya; Zhang, Yuan

    2011-01-01

    Stochastic orders are binary relations defined on probability distributions which capture intuitive notions like being larger or being more variable. This paper introduces stochastic ordering of instantaneous SNRs of fading channels as a tool to compare the performance of communication systems over different channels. Stochastic orders unify existing performance metrics such as ergodic capacity, and metrics based on error rate functions for commonly used modulation schemes through their relat...

  11. Credit Derivative Pricing with Stochastic Volatility Models

    Carl Chiarella; Samuel Chege Maina; Christina Nikitopoulos-Sklibosios

    2011-01-01

    This paper proposes a framework for pricing credit derivatives within the defaultable Markovian HJM framework featuring unspanned stochastic volatility. Motivated by empirical evidence, hump-shaped level dependent stochastic volatility specifications are proposed, such that the model admits finite dimensional Markovian structures. The model also accommodates a correlation structure between the stochastic volatility, default-free interest rates and credit spreads. Default free and defaultable ...

  12. Stochastic Turing Patterns on a Network

    Asslani, Malbor; Di Patti, Francesca; Fanelli, Duccio

    2012-01-01

    The process of stochastic Turing instability on a network is discussed for a specific case study, the stochastic Brusselator model. The system is shown to spontaneously differentiate into activator-rich and activator-poor nodes, outside the region of parameters classically deputed to the deterministic Turing instability. This phenomenon, as revealed by direct stochastic simulations, is explained analytically, and eventually traced back to the finite size corrections stemming from the inherent...

  13. Stochastic Turing patterns on a network

    Asslani, Malbor; Di Patti, Francesca; Fanelli, Duccio

    2012-10-01

    The process of stochastic Turing instability on a scale-free network is discussed for a specific case study: the stochastic Brusselator model. The system is shown to spontaneously differentiate into activator-rich and activator-poor nodes outside the region of parameters classically deputed to the deterministic Turing instability. This phenomenon, as revealed by direct stochastic simulations, is explained analytically and eventually traced back to the finite-size corrections stemming from the inherent graininess of the scrutinized medium.

  14. Stochastic Fluid Dynamic Model and Dimensional Reduction

    Resseguier, Valentin; Mémin, Etienne; Chapron, Bertrand

    2015-01-01

    International audience This paper uses a new decomposition of the fluid velocity in terms of a large-scale continuous component with respect to time and a small-scale non continuous random component. Within this general framework, a stochas-tic representation of the Reynolds transport theorem and Navier-Stokes equations can be derived, based on physical conservation laws. This physically relevant stochas-tic model is applied in the context of the POD-Galerkin method. In both the stochastic...

  15. STOCHASTIC GRADIENT METHODS FOR UNCONSTRAINED OPTIMIZATION

    Nataša Krejić; Nataša Krklec Jerinkić

    2014-01-01

    This papers presents an overview of gradient based methods for minimization of noisy functions. It is assumed that the objective functions is either given with error terms of stochastic nature or given as the mathematical expectation. Such problems arise in the context of simulation based optimization. The focus of this presentation is on the gradient based Stochastic Approximation and Sample Average Approximation methods. The concept of stochastic gradient approximation of the true gradient ...

  16. Loss Aversion, Stochastic Compensation, and Team Incentives

    Kohei Daido; Takeshi Murooka

    2013-01-01

    We investigate moral-hazard problems with limited liability where agents have expectation-based reference-dependent preferences. We show that stochastic compensation for low performance can be optimal. Because of loss aversion, the agents have first-order risk aversion to wage uncertainty. This causes the agents to work harder when their low performance is stochastically compensated. We also examine team incentives for credibly employing such stochastic compensation. In an optimal contract, l...

  17. Optimal Control of Stochastic Partial Differential Equations

    Zhang, Liangquan

    2010-01-01

    In this paper, we prove the necessary and sufficient maximum principles (NSMP in short) for the optimal control of system described by a quasilinear stochastic heat equation with the control domain being convex and all the coefficients containing control variable. For that, the optimal control problem of fully coupled forward-backward doubly stochastic system is studied. We apply our NSMP to solve a kind of forward-backward doubly stochastic linear quadratic optimal control problem as well.

  18. Inverse problems for stochastic transport equations

    Inverse problems for stochastic linear transport equations driven by a temporal or spatial white noise are discussed. We analyse stochastic linear transport equations which depend on an unknown potential and have either additive noise or multiplicative noise. We show that one can approximate the potential with arbitrary small error when the solution of the stochastic linear transport equation is observed over time at some fixed point in the state space. (paper)

  19. Ambit processes and stochastic partial differential equations

    Barndorff-Nielsen, Ole; Benth, Fred Espen; Veraart, Almut

    Ambit processes are general stochastic processes based on stochastic integrals with respect to Lévy bases. Due to their flexible structure, they have great potential for providing realistic models for various applications such as in turbulence and finance. This papers studies the connection betwe...... ambit processes and solutions to stochastic partial differential equations. We investigate this relationship from two angles: from the Walsh theory of martingale measures and from the viewpoint of the Lévy noise analysis....

  20. Modelling Cow Behaviour Using Stochastic Automata

    Jónsson, Ragnar Ingi

    2010-01-01

    This report covers an initial study on the modelling of cow behaviour using stochastic automata with the aim of detecting lameness. Lameness in cows is a serious problem that needs to be dealt with because it results in less profitable production units and in reduced quality of life for the affected livestock. By featuring training data consisting of measurements of cow activity, three different models are obtained, namely an autonomous stochastic automaton, a stochastic automaton with coinci...

  1. Stochastic Descent Analysis of Representation Learning Algorithms

    Golden, Richard M.

    2014-01-01

    Although stochastic approximation learning methods have been widely used in the machine learning literature for over 50 years, formal theoretical analyses of specific machine learning algorithms are less common because stochastic approximation theorems typically possess assumptions which are difficult to communicate and verify. This paper presents a new stochastic approximation theorem for state-dependent noise with easily verifiable assumptions applicable to the analysis and design of import...

  2. Block Triangular Preconditioning for Stochastic Galerkin Method

    Zheng, Bin; Lin, Guang; Xu, Jinchao

    2013-01-01

    In this paper we study fast iterative solvers for the large sparse linear systems resulting from the stochastic Galerkin discretization of stochastic partial differential equations. A block triangular preconditioner is introduced and applied to the Krylov subspace methods, including the generalized minimum residual method and the generalized preconditioned conjugate gradient method. This preconditioner utilizes the special structures of the stochastic Galerkin matrices to achieve high efficie...

  3. Stochastic Schr\\"odinger equations and memory

    Barchielli, A; Pellegrini, C; Petruccione, F

    2010-01-01

    By starting from the stochastic Schr\\"odinger equation and quantum trajectory theory, we introduce memory effects by considering stochastic adapted coefficients. As an example of a natural non-Markovian extension of the theory of white noise quantum trajectories we use an Ornstein-Uhlenbeck coloured noise as the output driving process. Under certain conditions a random Hamiltonian evolution is recovered. Moreover, we show that our non-Markovian stochastic Schr\\"odinger equations unravel some master equations with memory kernels.

  4. Stochastic Optimization of Wind Turbine Power Factor Using Stochastic Model of Wind Power

    Chen, Peiyuan; Siano, Pierluigi; Bak-Jensen, Birgitte; Chen, Zhe

    2010-01-01

    This paper proposes a stochastic optimization algorithm that aims to minimize the expectation of the system power losses by controlling wind turbine (WT) power factors. This objective of the optimization is subject to the probability constraints of bus voltage and line current requirements. The optimization algorithm utilizes the stochastic models of wind power generation (WPG) and load demand to take into account their stochastic variation. The stochastic model of WPG is developed on the bas...

  5. Classification of Stochastic Runge-Kutta Methods for the Weak Approximation of Stochastic Differential Equations

    Debrabant, Kristian; Rößler, Andreas

    2013-01-01

    In the present paper, a class of stochastic Runge-Kutta methods containing the second order stochastic Runge-Kutta scheme due to E. Platen for the weak approximation of It\\^o stochastic differential equation systems with a multi-dimensional Wiener process is considered. Order one and order two conditions for the coefficients of explicit stochastic Runge-Kutta methods are solved and the solution space of the possible coefficients is analyzed. A full classification of the coefficients for such ...

  6. The stochastic functional characteristics of radioelectronic devices.

    B. M. Uvarov

    2011-03-01

    Full Text Available The models of physical processes in radio electronic devices (RED as the stochastic differential equations are considered. Is shown, that all physical processes in RED - stochastic, and functional characteristics of devices should be submitted by functions with stochastic by parameters. The stochastic differential equations for electromagnetic, electrical, mechanical, thermal processes are given and the methods of reception of their decisions are offered on the basis of the determined models. The examples of reception of the functional characteristics are given at mechanical and thermal influences.

  7. The stochastic functional characteristics of radioelectronic devices

    B. M. Uvarov

    2011-03-01

    Full Text Available The models of physical processes in radio electronic devices (RED as the stochastic differential equations are considered. Is shown, that all physical processes in RED - stochastic, and functional characteristics of devices should be submitted by functions with stochastic by parameters. The stochastic differential equations for electromagnetic, electrical, mechanical, thermal processes are given and the methods of reception of their decisions are offered on the basis of the determined models. The examples of reception of the functional characteristics are given at mechanical and thermal influences.

  8. Efficient numerical integrators for stochastic models

    De Fabritiis, G; Español, P; Coveney, P V

    2006-01-01

    The efficient simulation of models defined in terms of stochastic differential equations (SDEs) depends critically on an efficient integration scheme. In this article, we investigate under which conditions the integration schemes for general SDEs can be derived using the Trotter expansion. It follows that, in the stochastic case, some care is required in splitting the stochastic generator. We test the Trotter integrators on an energy-conserving Brownian model and derive a new numerical scheme for dissipative particle dynamics. We find that the stochastic Trotter scheme provides a mathematically correct and easy-to-use method which should find wide applicability.

  9. A minicourse on stochastic partial differential equations

    Rassoul-Agha, Firas

    2009-01-01

    In May 2006, The University of Utah hosted an NSF-funded minicourse on stochastic partial differential equations. The goal of this minicourse was to introduce graduate students and recent Ph.D.s to various modern topics in stochastic PDEs, and to bring together several experts whose research is centered on the interface between Gaussian analysis, stochastic analysis, and stochastic partial differential equations. This monograph contains an up-to-date compilation of many of those lectures. Particular emphasis is paid to showcasing central ideas and displaying some of the many deep connections between the mentioned disciplines, all the time keeping a realistic pace for the student of the subject.

  10. Stochastic versus deterministic systems of differential equations

    Ladde, G S

    2003-01-01

    This peerless reference/text unfurls a unified and systematic study of the two types of mathematical models of dynamic processes-stochastic and deterministic-as placed in the context of systems of stochastic differential equations. Using the tools of variational comparison, generalized variation of constants, and probability distribution as its methodological backbone, Stochastic Versus Deterministic Systems of Differential Equations addresses questions relating to the need for a stochastic mathematical model and the between-model contrast that arises in the absence of random disturbances/flu

  11. Pricing long-dated insurance contracts with stochastic interest rates and stochastic volatility

    A. van Haastrecht; R. Lord; A. Pelsser; D. Schrager

    2009-01-01

    We consider the pricing of long-dated insurance contracts under stochastic interest rates and stochastic volatility. In particular, we focus on the valuation of insurance options with long-term equity or foreign exchange exposures. Our modeling framework extends the stochastic volatility model of Sc

  12. Probability, Statistics, and Stochastic Processes

    Olofsson, Peter

    2012-01-01

    This book provides a unique and balanced approach to probability, statistics, and stochastic processes.   Readers gain a solid foundation in all three fields that serves as a stepping stone to more advanced investigations into each area.  The Second Edition features new coverage of analysis of variance (ANOVA), consistency and efficiency of estimators, asymptotic theory for maximum likelihood estimators, empirical distribution function and the Kolmogorov-Smirnov test, general linear models, multiple comparisons, Markov chain Monte Carlo (MCMC), Brownian motion, martingales, and

  13. Stochastic modeling analysis and simulation

    Nelson, Barry L

    1995-01-01

    A coherent introduction to the techniques for modeling dynamic stochastic systems, this volume also offers a guide to the mathematical, numerical, and simulation tools of systems analysis. Suitable for advanced undergraduates and graduate-level industrial engineers and management science majors, it proposes modeling systems in terms of their simulation, regardless of whether simulation is employed for analysis. Beginning with a view of the conditions that permit a mathematical-numerical analysis, the text explores Poisson and renewal processes, Markov chains in discrete and continuous time, se

  14. Stochastic resonance in nuclear fission

    Fission decay of highly excited periodically driven compound nuclei is considered in the framework of Langevin approach. We used residual-time distribution (RTD) as a tool for studying the dynamic features in the presence of periodic perturbation. The structure of RTD essentially depends on the relation between Kramers decay rate and the frequency ω of periodic perturbation. In particular, the intensity of the first peak in RTD has a sharp maximum at certain nuclear temperature depending on ω. This maximum should be considered as fist-hand manifestation of stochastic resonance in nuclear dynamics

  15. Coexistence in stochastic spatial models

    Durrett, Rick

    2009-01-01

    In this paper I will review twenty years of work on the question: When is there coexistence in stochastic spatial models? The answer, announced in Durrett and Levin [Theor. Pop. Biol. 46 (1994) 363--394], and that we explain in this paper is that this can be determined by examining the mean-field ODE. There are a number of rigorous results in support of this picture, but we will state nine challenging and important open problems, most of which date from the 1990's.

  16. Stochastic Gravity: Theory and Applications

    Hu Bei Lok

    2004-01-01

    Full Text Available Whereas semiclassical gravity is based on the semiclassical Einstein equation with sources given by the expectation value of the stress-energy tensor of quantum fields, stochastic semiclassical gravity is based on the Einstein-Langevin equation, which has in addition sources due to the noise kernel. The noise kernel is the vacuum expectation value of the (operator-valued stress-energy bi-tensor which describes the fluctuations of quantum matter fields in curved spacetimes. In the first part, we describe the fundamentals of this new theory via two approaches: the axiomatic and the functional. The axiomatic approach is useful to see the structure of the theory from the framework of semiclassical gravity, showing the link from the mean value of the stress-energy tensor to their correlation functions. The functional approach uses the Feynman-Vernon influence functional and the Schwinger-Keldysh closed-time-path effective action methods which are convenient for computations. It also brings out the open systems concepts and the statistical and stochastic contents of the theory such as dissipation, fluctuations, noise, and decoherence. We then focus on the properties of the stress-energy bi-tensor. We obtain a general expression for the noise kernel of a quantum field defined at two distinct points in an arbitrary curved spacetime as products of covariant derivatives of the quantum field's Green function. In the second part, we describe three applications of stochastic gravity theory. First, we consider metric perturbations in a Minkowski spacetime. We offer an analytical solution of the Einstein-Langevin equation and compute the two-point correlation functions for the linearized Einstein tensor and for the metric perturbations. Second, we discuss structure formation from the stochastic gravity viewpoint, which can go beyond the standard treatment by incorporating the full quantum effect of the inflaton fluctuations. Third, we discuss the backreaction

  17. Stochastic Still Water Response Model

    Friis-Hansen, Peter; Ditlevsen, Ove Dalager

    2002-01-01

    obtaining the stochastic cargo container load field is based on a queuing and loading policy that assumes containers are handled by a first-come-first-serve policy. The load field is assumed to be Gaussian. The ballast system is imposed to counteract the angle of heel and to regulate both the draft and the...... trim caused by the possible uneven distribution of the cargo load and the bunker load over the system. Stability is not explicitly accounted for. Finally the calculated second moment statistics of the sectional forces in a container vessel in a full load condition are reported. The obtained statistics...

  18. Stochastic quantization for complex actions

    We use the stochastic quantization method to study systems with complex valued path integral weights. We assume a Langevin equation with a memory kernel and Einstein's relations with colored noise. The equilibrium solution of this non-Markovian Langevin equation is analyzed. We show that for a large class of elliptic non-Hermitian operators acting on scalar functions on Euclidean space, which define different models in quantum field theory, converges to an equilibrium state in the asymptotic limit of the Markov parameter τ → ∞. Moreover, as we expected, we obtain the Schwinger functions of the theory. (author)

  19. Stochastic solution to quantum dynamics

    John, Sarah; Wilson, John W.

    1994-01-01

    The quantum Liouville equation in the Wigner representation is solved numerically by using Monte Carlo methods. For incremental time steps, the propagation is implemented as a classical evolution in phase space modified by a quantum correction. The correction, which is a momentum jump function, is simulated in the quasi-classical approximation via a stochastic process. The technique, which is developed and validated in two- and three- dimensional momentum space, extends an earlier one-dimensional work. Also, by developing a new algorithm, the application to bound state motion in an anharmonic quartic potential shows better agreement with exact solutions in two-dimensional phase space.

  20. Stochastic Calculus of Wrapped Compartments

    Coppo, Mario; Drocco, Maurizio; Grassi, Elena; Troina, Angelo; 10.4204/EPTCS.28.6

    2010-01-01

    The Calculus of Wrapped Compartments (CWC) is a variant of the Calculus of Looping Sequences (CLS). While keeping the same expressiveness, CWC strongly simplifies the development of automatic tools for the analysis of biological systems. The main simplification consists in the removal of the sequencing operator, thus lightening the formal treatment of the patterns to be matched in a term (whose complexity in CLS is strongly affected by the variables matching in the sequences). We define a stochastic semantics for this new calculus. As an application we model the interaction between macrophages and apoptotic neutrophils and a mechanism of gene regulation in E.Coli.

  1. Multiple Fields in Stochastic Inflation

    Assadullahi, Hooshyar; Noorbala, Mahdiyar; Vennin, Vincent; Wands, David

    2016-01-01

    Stochastic effects in multi-field inflationary scenarios are investigated. A hierarchy of diffusion equations is derived, the solutions of which yield moments of the numbers of inflationary $e$-folds. Solving the resulting partial differential equations in multi-dimensional field space is more challenging than the single-field case. A few tractable examples are discussed, which show that the number of fields is, in general, a critical parameter. When more than two fields are present for instance, the probability to explore arbitrarily large-field regions of the potential, otherwise inaccessible to single-field dynamics, becomes non-zero. In some configurations, this gives rise to an infinite mean number of $e$-folds, regardless of the initial conditions. Another difference with respect to single-field scenarios is that multi-field stochastic effects can be large even at sub-Planckian energy. This opens interesting new possibilities for probing quantum effects in inflationary dynamics, since the moments of the...

  2. Stochastic background of gravitational waves

    D'Araújo, J C N; Aguiar, O D

    2000-01-01

    A continuous stochastic background of gravitational waves (GWs) for burst sources is produced if the mean time interval between the occurrence of bursts is smaller than the average time duration of a single burst at the emission, i.e., the so called duty cycle must be greater than one. To evaluate the background of GWs produced by an ensemble of sources, during their formation, for example, one needs to know the average energy flux emitted during the formation of a single object and the formation rate of such objects as well. In many cases the energy flux emitted during an event of production of GWs is not known in detail, only characteristic values for the dimensionless amplitude and frequencies are known. Here we present a shortcut to calculate stochastic backgrounds of GWs produced from cosmological sources. For this approach it is not necessary to know in detail the energy flux emitted at each frequency. Knowing the characteristic values for the ``lumped'' dimensionless amplitude and frequency we show tha...

  3. Single-molecule stochastic resonance

    Hayashi, K; Manosas, M; Huguet, J M; Ritort, F; 10.1103/PhysRevX.2.031012

    2012-01-01

    Stochastic resonance (SR) is a well known phenomenon in dynamical systems. It consists of the amplification and optimization of the response of a system assisted by stochastic noise. Here we carry out the first experimental study of SR in single DNA hairpins which exhibit cooperatively folding/unfolding transitions under the action of an applied oscillating mechanical force with optical tweezers. By varying the frequency of the force oscillation, we investigated the folding/unfolding kinetics of DNA hairpins in a periodically driven bistable free-energy potential. We measured several SR quantifiers under varied conditions of the experimental setup such as trap stiffness and length of the molecular handles used for single-molecule manipulation. We find that the signal-to-noise ratio (SNR) of the spectral density of measured fluctuations in molecular extension of the DNA hairpins is a good quantifier of the SR. The frequency dependence of the SNR exhibits a peak at a frequency value given by the resonance match...

  4. Stochastic Time-Series Spectroscopy

    Scoville, John

    2015-01-01

    Spectroscopically measuring low levels of non-equilibrium phenomena (e.g. emission in the presence of a large thermal background) can be problematic due to an unfavorable signal-to-noise ratio. An approach is presented to use time-series spectroscopy to separate non-equilibrium quantities from slowly varying equilibria. A stochastic process associated with the non-equilibrium part of the spectrum is characterized in terms of its central moments or cumulants, which may vary over time. This parameterization encodes information about the non-equilibrium behavior of the system. Stochastic time-series spectroscopy (STSS) can be implemented at very little expense in many settings since a series of scans are typically recorded in order to generate a low-noise averaged spectrum. Higher moments or cumulants may be readily calculated from this series, enabling the observation of quantities that would be difficult or impossible to determine from an average spectrum or from prinicipal components analysis (PCA). This meth...

  5. Stochastic geometry in PRIZMA code

    The paper describes a method used to simulate radiation transport through random media - randomly placed grains in a matrix material. The method models the medium consequently from one grain crossed by particle trajectory to another. Like in the Limited Chord Length Sampling (LCLS) method, particles in grains are tracked in the actual grain geometry, but unlike LCLS, the medium is modeled using only Matrix Chord Length Sampling (MCLS) from the exponential distribution and it is not necessary to know the grain chord length distribution. This helped us extend the method to media with randomly oriented arbitrarily shaped convex grains. Other extensions include multicomponent media - grains of several sorts, and polydisperse media - grains of different sizes. Sort and size distributions of crossed grains were obtained and an algorithm was developed for sampling grain orientations and positions. Special consideration was given to medium modeling at the boundary of the stochastic region. The method was implemented in the universal 3D Monte Carlo code PRIZMA. The paper provides calculated results for a model problem where we determine volume fractions of modeled components crossed by particle trajectories. It also demonstrates the use of biased sampling techniques implemented in PRIZMA for solving a problem of deep penetration in model random media. Described are calculations for the spectral response of a capacitor dose detector whose anode was modeled with account for its stochastic structure. (authors)

  6. Stochastic Methods for Aircraft Design

    Pelz, Richard B.; Ogot, Madara

    1998-01-01

    The global stochastic optimization method, simulated annealing (SA), was adapted and applied to various problems in aircraft design. The research was aimed at overcoming the problem of finding an optimal design in a space with multiple minima and roughness ubiquitous to numerically generated nonlinear objective functions. SA was modified to reduce the number of objective function evaluations for an optimal design, historically the main criticism of stochastic methods. SA was applied to many CFD/MDO problems including: low sonic-boom bodies, minimum drag on supersonic fore-bodies, minimum drag on supersonic aeroelastic fore-bodies, minimum drag on HSCT aeroelastic wings, FLOPS preliminary design code, another preliminary aircraft design study with vortex lattice aerodynamics, HSR complete aircraft aerodynamics. In every case, SA provided a simple, robust and reliable optimization method which found optimal designs in order 100 objective function evaluations. Perhaps most importantly, from this academic/industrial project, technology has been successfully transferred; this method is the method of choice for optimization problems at Northrop Grumman.

  7. Optimizing Resource Acquisition Decisions by Stochastic Programming

    Daniel Bienstock; Jeremy F. Shapiro

    1988-01-01

    This paper reports on the application of stochastic programming with recourse to strategic planning decisions regarding resource acquisition. A resource directed decomposition method, which simultaneously exploits stochastic programming and mixed integer programming model structures, is proposed. Computational experience with the method applied to fuel contract and plant construction decisions faced by an electric utility is presented.

  8. Option valuation models with stochastic volatility

    Šigut, Jiří

    2012-01-01

    This work describes stochastic volatility models and application of such models for option pricing. Models for underlying asset and then pricing models for options with stochastic volatility are derived. Black-Scholes and Heston-Nandi models are compared in empirical part of this work.

  9. From Complex to Simple: Interdisciplinary Stochastic Models

    Mazilu, D. A.; Zamora, G.; Mazilu, I.

    2012-01-01

    We present two simple, one-dimensional, stochastic models that lead to a qualitative understanding of very complex systems from biology, nanoscience and social sciences. The first model explains the complicated dynamics of microtubules, stochastic cellular highways. Using the theory of random walks in one dimension, we find analytical expressions…

  10. Brownian motion an introduction to stochastic processes

    Schilling, René L; Böttcher, Björn

    2014-01-01

    Stochastic processes occur everywhere in sciences and engineering, and need to be understood by applied mathematicians, engineers and scientists alike. This is a first course introducing the reader gently to the subject. Brownian motions are a stochastic process, central to many applications and easy to treat.

  11. Stochastic Dynamics for the Matching Pennies Game

    Ziv Gorodeisky

    2006-01-01

    We consider stochastic dynamics for the Matching Pennies game, that behave, in expectation, like best-response dynamics (the continuous fictitious play). We prove convergence to the unique equilibrium by extending the result of Benaim and Weibull [2003] on deterministic approximations for stochastic dynamics to the case of discontinuous dynamics - such as the best-reply dynamics.

  12. Bisimulation for general stochastic hybrid systems

    Bujorianu, Manuela L.; Lygeros, John; Bujorianu, Marius C.

    2005-01-01

    In this paper we define a bisimulation concept for some very general models for stochastic hybrid systems (general stochastic hybrid systems). The definition of bisimulation builds on the ideas of Edalat and of Larsen and Skou and of Joyal, Nielsen and Winskel. The main result is that this bisimulat

  13. Stochastic properties of the Friedman dynamical system

    Some mathematical aspects of the stochastic cosmology are discussed in the corresponding ordinary Friedman world models. In particulare, it is shown that if the strong and Lorentz energy conditions are known, or the potential function is given, or a stochastic measure is suitably defined then the structure of the phase plane of the Friedman dynamical system is determined. 11 refs., 2 figs. (author)

  14. On the stochastic stability of MHD equilibria

    The stochastic stability in the large of stationary equilibria of ideal and dissipative magnetohydrodynamics under the influence of stationary random fluctuations is studied using the direct Liapunov method. Sufficient and necessary conditions for stability of the linearized Euler-Lagrangian systems are given. The destabilizing effect of stochastic fluctuations is demonstrated. (orig.)

  15. Stochastic Kinetics of Intracellular Calcium Oscillations

    陈昌胜; 曾仁端

    2003-01-01

    A stochastic model of intracellular calcium oscillations is put forward by taking into account the random opening-closing of Ca2+ channels in endoplasmic reticulum (ER) membrane. The numerical results of the stochastic model show simple and complex calcium oscillations, which accord with the experiment results.

  16. Integration of stochastic generation in power systems

    Papaefthymiou, G.

    2007-01-01

    Stochastic Generation is the electrical power production by the use of an uncontrollable prime energy mover, corresponding mainly to renewable energy sources. For the large-scale integration of stochastic generation in power systems, methods are necessary for the modeling of power generation uncerta

  17. Exact Algorithms for Solving Stochastic Games

    Hansen, A K; Koucký, M.; Lauritzen, N.; Miltersen, P.B.; Tsigaridas, E.P.

    2012-01-01

    Shapley's discounted stochastic games, Everett's recursive games and Gillette's undiscounted stochastic games are classical models of game theory describing two-player zero-sum games of potentially infinite duration. We describe algorithms for exactly solving these games. When the number of positions of the game is constant, our algorithms run in polynomial time.

  18. Variational principles for a relativistic stochastic mechanics

    An extension to the relativistic case of the stochastic variational principles both of Lagrangian and Eulerian type is proposed. The action used is the mean classical action evaluated on the paths of relativistic covariant diffusions. The resulting equations of motion are the relativistic stochastic Lagrange equations

  19. Are limit cycle calculations a stochastic process?

    Stochasticity is typically associated with processes that produce uncertain results which, in many cases, are due to process nonlinearities and/or extreme sensitivity to initial conditions. By its name, a stochastic process should have a probabilistic or random nature; however, it is well known that many if not all, of the processes that behave stochasticly are indeed deterministic. This is the case with computer calculations to predict the stability of boiling water reactors (BWRs). This paper attempts to introduce the reader to some of the ''stochastic'' uncertainties involved in this topic, and in particular the errors introduced by the approximations used to integrate numerically the solutions in the time domain. The knowledge of this type of errors is relevant not only in BWR stability calculations but also in time domain calculations involving nonlinear or stochastic processes

  20. Modelling and application of stochastic processes

    1986-01-01

    The subject of modelling and application of stochastic processes is too vast to be exhausted in a single volume. In this book, attention is focused on a small subset of this vast subject. The primary emphasis is on realization and approximation of stochastic systems. Recently there has been considerable interest in the stochastic realization problem, and hence, an attempt has been made here to collect in one place some of the more recent approaches and algorithms for solving the stochastic realiza­ tion problem. Various different approaches for realizing linear minimum-phase systems, linear nonminimum-phase systems, and bilinear systems are presented. These approaches range from time-domain methods to spectral-domain methods. An overview of the chapter contents briefly describes these approaches. Also, in most of these chapters special attention is given to the problem of developing numerically ef­ ficient algorithms for obtaining reduced-order (approximate) stochastic realizations. On the application side,...

  1. Turbulent response in a stochastic regime

    The theory for the non-linear, turbulent response in a system with intrinsic stochasticity is considered. It is argued that perturbative Eulerian theories, such as the Direct Interaction Approximation (DIA), are inherently unsuited to describe such a system. The exponentiation property that characterizes stochasticity appears in the Lagrangian picture and cannot even be defined in the Eulerian representation. An approximation for stochastic systems - the Normal Stochastic Approximation - is developed and states that the perturbed orbit functions (Lagrangian fluctuations) behave as normally distributed random variables. This is independent of the Eulerian statistics and, in fact, we treat the Eulerian fluctuations as fixed. A simple model problem (appropriate for the electron response in the drift wave) is subjected to a series of computer experiments. To within numerical noise the results are in agreement with the Normal Stochastic Approximation. The predictions of the DIA for this mode show substantial qualitative and quantitative departures from the observations

  2. Time series modeling with pruned multi-layer perceptron and 2-stage damped least-squares method

    A Multi-Layer Perceptron (MLP) defines a family of artificial neural networks often used in TS modeling and forecasting. Because of its ''black box'' aspect, many researchers refuse to use it. Moreover, the optimization (often based on the exhaustive approach where ''all'' configurations are tested) and learning phases of this artificial intelligence tool (often based on the Levenberg-Marquardt algorithm; LMA) are weaknesses of this approach (exhaustively and local minima). These two tasks must be repeated depending on the knowledge of each new problem studied, making the process, long, laborious and not systematically robust. In this paper a pruning process is proposed. This method allows, during the training phase, to carry out an inputs selecting method activating (or not) inter-nodes connections in order to verify if forecasting is improved. We propose to use iteratively the popular damped least-squares method to activate inputs and neurons. A first pass is applied to 10% of the learning sample to determine weights significantly different from 0 and delete other. Then a classical batch process based on LMA is used with the new MLP. The validation is done using 25 measured meteorological TS and cross-comparing the prediction results of the classical LMA and the 2-stage LMA

  3. Stochastic inflation and nonlinear gravity

    Salopek, D. S.; Bond, J. R.

    1991-02-01

    We show how nonlinear effects of the metric and scalar fields may be included in stochastic inflation. Our formalism can be applied to non-Gaussian fluctuation models for galaxy formation. Fluctuations with wavelengths larger than the horizon length are governed by a network of Langevin equations for the physical fields. Stochastic noise terms arise from quantum fluctuations that are assumed to become classical at horizon crossing and that then contribute to the background. Using Hamilton-Jacobi methods, we solve the Arnowitt-Deser-Misner constraint equations which allows us to separate the growing modes from the decaying ones in the drift phase following each stochastic impulse. We argue that the most reasonable choice of time hypersurfaces for the Langevin system during inflation is T=ln(Ha), where H and a are the local values of the Hubble parameter and the scale factor, since T is the natural time for evolving the short-wavelength scalar field fluctuations in an inhomogeneous background. We derive a Fokker-Planck equation which describes how the probability distribution of scalar field values at a given spatial point evolves in T. Analytic Green's-function solutions obtained for a single scalar field self-interacting through an exponential potential are used to demonstrate (1) if the initial condition of the Hubble parameter is chosen to be consistent with microwave-background limits, H(φ0)/mρUniverse, the distribution is non-Gaussian, with a tail extending to large energy densities; although there are no observable manifestations, it does show eternal inflation. Lattice simulations of our Langevin network for the exponential potential demonstrate how spatial correlations are incorporated. An initially homogeneous and isotropic lattice develops fluctuations as more and more quantum fluctuation modes leave the horizon, yielding Gaussian contour maps for a region corresponding to our observable patch and non-Gaussian contour maps for the ultra

  4. Cancer Basics

    ... Cancer? Breast Cancer Colon/Rectum Cancer Lung Cancer Prostate Cancer Skin Cancer Show All Cancer Types News and Features Cancer Glossary ACS Bookstore Cancer Information Cancer Basics Cancer Prevention & Detection Signs & Symptoms of Cancer Treatments & Side Effects ...

  5. Stochastic differential equations: singularity of coefficients, regression models, and stochastic approximation

    Mel'nikov, A. V.

    1996-10-01

    Contents Introduction Chapter I. Basic notions and results from contemporary martingale theory §1.1. General notions of the martingale theory §1.2. Convergence (a.s.) of semimartingales. The strong law of large numbers and the law of the iterated logarithm Chapter II. Stochastic differential equations driven by semimartingales §2.1. Basic notions and results of the theory of stochastic differential equations driven by semimartingales §2.2. The method of monotone approximations. Existence of strong solutions of stochastic equations with non-smooth coefficients §2.3. Linear stochastic equations. Properties of stochastic exponentials §2.4. Linear stochastic equations. Applications to models of the financial market Chapter III. Procedures of stochastic approximation as solutions of stochastic differential equations driven by semimartingales §3.1. Formulation of the problem. A general model and its relation to the classical one §3.2. A general description of the approach to the procedures of stochastic approximation. Convergence (a.s.) and asymptotic normality §3.3. The Gaussian model of stochastic approximation. Averaged procedures and their effectiveness Chapter IV. Statistical estimation in regression models with martingale noises §4.1. The formulation of the problem and classical regression models §4.2. Asymptotic properties of MLS-estimators. Strong consistency, asymptotic normality, the law of the iterated logarithm §4.3. Regression models with deterministic regressors §4.4. Sequential MLS-estimators with guaranteed accuracy and sequential statistical inferences Bibliography

  6. Classical and stochastic Laplacian growth

    Gustafsson, Björn; Vasil’ev, Alexander

    2014-01-01

    This monograph covers a multitude of concepts, results, and research topics originating from a classical moving-boundary problem in two dimensions (idealized Hele-Shaw flows, or classical Laplacian growth), which has strong connections to many exciting modern developments in mathematics and theoretical physics. Of particular interest are the relations between Laplacian growth and the infinite-size limit of ensembles of random matrices with complex eigenvalues; integrable hierarchies of differential equations and their spectral curves; classical and stochastic Löwner evolution and critical phenomena in two-dimensional statistical models; weak solutions of hyperbolic partial differential equations of singular-perturbation type; and resolution of singularities for compact Riemann surfaces with anti-holomorphic involution. The book also provides an abundance of exact classical solutions, many explicit examples of dynamics by conformal mapping as well as a solid foundation of potential theory. An extensive biblio...

  7. Stochastic sensing through covalent interactions

    Bayley, Hagan; Shin, Seong-Ho; Luchian, Tudor; Cheley, Stephen

    2013-03-26

    A system and method for stochastic sensing in which the analyte covalently bonds to the sensor element or an adaptor element. If such bonding is irreversible, the bond may be broken by a chemical reagent. The sensor element may be a protein, such as the engineered P.sub.SH type or .alpha.HL protein pore. The analyte may be any reactive analyte, including chemical weapons, environmental toxins and pharmaceuticals. The analyte covalently bonds to the sensor element to produce a detectable signal. Possible signals include change in electrical current, change in force, and change in fluorescence. Detection of the signal allows identification of the analyte and determination of its concentration in a sample solution. Multiple analytes present in the same solution may be detected.

  8. Efficient Discretization of Stochastic Integrals

    Fukasawa, Masaaki

    2012-01-01

    Sharp asymptotic lower bounds of the expected quadratic variation of discretization error in stochastic integration are given. The theory relies on inequalities for the kurtosis and skewness of a general random variable which are themselves seemingly new. Asymptotically efficient schemes which attain the lower bounds are constructed explicitly. The result is directly applicable to practical hedging problem in mathematical finance; it gives an asymptotically optimal way to choose rebalancing dates and portofolios with respect to transaction costs. The asymptotically efficient strategies in fact reflect the structure of transaction costs. In particular a specific biased rebalancing scheme is shown to be superior to unbiased schemes if transaction costs follow a convex model. The problem is discussed also in terms of the exponential utility maximization.

  9. Extracting work from stochastic pumps

    The efficiency at maximum power output of stochastic pumps, systems driven away from equilibrium by a periodic variation of parameters, is investigated by studying simple models in which the driving consists of abrupt jumps of system parameters. The models are chosen to differ by qualitative aspects of the dynamics, namely the degree of directionality and cycle control. The behaviour at maximum power output is found to depend on the choice of parameters used to maximize the output. For a natural choice, in which the external force and the period of the driving are varied to increase the power output, numerical and analytical results suggest that the efficiency at maximum power output is at most 1/2

  10. Multiscale Stochastic Simulation and Modeling

    James Glimm; Xiaolin Li

    2006-01-10

    Acceleration driven instabilities of fluid mixing layers include the classical cases of Rayleigh-Taylor instability, driven by a steady acceleration and Richtmyer-Meshkov instability, driven by an impulsive acceleration. Our program starts with high resolution methods of numerical simulation of two (or more) distinct fluids, continues with analytic analysis of these solutions, and the derivation of averaged equations. A striking achievement has been the systematic agreement we obtained between simulation and experiment by using a high resolution numerical method and improved physical modeling, with surface tension. Our study is accompanies by analysis using stochastic modeling and averaged equations for the multiphase problem. We have quantified the error and uncertainty using statistical modeling methods.

  11. Thermodynamics of stochastic Turing machines

    Strasberg, Philipp; Cerrillo, Javier; Schaller, Gernot; Brandes, Tobias

    2015-10-01

    In analogy to Brownian computers we explicitly show how to construct stochastic models which mimic the behavior of a general-purpose computer (a Turing machine). Our models are discrete state systems obeying a Markovian master equation, which are logically reversible and have a well-defined and consistent thermodynamic interpretation. The resulting master equation, which describes a simple one-step process on an enormously large state space, allows us to thoroughly investigate the thermodynamics of computation for this situation. Especially in the stationary regime we can well approximate the master equation by a simple Fokker-Planck equation in one dimension. We then show that the entropy production rate at steady state can be made arbitrarily small, but the total (integrated) entropy production is finite and grows logarithmically with the number of computational steps.

  12. Stochastic Approximation with Averaging Innovation

    Laruelle, Sophie

    2010-01-01

    The aim of the paper is to establish a convergence theorem for multi-dimensional stochastic approximation in a setting with innovations satisfying some averaging properties and to study some applications. The averaging assumptions allow us to unify the framework where the innovations are generated (to solve problems from Numerical Probability) and the one with exogenous innovations (market data, output of "device" $e.g.$ an Euler scheme) with stationary or ergodic properties. We propose several fields of applications with random innovations or quasi-random numbers. In particular we provide in both setting a rule to tune the step of the algorithm. At last we illustrate our results on five examples notably in Finance.

  13. Asymptotic Phase for Stochastic Oscillators

    Thomas, Peter J.; Lindner, Benjamin

    2014-12-01

    Oscillations and noise are ubiquitous in physical and biological systems. When oscillations arise from a deterministic limit cycle, entrainment and synchronization may be analyzed in terms of the asymptotic phase function. In the presence of noise, the asymptotic phase is no longer well defined. We introduce a new definition of asymptotic phase in terms of the slowest decaying modes of the Kolmogorov backward operator. Our stochastic asymptotic phase is well defined for noisy oscillators, even when the oscillations are noise dependent. It reduces to the classical asymptotic phase in the limit of vanishing noise. The phase can be obtained either by solving an eigenvalue problem, or by empirical observation of an oscillating density's approach to its steady state.

  14. Stochastic Quantization and Casimir Forces

    Rodriguez-Lopez, Pablo; Soto, Rodrigo

    2011-01-01

    In this paper we show how the stochastic quantization method developed by Parisi and Wu can be used to obtain Casimir forces. Both quantum and thermal fluctuations are taken into account by a Langevin equation for the field. The method allows the Casimir force to be obtained directly, derived from the stress tensor instead of the free energy. It only requires the spectral decomposition of the Laplacian operator in the given geometry. The formalism provides also an expression for the fluctuations of the force. As an application we compute the Casimir force on the plates of a finite piston of arbitrary cross section. Fluctuations of the force are also directly obtained, and it is shown that, in the piston case, the variance of the force is twice the force squared.

  15. Thermodynamics of stochastic Turing machines.

    Strasberg, Philipp; Cerrillo, Javier; Schaller, Gernot; Brandes, Tobias

    2015-10-01

    In analogy to Brownian computers we explicitly show how to construct stochastic models which mimic the behavior of a general-purpose computer (a Turing machine). Our models are discrete state systems obeying a Markovian master equation, which are logically reversible and have a well-defined and consistent thermodynamic interpretation. The resulting master equation, which describes a simple one-step process on an enormously large state space, allows us to thoroughly investigate the thermodynamics of computation for this situation. Especially in the stationary regime we can well approximate the master equation by a simple Fokker-Planck equation in one dimension. We then show that the entropy production rate at steady state can be made arbitrarily small, but the total (integrated) entropy production is finite and grows logarithmically with the number of computational steps. PMID:26565165

  16. Stochastic resonance for exploration geophysics

    Omerbashich, Mensur

    2008-01-01

    Stochastic resonance (SR) is a phenomenon in which signal to noise (SN) ratio gets improved by noise addition rather than removal as envisaged classically. SR was first claimed in climatology a few decades ago and then in other disciplines as well. The same as it is observed in natural systems, SR is used also for allowable SN enhancements at will. Here I report a proof of principle that SR can be useful in exploration geophysics. For this I perform high frequency GaussVanicek variance spectral analyses (GVSA) of model traces characterized by varying levels of complexity, completeness and pollution. This demonstration justifies all further research on SR in applied geophysics, as energy demands and depletion of reachable supplies potentially make SR vital in a near future.

  17. Stochastic cooling of particle beams

    Moehl, Dieter [European Organization for Nuclear Research (CERN), Geneva (Switzerland)

    2013-02-01

    First topical monograph on this subject matter. Provides conceptual and theoretical introduction. Introduces modern cooling schemes. This lecture note describes the main analytical approaches to stochastic cooling. The first is the time-domain picture, in which the beam is rapidly sampled at a high rate and a statistical analysis is used to describe the cooling behaviour. The second is the frequency-domain picture, which is particularly useful since the observations made on the beam and the numerical cooling simulations are mainly in this domain. This second picture is developed in detail to assess key components of modern cooling theory like mixing and signal shielding and to illustrate some of the diagnostic methods. Finally the use of a distribution function and the Fokker-Plank equation, which offer the most complete description of the beam during the cooling, are discussed.

  18. Stochastic cooling of particle beams

    Möhl, Dieter

    2013-01-01

    This lecture note describes the main analytical approaches to stochastic cooling. The first is the time-domain picture, in which the beam is rapidly sampled at a high rate and a statistical analysis is used to describe the cooling behaviour. The second is the frequency-domain picture, which is particularly useful since the observations made on the beam and the numerical cooling simulations are mainly in this domain. This second picture is developed in detail to assess key components of modern cooling theory like mixing and signal shielding and to illustrate some of the diagnostic methods. Finally the use of a distribution function and the Fokker-Plank equation, which offer the most complete description of the beam during the cooling, are discussed.

  19. Stochastic models of technology diffusion

    Horner, S.M.

    1978-01-01

    Simple stochastic models of epidemics have often been employed by economists and sociologists in the study of the diffusion of information or new technology. In the present theoretical inquiry the properties of a family of models related to these epidemic processes are investigated, and use of the results in the study of technical change phenomena is demonstrated. A moving limit to the level of productivity of capital is hypothesized, the exact increment is determined exogenously by basic or applied research carried on outside the industry. It is this level of latent productivity (LPRO) which fills the role of the ''disease'' which ''spreads'' through the industry. In the single advance models, LPRO is assumed to have moved forward at some point in time, after which an individual firm may advance to the limit by virtue of its own research and development or through imitation of the successful efforts of another firm. In the recurrent advance models, LPRO is assumed to increase at either a constant absolute or relative rate. The firms, in the course of their research and imitation efforts, follow behind LPRO. Using the methods of stochastic processes, it is shown that these models are equivalent to ergodic Markov chains. Based on an assumption of constant intensity of R and D effort, it is shown how the single and recurrent advance models reflect on Joseph Schumpeter's hypothesis that more concentrated industries tend to be more technologically advanced than less concentrated. The results corroborate the weakest version of the hypothesis: monopoly prices need not be higher than competitive prices.

  20. Solving stochastic epidemiological models using computer algebra

    Hincapie, Doracelly; Ospina, Juan

    2011-06-01

    Mathematical modeling in Epidemiology is an important tool to understand the ways under which the diseases are transmitted and controlled. The mathematical modeling can be implemented via deterministic or stochastic models. Deterministic models are based on short systems of non-linear ordinary differential equations and the stochastic models are based on very large systems of linear differential equations. Deterministic models admit complete, rigorous and automatic analysis of stability both local and global from which is possible to derive the algebraic expressions for the basic reproductive number and the corresponding epidemic thresholds using computer algebra software. Stochastic models are more difficult to treat and the analysis of their properties requires complicated considerations in statistical mathematics. In this work we propose to use computer algebra software with the aim to solve epidemic stochastic models such as the SIR model and the carrier-borne model. Specifically we use Maple to solve these stochastic models in the case of small groups and we obtain results that do not appear in standard textbooks or in the books updated on stochastic models in epidemiology. From our results we derive expressions which coincide with those obtained in the classical texts using advanced procedures in mathematical statistics. Our algorithms can be extended for other stochastic models in epidemiology and this shows the power of computer algebra software not only for analysis of deterministic models but also for the analysis of stochastic models. We also perform numerical simulations with our algebraic results and we made estimations for the basic parameters as the basic reproductive rate and the stochastic threshold theorem. We claim that our algorithms and results are important tools to control the diseases in a globalized world.

  1. Stochastic differential equations and diffusion processes

    Ikeda, N

    1989-01-01

    Being a systematic treatment of the modern theory of stochastic integrals and stochastic differential equations, the theory is developed within the martingale framework, which was developed by J.L. Doob and which plays an indispensable role in the modern theory of stochastic analysis.A considerable number of corrections and improvements have been made for the second edition of this classic work. In particular, major and substantial changes are in Chapter III and Chapter V where the sections treating excursions of Brownian Motion and the Malliavin Calculus have been expanded and refined. Sectio

  2. A multilevel stochastic collocation method for SPDEs

    We present a multilevel stochastic collocation method that, as do multilevel Monte Carlo methods, uses a hierarchy of spatial approximations to reduce the overall computational complexity when solving partial differential equations with random inputs. For approximation in parameter space, a hierarchy of multi-dimensional interpolants of increasing fidelity are used. Rigorous convergence and computational cost estimates for the new multilevel stochastic collocation method are derived and used to demonstrate its advantages compared to standard single-level stochastic collocation approximations as well as multilevel Monte Carlo methods

  3. Stochastic deformation of a thermodynamic symplectic structure

    Kazinski, P. O.

    2009-01-01

    A stochastic deformation of a thermodynamic symplectic structure is studied. The stochastic deformation is analogous to the deformation of an algebra of observables such as deformation quantization, but for an imaginary deformation parameter (the Planck constant). Gauge symmetries of thermodynamics and corresponding stochastic mechanics, which describes fluctuations of a thermodynamic system, are revealed and gauge fields are introduced. A physical interpretation to the gauge transformations and gauge fields is given. An application of the formalism to a description of systems with distributed parameters in a local thermodynamic equilibrium is considered.

  4. Modeling and analysis of stochastic systems

    Kulkarni, Vidyadhar G

    2011-01-01

    Based on the author's more than 25 years of teaching experience, Modeling and Analysis of Stochastic Systems, Second Edition covers the most important classes of stochastic processes used in the modeling of diverse systems, from supply chains and inventory systems to genetics and biological systems. For each class of stochastic process, the text includes its definition, characterization, applications, transient and limiting behavior, first passage times, and cost/reward models. Along with reorganizing the material, this edition revises and adds new exercises and examples. New to the second edi

  5. STOCHASTIC GRADIENT METHODS FOR UNCONSTRAINED OPTIMIZATION

    Nataša Krejić

    2014-12-01

    Full Text Available This papers presents an overview of gradient based methods for minimization of noisy functions. It is assumed that the objective functions is either given with error terms of stochastic nature or given as the mathematical expectation. Such problems arise in the context of simulation based optimization. The focus of this presentation is on the gradient based Stochastic Approximation and Sample Average Approximation methods. The concept of stochastic gradient approximation of the true gradient can be successfully extended to deterministic problems. Methods of this kind are presented for the data fitting and machine learning problems.

  6. A Model of Stochastic Variety-Seeking

    Minakshi Trivedi; Frank M. Bass; Ram C. Rao

    1994-01-01

    In this paper, we propose and test a stochastic model of consumer choice that incorporates attribute-based variety seeking. Our stochastic variety-seeking model (SVS) has nested within it a fixed variety-seeking model, a zero-order model of choice, and a first-order (“pure variety”) model. We compare the SVS model to alternative models. Under stochastic variety seeking, we examine the nature of the variety sought and provide a test of the “satiation” hypothesis. Unlike fixed variety-seeking m...

  7. The astrophysical gravitational wave stochastic background

    Tania Regimbau

    2011-01-01

    A stochastic background of gravitational waves with astrophysical origins may have resulted from the superposition of a large number of unresolved sources since the beginning of stellar activity.Its detection would put very strong constraints on the physical properties of compact objects, the initial mass function and star formarion history.On the other hand, it could be a ‘noise' that would mask the stochastic background of its cosmological origin.We review the main astrophysical processes which are able to produce a stochastic background and discuss how they may differ from the primordial contribution in terms of statistical properties.Current detection methods are also presented.

  8. CAM Stochastic Volatility Model for Option Pricing

    Wanwan Huang

    2016-01-01

    Full Text Available The coupled additive and multiplicative (CAM noises model is a stochastic volatility model for derivative pricing. Unlike the other stochastic volatility models in the literature, the CAM model uses two Brownian motions, one multiplicative and one additive, to model the volatility process. We provide empirical evidence that suggests a nontrivial relationship between the kurtosis and skewness of asset prices and that the CAM model is able to capture this relationship, whereas the traditional stochastic volatility models cannot. We introduce a control variate method and Monte Carlo estimators for some of the sensitivities (Greeks of the model. We also derive an approximation for the characteristic function of the model.

  9. Stochastic Control Model on Rent Seeking

    2008-01-01

    A continuous-time stochastic model is constructed to analyze how to control rent seeking behaviors. Using the stochastic optimization methods based on the modern risky theory, a unique positive solution to the dynamic model is derived. The effects of preference-related parameters on the optimal control level of rent seeking are discussed, and some policy measures are given. The results show that there exists a unique solution to the stochastic dynamic model under some macroeconomic assumptions, and that raising public expenditure may have reverse effects on rent seeking in an underdeveloped or developed economic environment.

  10. Stochastic transition model for pedestrian dynamics

    Schultz, Michael

    2012-01-01

    The proposed stochastic model for pedestrian dynamics is based on existing approaches using cellular automata, combined with substantial extensions, to compensate the deficiencies resulting of the discrete grid structure. This agent motion model is extended by both a grid-based path planning and mid-range agent interaction component. The stochastic model proves its capabilities for a quantitative reproduction of the characteristic shape of the common fundamental diagram of pedestrian dynamics. Moreover, effects of self-organizing behavior are successfully reproduced. The stochastic cellular automata approach is found to be adequate with respect to uncertainties in human motion patterns, a feature previously held by artificial noise terms alone.

  11. Immigration-extinction dynamics of stochastic populations

    Meerson, Baruch; Ovaskainen, Otso

    2013-07-01

    How high should be the rate of immigration into a stochastic population in order to significantly reduce the probability of observing the population become extinct? Is there any relation between the population size distributions with and without immigration? Under what conditions can one justify the simple patch occupancy models, which ignore the population distribution and its dynamics in a patch, and treat a patch simply as either occupied or empty? We answer these questions by exactly solving a simple stochastic model obtained by adding a steady immigration to a variant of the Verhulst model: a prototypical model of an isolated stochastic population.

  12. High-speed Stochastic Fatigue Testing

    Brincker, Rune; Sørensen, John Dalsgaard

    1990-01-01

    Good stochastic fatigue tests are difficult to perform. One of the major reasons is that ordinary servohydraulic loading systems realize the prescribed load history accurately at very low testing speeds only. If the speeds used for constant amplitude testing are applied to stochastic fatigue...... testing, quite unacceptable errors are introduced. Usually this problem is solved by running the tests at very low speeds and by editing the load history in order to reduce the duration of the test. In this paper a new method for control of stochastic fatigue tests is proposed. It is based on letting the...... allowable speed by a factor from 10 to 30....

  13. Stochastic Einstein equations with fluctuating volume

    Dzhunushaliev, Vladimir

    2016-01-01

    We develop a simple model to study classical fields on the background of a fluctuating spacetime volume. It is applied to formulate the stochastic Einstein equations with a perfect-fluid source. We investigate the particular case of a stochastic Friedmann-Lema\\^itre-Robertson-Walker cosmology, and show that the resulting field equations can lead to solutions which avoid the initial big bang singularity. By interpreting the fluctuations as the result of the presence of a quantum spacetime, we conclude that classical singularities can be avoided even within a stochastic model that include quantum effects in a very simple manner.

  14. Topological charge conservation in stochastic optical fields

    Roux, Filippus S.

    2016-05-01

    The fact that phase singularities in scalar stochastic optical fields are topologically conserved implies the existence of an associated conserved current, which can be expressed in terms of local correlation functions of the optical field and its transverse derivatives. Here, we derive the topological charge current for scalar stochastic optical fields and show that it obeys a conservation equation. We use the expression for the topological charge current to investigate the topological charge flow in inhomogeneous stochastic optical fields with a one-dimensional topological charge density.

  15. Recent advances in ambit stochastics with a view towards tempo-spatial stochastic volatility/intermittency

    Barndorff-Nielsen, Ole E.; Benth, Fred Espen; Veraart, Almut

    Ambit stochastics is the name for the theory and applications of ambit fields and ambit processes and constitutes a new research area in stochastics for tempo-spatial phenomena. This paper gives an overview of the main findings in ambit stochastics up to date and establishes new results on general...... properties of ambit fields. Moreover, it develops the concept of tempo-spatial stochastic volatility/intermittency within ambit fields. Various types of volatility modulation ranging from stochastic scaling of the amplitude, to stochastic time change and extended subordination of random measures and to...... probability and L\\'{e}vy mixing of volatility/intensity parameters will be developed. Important examples for concrete model specifications within the class of ambit fields are given....

  16. Stochastic pump effect and geometric phases in dissipative and stochastic systems

    Sinitsyn, Nikolai [Los Alamos National Laboratory

    2008-01-01

    The success of Berry phases in quantum mechanics stimulated the study of similar phenomena in other areas of physics, including the theory of living cell locomotion and motion of patterns in nonlinear media. More recently, geometric phases have been applied to systems operating in a strongly stochastic environment, such as molecular motors. We discuss such geometric effects in purely classical dissipative stochastic systems and their role in the theory of the stochastic pump effect (SPE).

  17. Option pricing under stochastic volatility and stochastic interest rate in the Spanish case

    S??ez, Marc

    1995-01-01

    Among the underlying assumptions of the Black-Scholes option pricing model, those of a fixed volatility of the underlying asset and of a constant short-term riskless interest rate, cause the largest empirical biases. Only recently has attention been paid to the simultaneous effects of the stochastic nature of both variables on the pricing of options. This paper has tried to estimate the effects of a stochastic volatility and a stochastic interest rate in the Spanish...

  18. The Dynamic Programming Method of Stochastic Differential Game for Functional Forward-Backward Stochastic System

    Shaolin Ji; Chuanfeng Sun; Qingmeng Wei

    2013-01-01

    This paper is devoted to a stochastic differential game (SDG) of decoupled functional forward-backward stochastic differential equation (FBSDE). For our SDG, the associated upper and lower value functions of the SDG are defined through the solution of controlled functional backward stochastic differential equations (BSDEs). Applying the Girsanov transformation method introduced by Buckdahn and Li (2008), the upper and the lower value functions are shown to be deterministic. We also generalize...

  19. Stochastic modeling for the COMET-assay

    Boulesteix, Anne-Laure; Hösel, Volker; Liebscher, Volkmar

    2003-01-01

    We present a stochastic model for single cell gel electrophoresis (COMET-assay) data. Essential is the use of point process structures, renewal theory and reduction to intensity histograms for further data analysis.

  20. Bootstrap performance profiles in stochastic algorithms assessment

    Optimization with stochastic algorithms has become a relevant research field. Due to its stochastic nature, its assessment is not straightforward and involves integrating accuracy and precision. Performance profiles for the mean do not show the trade-off between accuracy and precision, and parametric stochastic profiles require strong distributional assumptions and are limited to the mean performance for a large number of runs. In this work, bootstrap performance profiles are used to compare stochastic algorithms for different statistics. This technique allows the estimation of the sampling distribution of almost any statistic even with small samples. Multiple comparison profiles are presented for more than two algorithms. The advantages and drawbacks of each assessment methodology are discussed

  1. Communication: Embedded fragment stochastic density functional theory

    We develop a method in which the electronic densities of small fragments determined by Kohn-Sham density functional theory (DFT) are embedded using stochastic DFT to form the exact density of the full system. The new method preserves the scaling and the simplicity of the stochastic DFT but cures the slow convergence that occurs when weakly coupled subsystems are treated. It overcomes the spurious charge fluctuations that impair the applications of the original stochastic DFT approach. We demonstrate the new approach on a fullerene dimer and on clusters of water molecules and show that the density of states and the total energy can be accurately described with a relatively small number of stochastic orbitals

  2. Assessing the quality of stochastic oscillations

    Guillermo Abramson; Sebastián Risau-Gusman

    2008-06-01

    We analyze the relationship between the macroscopic and microscopic descriptions of two-state systems, in particular the regime in which the microscopic one shows sustained `stochastic oscillations' while the macroscopic tends to a fixed point. We propose a quantification of the oscillatory appearance of the fluctuating populations, and show that good stochastic oscillations are present if a parameter of the macroscopic model is small, and that no microscopic model will show oscillations if that parameter is large. The transition between these two regimes is smooth. In other words, given a macroscopic deterministic model, one can know whether any microscopic stochastic model that has it as a limit, will display good sustained stochastic oscillations.

  3. Electric charge in the stochastic electric field

    Simonov, Yu A

    2016-01-01

    The influence of electric stochastic fields on the relativistic charged particles is investigated in the gauge invariant path integral formalism. Using the cumulant expansion one finds the exponential relaxation of the charge Green's function both for spinless and Dirac charges.

  4. Quadratic Stochastic Operators with Countable State Space

    Ganikhodjaev, Nasir

    2016-03-01

    In this paper, we provide the classes of Poisson and Geometric quadratic stochastic operators with countable state space, study the dynamics of these operators and discuss their application to economics.

  5. Transient Growth in Stochastic Burgers Flows

    Poças, Diogo

    2015-01-01

    This study considers the problem of the extreme behavior exhibited by solutions to Burgers equation subject to stochastic forcing. More specifically, we are interested in the maximum growth achieved by the "enstrophy" (the Sobolev $H^1$ seminorm of the solution) as a function of the initial enstrophy $\\mathcal{E}_0$, in particular, whether in the stochastic setting this growth is different than in the deterministic case considered by Ayala & Protas (2011). This problem is motivated by questions about the effect of noise on the possible singularity formation in hydrodynamic models. The main quantities of interest in the stochastic problem are the expected value of the enstrophy and the enstrophy of the expected value of the solution. The stochastic Burgers equation is solved numerically with a Monte Carlo sampling approach. By studying solutions obtained for a range of optimal initial data and different noise magnitudes, we reveal different solution behaviors and it is demonstrated that the two quantities ...

  6. Linear systems control deterministic and stochastic methods

    Hendricks, Elbert; Sørensen, Paul Haase

    2008-01-01

    Linear Systems Control provides a very readable graduate text giving a good foundation for reading more rigorous texts. There are multiple examples, problems and solutions. This unique book successfully combines stochastic and deterministic methods.

  7. Stochastic differential equation model to Prendiville processes

    Granita, E-mail: granitafc@gmail.com [Dept. of Mathematical Science, Universiti Teknologi Malaysia, 81310, Johor Malaysia (Malaysia); Bahar, Arifah [Dept. of Mathematical Science, Universiti Teknologi Malaysia, 81310, Johor Malaysia (Malaysia); UTM Center for Industrial & Applied Mathematics (UTM-CIAM) (Malaysia)

    2015-10-22

    The Prendiville process is another variation of the logistic model which assumes linearly decreasing population growth rate. It is a continuous time Markov chain (CTMC) taking integer values in the finite interval. The continuous time Markov chain can be approximated by stochastic differential equation (SDE). This paper discusses the stochastic differential equation of Prendiville process. The work started with the forward Kolmogorov equation in continuous time Markov chain of Prendiville process. Then it was formulated in the form of a central-difference approximation. The approximation was then used in Fokker-Planck equation in relation to the stochastic differential equation of the Prendiville process. The explicit solution of the Prendiville process was obtained from the stochastic differential equation. Therefore, the mean and variance function of the Prendiville process could be easily found from the explicit solution.

  8. ECE6010 - Stochastic Processes, Spring 2006

    Moon, Todd K.

    2006-01-01

    This course provides an introduction to stochastic processes in communications, signal processing, digital and computer systems, and control. Topics include continuous and discrete random processes, correlation and power spectral density, optimal filtering, Markov chains, and queuing theory. Technical Requirements: MATLAB

  9. Extending Stochastic Network Calculus to Loss Analysis

    Chao Luo

    2013-01-01

    Full Text Available Loss is an important parameter of Quality of Service (QoS. Though stochastic network calculus is a very useful tool for performance evaluation of computer networks, existing studies on stochastic service guarantees mainly focused on the delay and backlog. Some efforts have been made to analyse loss by deterministic network calculus, but there are few results to extend stochastic network calculus for loss analysis. In this paper, we introduce a new parameter named loss factor into stochastic network calculus and then derive the loss bound through the existing arrival curve and service curve via this parameter. We then prove that our result is suitable for the networks with multiple input flows. Simulations show the impact of buffer size, arrival traffic, and service on the loss factor.

  10. Stochastic and epistemic uncertainty propagation in LCA

    Clavreul, Julie; Guyonnet, Dominique; Tonini, Davide;

    2013-01-01

    When performing uncertainty propagation, most LCA practitioners choose to represent uncertainties by single probability distributions and to propagate them using stochastic methods. However, the selection of single probability distributions appears often arbitrary when faced with scarce informati...

  11. Stochastic Effects; Application in Nuclear Physics

    Stochastic effects in nuclear physics refer to the study of the dynamics of nuclear systems evolving under stochastic equations of motion. In this dissertation we restrict our attention to classical scattering models. We begin with introduction of the model of nuclear dynamics and deterministic equations of evolution. We apply a Langevin approach - an additional property of the model, which reflect the statistical nature of low energy nuclear behaviour. We than concentrate our attention on the problem of calculating tails of distribution functions, which actually is the problem of calculating probabilities of rare outcomes. Two general strategies are proposed. Result and discussion follow. Finally in the appendix we consider stochastic effects in nonequilibrium systems. A few exactly solvable models are presented. For one model we show explicitly that stochastic behaviour in a microscopic description can lead to ordered collective effects on the macroscopic scale. Two others are solved to confirm the predictions of the fluctuation theorem. (author)

  12. Embedded fragment stochastic density functional theory

    Neuhauser, Daniel; Rabani, Eran

    2014-01-01

    We develop a method in which the electronic densities of small fragments determined by Kohn-Sham density functional theory (DFT) are embedded using stochastic DFT to form the exact density of the full system. The new method preserves the scaling and the simplicity of the stochastic DFT but cures the slow convergence that occurs when weakly coupled subsystems are treated. It overcomes the spurious charge fluctuations that impair the applications of the original stochastic DFT approach. We demonstrate the new approach on a fullerene dimer and on clusters of water molecules and show that the density of states and the total energy can be accurately described with a relatively small number of stochastic orbitals.

  13. Stochastic dynamical models for ecological regime shifts

    Møller, Jan Kloppenborg; Carstensen, Jacob; Madsen, Henrik; Andersen, Tom

    Ecosystems are influenced by a variety of known and unknown drivers. Unknown drivers should be modeled as noise and it is therefore important to analyze how noise influences the deterministic skeleton of system equations. The deterministic skeleton of stochastic dynamical models contains the...... physical and biological knowledge of the system, and nonlinearities introduced here can generate regime shifts or enhance the probability of regime shifts in the case of stochastic models, typically characterized by a threshold value for the known driver. A simple model for light competition between...... phytoplankton and benthic vegetation with feedback mechanisms is formulated, and it is demonstrated that bistability can occur for specific parameter settings. When stochastic input and stochastic propagation of the states are applied on the system regime shifts occur more frequently, and the threshold...

  14. MODELLING OF A STOCHASTIC CONTINUOUS SYSTEM

    Martin Albertyn

    2012-01-01

    Full Text Available The key objective is to develop a method which can be utilized to model a stochastic continuous system. A system from the "real world" is used as the basis for the simulation modelling technique that is presented. The conceptualization phase indicates that the model has to incorporate stochastic and deterministic elements. A method is developed that utilizes the discrete simulation ability of a stochastic package (ARENA, in conjunction with a deterministic package (FORTRAN, to model the continuous system. (Software packages tend to specialize in either stochastic, or deterministic modelling. The length of the iteration time interval and adequate sample size are investigated. The method is authenticated by the verification and validation ofthe defined model. Two scenarios are modelled and the results are discussed . Conclusions are presented and strengths and weaknesses of this method are considered and discussed .

  15. Fractional Smoothness of Some Stochastic Integrals

    Peng XIE; Xi Cheng ZHANG

    2007-01-01

    We study the fractional smoothness in the sense of Malliavin calculus of stochastic integralsof the form ∫10 φ(Xs)d Xs,where Xs is a semimartingale and φ belongs to some fractional Sobolev spaceover R.

  16. Synchronization of noisy systems by stochastic signals

    We study, in terms of synchronization, the nonlinear response of noisy bistable systems to a stochastic external signal, represented by Markovian dichotomic noise. We propose a general kinetic model which allows us to conduct a full analytical study of the nonlinear response, including the calculation of cross-correlation measures, the mean switching frequency, and synchronization regions. Theoretical results are compared with numerical simulations of a noisy overdamped bistable oscillator. We show that dichotomic noise can instantaneously synchronize the switching process of the system. We also show that synchronization is most pronounced at an optimal noise level emdash this effect connects this phenomenon with aperiodic stochastic resonance. Similar synchronization effects are observed for a stochastic neuron model stimulated by a stochastic spike train. copyright 1999 The American Physical Society

  17. Regression Analysis with a Stochastic Design Variable

    Sazak,, Hakan S.; Moti L Tiku; Qamarul Islam, M.

    2006-01-01

    In regression models, the design variable has primarily been treated as a nonstochastic variable. In numerous situations, however, the design variable is stochastic. The estimation and hypothesis testing problems in such situations are considered. Real life examples are given.

  18. Transport stochastic multi-dimensional media

    Many physical phenomena evolve according to known deterministic rules, but in a stochastic media in which the composition changes in space and time. Examples to such phenomena are heat transfer in turbulent atmosphere with non uniform diffraction coefficients, neutron transfer in boiling coolant of a nuclear reactor and radiation transfer through concrete shields. The results of measurements conducted upon such a media are stochastic by nature, and depend on the specific realization of the media. In the last decade there has been a considerable efforts to describe linear particle transport in one dimensional stochastic media composed of several immiscible materials. However, transport in two or three dimensional stochastic media has been rarely addressed. The important effect in multi-dimensional transport that does not appear in one dimension is the ability to bypass obstacles. The current work is an attempt to quantify this effect. (authors)

  19. Applications of Stochastic Ordering to Wireless Communications

    Tepedelenlioglu, Cihan; Zhang, Yuan

    2011-01-01

    Stochastic orders are binary relations defined on probability distributions which capture intuitive notions like being larger or being more variable. This paper introduces stochastic ordering of instantaneous SNRs of fading channels as a tool to compare the performance of communication systems over different channels. Stochastic orders unify existing performance metrics such as ergodic capacity, and metrics based on error rate functions for commonly used modulation schemes through their relation with convex, and completely monotonic (c.m.) functions. Toward this goal, performance metrics such as instantaneous error rates of M-QAM and M-PSK modulations are shown to be c.m. functions of the instantaneous SNR, while metrics such as the instantaneous capacity are seen to have a completely monotonic derivative (c.m.d.). It is shown that the commonly used parametric fading distributions for modeling line of sight (LoS), exhibit a monotonicity in the LoS parameter with respect to the stochastic Laplace transform ord...

  20. Stochastic differential equation model to Prendiville processes

    The Prendiville process is another variation of the logistic model which assumes linearly decreasing population growth rate. It is a continuous time Markov chain (CTMC) taking integer values in the finite interval. The continuous time Markov chain can be approximated by stochastic differential equation (SDE). This paper discusses the stochastic differential equation of Prendiville process. The work started with the forward Kolmogorov equation in continuous time Markov chain of Prendiville process. Then it was formulated in the form of a central-difference approximation. The approximation was then used in Fokker-Planck equation in relation to the stochastic differential equation of the Prendiville process. The explicit solution of the Prendiville process was obtained from the stochastic differential equation. Therefore, the mean and variance function of the Prendiville process could be easily found from the explicit solution

  1. Stochastic dynamics and Fokker-Planck equation

    The aim of this contribution is to study the particle dynamics in a storage ring under the influence of noise. Some simplified stochastic beam dynamics problems are treated by solving the corresponding Fokker-Planck equations numerically

  2. Stochastic fluctuation induced the competition between extinction and recurrence in a model of tumor growth

    Li, Dongxi, E-mail: lidongxi@yahoo.cn [Department of Applied Mathematics, Northwestern Polytechnical University, Xi' an, 710072 (China); Xu, Wei; Sun, Chunyan; Wang, Liang [Department of Applied Mathematics, Northwestern Polytechnical University, Xi' an, 710072 (China)

    2012-04-30

    We investigate the phenomenon that stochastic fluctuation induced the competition between tumor extinction and recurrence in the model of tumor growth derived from the catalytic Michaelis–Menten reaction. We analyze the probability transitions between the extinction state and the state of the stable tumor by the Mean First Extinction Time (MFET) and Mean First Return Time (MFRT). It is found that the positional fluctuations hinder the transition, but the environmental fluctuations, to a certain level, facilitate the tumor extinction. The observed behavior could be used as prior information for the treatment of cancer. -- Highlights: ► Stochastic fluctuation induced the competition between extinction and recurrence. ► The probability transitions are investigated. ► The positional fluctuations hinder the transition. ► The environmental fluctuations, to a certain level, facilitate the tumor extinction. ► The observed behavior can be used as prior information for the treatment of cancer.

  3. Stochastic fluctuation induced the competition between extinction and recurrence in a model of tumor growth

    We investigate the phenomenon that stochastic fluctuation induced the competition between tumor extinction and recurrence in the model of tumor growth derived from the catalytic Michaelis–Menten reaction. We analyze the probability transitions between the extinction state and the state of the stable tumor by the Mean First Extinction Time (MFET) and Mean First Return Time (MFRT). It is found that the positional fluctuations hinder the transition, but the environmental fluctuations, to a certain level, facilitate the tumor extinction. The observed behavior could be used as prior information for the treatment of cancer. -- Highlights: ► Stochastic fluctuation induced the competition between extinction and recurrence. ► The probability transitions are investigated. ► The positional fluctuations hinder the transition. ► The environmental fluctuations, to a certain level, facilitate the tumor extinction. ► The observed behavior can be used as prior information for the treatment of cancer.

  4. Modular and Stochastic Approaches to Molecular Pathway Models of ATM, TGF beta, and WNT Signaling

    Cucinotta, Francis A.; O'Neill, Peter; Ponomarev, Artem; Carra, Claudio; Whalen, Mary; Pluth, Janice M.

    2009-01-01

    Deterministic pathway models that describe the biochemical interactions of a group of related proteins, their complexes, activation through kinase, etc. are often the basis for many systems biology models. Low dose radiation effects present a unique set of challenges to these models including the importance of stochastic effects due to the nature of radiation tracks and small number of molecules activated, and the search for infrequent events that contribute to cancer risks. We have been studying models of the ATM, TGF -Smad and WNT signaling pathways with the goal of applying pathway models to the investigation of low dose radiation cancer risks. Modeling challenges include introduction of stochastic models of radiation tracks, their relationships to more than one substrate species that perturb pathways, and the identification of a representative set of enzymes that act on the dominant substrates. Because several pathways are activated concurrently by radiation the development of modular pathway approach is of interest.

  5. Stochastic TDHF and the Boltzman-Langevin equation

    Outgoing from a time-dependent theory of correlations, we present a stochastic differential equation for the propagation of ensembles of Slater determinants, called Stochastic Time-Dependent Hartree-Fock (Stochastic TDHF). These ensembles are allowed to develop large fluctuations in the Hartree-Fock mean fields. An alternative stochastic differential equation, the Boltzmann-Langevin equation, can be derived from Stochastic TDHF by averaging over subensembles with small fluctuations

  6. Stability of infinite dimensional stochastic differential equations with applications

    Liu, Kai

    2005-01-01

    PrefaceSTOCHASTIC DIFFERENTIAL EQUATIONS IN INFINITE DIMENSIONSNotations,Definitions and Preliminaries Wiener Processes and Stochastic Integration Definitions and Methods of Stability Notes and Comments STABILITY F LINEAR STOCHASTIC DIFFERENTIAL EQUATIONSStable Semigroups Lyapunov Equations and Stability Uniformly Asymptotic Stability STABILITY F NONLINEAR STOCHASTIC DIFFERENTIAL EQUATIONSEquivalence of L p -Stability and Exponential Stability A Coerciv Decay Condition Stability of Semilinear Stochastic Evolution Equations Lyapunov Functions for Strong Solutions Two Applications Further Result

  7. Stochastic description of quantum Brownian dynamics

    Yan, Yun-An; Shao, Jiushu

    2016-08-01

    Classical Brownian motion has well been investigated since the pioneering work of Einstein, which inspired mathematicians to lay the theoretical foundation of stochastic processes. A stochastic formulation for quantum dynamics of dissipative systems described by the system-plus-bath model has been developed and found many applications in chemical dynamics, spectroscopy, quantum transport, and other fields. This article provides a tutorial review of the stochastic formulation for quantum dissipative dynamics. The key idea is to decouple the interaction between the system and the bath by virtue of the Hubbard-Stratonovich transformation or Itô calculus so that the system and the bath are not directly entangled during evolution, rather they are correlated due to the complex white noises introduced. The influence of the bath on the system is thereby defined by an induced stochastic field, which leads to the stochastic Liouville equation for the system. The exact reduced density matrix can be calculated as the stochastic average in the presence of bath-induced fields. In general, the plain implementation of the stochastic formulation is only useful for short-time dynamics, but not efficient for long-time dynamics as the statistical errors go very fast. For linear and other specific systems, the stochastic Liouville equation is a good starting point to derive the master equation. For general systems with decomposable bath-induced processes, the hierarchical approach in the form of a set of deterministic equations of motion is derived based on the stochastic formulation and provides an effective means for simulating the dissipative dynamics. A combination of the stochastic simulation and the hierarchical approach is suggested to solve the zero-temperature dynamics of the spin-boson model. This scheme correctly describes the coherent-incoherent transition (Toulouse limit) at moderate dissipation and predicts a rate dynamics in the overdamped regime. Challenging problems

  8. A New Theory of Stochastic Inflation

    Matacz, Andrew

    1996-01-01

    The stochastic inflation program is a framework for understanding the dynamics of a quantum scalar field driving an inflationary phase. Though widely used and accepted, there have over recent years been serious criticisms of this theory. In this paper I will present a new theory of stochastic inflation which avoids the problems of the conventional approach. Specifically, the theory can address the quantum-to-classical transition problem, and it will be shown to lead to a dramatic easing of th...

  9. Scalable Kernel Methods via Doubly Stochastic Gradients

    Dai, Bo; Xie, Bo; He, Niao; Liang, Yingyu; Raj, Anant; Balcan, Maria-Florina; Song, Le

    2014-01-01

    The general perception is that kernel methods are not scalable, and neural nets are the methods of choice for nonlinear learning problems. Or have we simply not tried hard enough for kernel methods? Here we propose an approach that scales up kernel methods using a novel concept called "doubly stochastic functional gradients". Our approach relies on the fact that many kernel methods can be expressed as convex optimization problems, and we solve the problems by making two unbiased stochastic ap...

  10. Crossing Statistics of Anisotropic Stochastic Surface

    Nezhadhaghighi, M Ghasemi; Yasseri, T; Allaei, S M Vaez

    2015-01-01

    We use crossing statistics and its generalization to determine the anisotropic direction imposed on a stochastic fields in $(2+1)$Dimension. This approach enables us to examine not only the rotational invariance of morphology but also we can determine the Gaussianity of underlying stochastic field in various dimensions. Theoretical prediction of up-crossing statistics (crossing with positive slope at a given threshold $\\alpha$ of height fluctuation), $\

  11. The Stochastic Dynamics of Epidemic Models

    Black, Andrew James

    2010-01-01

    This thesis is concerned with quantifying the dynamical role of stochasticity in models of recurrent epidemics. Although the simulation of stochastic models can accurately capture the qualitative epidemic patterns of childhood diseases, there is still considerable discussion concerning the basic mechanisms generating these patterns. The novel aspect of this thesis is the use of analytic methods to quantify the results from simulations. All the models are formulated as continuous time Markov ...

  12. Online Advertisement, Optimization and Stochastic Networks

    Tan, Bo; Srikant, R.

    2010-01-01

    In this paper, we propose a stochastic model to describe how search service providers charge client companies based on users' queries for the keywords related to these companies' ads by using certain advertisement assignment strategies. We formulate an optimization problem to maximize the long-term average revenue for the service provider under each client's long-term average budget constraint, and design an online algorithm which captures the stochastic properties of users' queries and click...

  13. Desynchronization of stochastically synchronized chemical oscillators

    Experimental and theoretical studies are presented on the design of perturbations that enhance desynchronization in populations of oscillators that are synchronized by periodic entrainment. A phase reduction approach is used to determine optimal perturbation timing based upon experimentally measured phase response curves. The effectiveness of the perturbation waveforms is tested experimentally in populations of periodically and stochastically synchronized chemical oscillators. The relevance of the approach to therapeutic methods for disrupting phase coherence in groups of stochastically synchronized neuronal oscillators is discussed

  14. Testing for Stochastic Dominance with Diversification Possibilities

    2001-01-01

    We derive empirical tests for stochastic dominance that allow for diversification between choice alternatives. The tests can be computed using straightforward linear programming. Bootstrapping techniques and asymptotic distribution theory can approximate the sampling properties of the test results and allow for statistical inference. Our results could provide a stimulus to the further proliferation of stochastic dominance for the problem of portfolio selection and evaluation (as well as other...

  15. Stability of Stochastic Neutral Cellular Neural Networks

    Chen, Ling; Zhao, Hongyong

    In this paper, we study a class of stochastic neutral cellular neural networks. By constructing a suitable Lyapunov functional and employing the nonnegative semi-martingale convergence theorem we give some sufficient conditions ensuring the almost sure exponential stability of the networks. The results obtained are helpful to design stability of networks when stochastic noise is taken into consideration. Finally, two examples are provided to show the correctness of our analysis.

  16. CREDIT DERIVATIVES PRICING WITH STOCHASTIC VOLATILITY MODELS

    CARL CHIARELLA; SAMUEL CHEGE MAINA; CHRISTINA NIKITOPOULOS SKLIBOSIOS

    2013-01-01

    This paper proposes a model for pricing credit derivatives in a defaultable HJM framework. The model features hump-shaped, level dependent, and unspanned stochastic volatility, and accommodates a correlation structure between the stochastic volatility, the default-free interest rates, and the credit spreads. The model is finite-dimensional, and leads (a) to exponentially affine default-free and defaultable bond prices, and (b) to an approximation for pricing credit default swaps and swaptions...

  17. Topologies of Stochastic Markov Models: Computational Aspects

    Bacci, Giorgio; Bacci, Giovanni; Larsen, Kim G.; Mardare, Radu

    2014-01-01

    In this paper we propose two behavioral distances that support approximate reasoning on Stochastic Markov Models (SMMs), that are continuous-time stochastic transition systems where the residence time on each state is described by a generic probability measure on the positive real line. In particular, we study the problem of measuring the behavioral dissimilarity of two SMMs against linear real-time specifications expressed as Metric Temporal Logic (MTL) formulas or Deterministic Timed-Automa...

  18. Stochastic Physics, Complex Systems and Biology

    Qian, Hong

    2012-01-01

    In complex systems, the interplay between nonlinear and stochastic dynamics gives rise to an evolution process in Darwinian sense with punctuated equilibrium, random "mutations" and "adaptations". The emergent discrete states in such a system, i.e., attractors, have natural robustness against both internal and external perturbations. Epigenetic states of a biological cell, a mesoscopic nonlinear stochastic open biochemical system, could be understood through such a framework.

  19. The Stochastic Dynamics of Speculative Prices

    Carl Chiarella; Xue-Zhong He; Min Zheng

    2007-01-01

    Within the framework of the heterogeneous agent paradigm, we establish a stochastic model of speculative price dynamics involving of two types of agents, fundamentalists and chartists, and the market price equilibria of which can be characterised by the invariant measures of a random dynamical system. By conducting a stochastic bifurcation analysis, we examine the market impact of speculative behaviour. We show that, when the chartists use lagged price trends to form their expectations, the m...

  20. Stochastic Programming E-Print Series

    Morton, David P.; Pan, Feng; Saeger, Kevin J.

    2006-01-01

    We describe two stochastic network interdiction models for thwarting nuclear smuggling. In the first model, the smuggler travels through a transportation network on a path that maximizes the probability of evading detection, and the interdictor installs radiation sensors to minimize that evasion probability. The problem is stochastic because the smuggler’s origin-destination pair is known only through a probability distribution at the time when the sensors are installed. In this model, th...

  1. Mean Field Approximation of Uncertain Stochastic Models

    Bortolussi, Luca; Gast, Nicolas

    2016-01-01

    —We consider stochastic models in presence of uncertainty , originating from lack of knowledge of parameters or by unpredictable effects of the environment. We focus on population processes, encompassing a large class of systems, from queueing networks to epidemic spreading. We set up a formal framework for imprecise stochastic processes, where some parameters are allowed to vary in time within a given domain, but with no further constraint. We then consider the limit behaviour of these syste...

  2. On the stochastic dynamics of molecular conformation

    2007-01-01

    An important functioning mechanism of biological macromolecules is the transition between different conformed states due to thermal fluctuation. In the present paper, a biological macromolecule is modeled as two strands with side chains facing each other, and its stochastic dynamics including the statistics of stationary motion and the statistics of conformational transition is studied by using the stochastic averaging method for quasi Hamiltonian systems. The theoretical results are confirmed with the results from Monte Carlo simulation.

  3. Stochastic relations foundations for Markov transition systems

    Doberkat, Ernst-Erich

    2007-01-01

    Collecting information previously scattered throughout the vast literature, including the author's own research, Stochastic Relations: Foundations for Markov Transition Systems develops the theory of stochastic relations as a basis for Markov transition systems. After an introduction to the basic mathematical tools from topology, measure theory, and categories, the book examines the central topics of congruences and morphisms, applies these to the monoidal structure, and defines bisimilarity and behavioral equivalence within this framework. The author views developments from the general

  4. Numerical Stochastic Perturbation Theory for full QCD

    F. Di Renzo; Scorzato, L.

    2004-01-01

    We give a full account of the Numerical Stochastic Perturbation Theory method for Lattice Gauge Theories. Particular relevance is given to the inclusion of dynamical fermions, which turns out to be surprisingly cheap in this context. We analyse the underlying stochastic process and discuss the convergence properties. We perform some benchmark calculations and - as a byproduct - we present original results for Wilson loops and the 3-loop critical mass for Wilson fermions.

  5. Stochastic Portfolio Theory: A Machine Learning Perspective

    Samo, Yves-Laurent Kom; Vervuurt, Alexander

    2016-01-01

    In this paper we propose a novel application of Gaussian processes (GPs) to financial asset allocation. Our approach is deeply rooted in Stochastic Portfolio Theory (SPT), a stochastic analysis framework introduced by Robert Fernholz that aims at flexibly analysing the performance of certain investment strategies in stock markets relative to benchmark indices. In particular, SPT has exhibited some investment strategies based on company sizes that, under realistic assumptions, outperform bench...

  6. Statistical Model Checking for Stochastic Hybrid Systems

    David, Alexandre; Du, Dehui; Larsen, Kim Guldstrand; Legay, Axel; Mikučionis, Marius; Poulsen, Danny Bøgsted; Sedwards, Sean

    This paper presents novel extensions and applications of the UPPAAL-SMC model checker. The extensions allow for statistical model checking of stochastic hybrid systems. We show how our race-based stochastic semantics extends to networks of hybrid systems, and indicate the integration technique...... applied for implementing this semantics in the UPPAAL-SMC simulation engine. We report on two applications of the resulting tool-set coming from systems biology and energy aware buildings....

  7. Experimental Tests for Stochastic Reduction Models

    Brody, D; Brody, Dorje; Hughston, Lane

    2001-01-01

    Stochastic models for quantum state reduction give rise to statistical laws that are in many respects in agreement with those of standard quantum measurement theory. Here we construct a counterexample involving a Hamiltonian with degenerate eigenvalues such that the statistical predictions of stochastic reduction models differ from the predictions of quantum measurement theory. An idealised experiment is proposed whereby the validity of these predictions can be put to the test.

  8. Stochastic thermodynamics of chemical reaction networks

    Schmiedl, Tim; Seifert, Udo

    2006-01-01

    For chemical reaction networks described by a master equation, we define energy and entropy on a stochastic trajectory and develop a consistent nonequilibrium thermodynamic description along a single stochastic trajectory of reaction events. A first-law like energy balance relates internal energy, applied (chemical) work and dissipated heat for every single reaction. Entropy production along a single trajectory involves a sum over changes in the entropy of the network itself and the entropy o...

  9. Second Quantization Approach to Stochastic Epidemic Models

    Mondaini, Leonardo

    2015-01-01

    We show how the standard field theoretical language based on creation and annihilation operators may be used for a straightforward derivation of closed master equations describing the population dynamics of multivariate stochastic epidemic models. In order to do that, we introduce an SIR-inspired stochastic model for hepatitis C virus epidemic, from which we obtain the time evolution of the mean number of susceptible, infected, recovered and chronically infected individuals in a population whose total size is allowed to change.

  10. Stochastic chains with memory of variable length

    Galves, Antonio; Loecherbach, Eva

    2008-01-01

    International audience Stochastic chains with memory of variable length constitute an interesting family of stochastic chains of infinite order on a finite alphabet. The idea is that for each past, only a finite suffix of the past, called context, is enough to predict the next symbol. These models were first introduced in the information theory literature by Rissanen (1983) as a universal tool to perform data compression. Recently, they have been used to model up scientific data in areas a...

  11. Stochastic stability of continuous time consensus protocols

    Medvedev, Georgi S.

    2010-01-01

    A unified approach to studying convergence and stochastic stability of continuous time consensus protocols (CPs) is presented in this work. Our method applies to networks with directed information flow; both cooperative and noncooperative interactions; networks under weak stochastic forcing; and those whose topology and strength of connections may vary in time. The graph theoretic interpretation of the analytical results is emphasized. We show how the spectral properties, such as algebraic co...

  12. Desynchronization of stochastically synchronized chemical oscillators

    Snari, Razan; Tinsley, Mark R., E-mail: mark.tinsley@mail.wvu.edu, E-mail: kshowalt@wvu.edu; Faramarzi, Sadegh; Showalter, Kenneth, E-mail: mark.tinsley@mail.wvu.edu, E-mail: kshowalt@wvu.edu [C. Eugene Bennett Department of Chemistry, West Virginia University, Morgantown, West Virginia 26506-6045 (United States); Wilson, Dan; Moehlis, Jeff [Department of Mechanical Engineering, University of California, Santa Barbara, California 93106 (United States); Netoff, Theoden Ivan [Department of Biomedical Engineering, University of Minnesota, Minneapolis, Minnesota 55455 (United States)

    2015-12-15

    Experimental and theoretical studies are presented on the design of perturbations that enhance desynchronization in populations of oscillators that are synchronized by periodic entrainment. A phase reduction approach is used to determine optimal perturbation timing based upon experimentally measured phase response curves. The effectiveness of the perturbation waveforms is tested experimentally in populations of periodically and stochastically synchronized chemical oscillators. The relevance of the approach to therapeutic methods for disrupting phase coherence in groups of stochastically synchronized neuronal oscillators is discussed.

  13. Desynchronization of stochastically synchronized chemical oscillators

    Snari, Razan; Tinsley, Mark R.; Wilson, Dan; Faramarzi, Sadegh; Netoff, Theoden Ivan; Moehlis, Jeff; Showalter, Kenneth

    2015-12-01

    Experimental and theoretical studies are presented on the design of perturbations that enhance desynchronization in populations of oscillators that are synchronized by periodic entrainment. A phase reduction approach is used to determine optimal perturbation timing based upon experimentally measured phase response curves. The effectiveness of the perturbation waveforms is tested experimentally in populations of periodically and stochastically synchronized chemical oscillators. The relevance of the approach to therapeutic methods for disrupting phase coherence in groups of stochastically synchronized neuronal oscillators is discussed.

  14. Production possibility frontier and stochastic Programming

    Chovanec, Petr

    Praha : MATFYZPRESS, 2005 - (Šafránková, J.), s. 108-113 ISBN 80-86732-59-2. [Annual Conference of Doctoral Students /14./. Praha (CZ), 07.06.2005-10.06.2005] R&D Projects: GA ČR(CZ) GA402/04/1294; GA ČR GD402/03/H057 Institutional research plan: CEZ:AV0Z10750506 Keywords : stochastic DEA * technical efficiency * stochastic programming Subject RIV: BB - Applied Statistics, Operational Research

  15. Stochastic energy balancing in substation energy management

    Hassan Shirzeh; Fazel Naghdy; Philip Ciufo; Montserrat Ros

    2015-01-01

    In the current research, a smart grid is considered as a network of distributed interacting nodes represented by renewable energy sources, storage and loads. The source nodes become active or inactive in a stochastic manner due to the intermittent nature of natural resources such as wind and solar irradiance. Prediction and stochastic modelling of electrical energy flow is a critical task in such a network in order to achieve load levelling and/or peak shaving in order to minimise the fluct...

  16. Stochastic Modelling Of The Repairable System

    Andrzejczak Karol

    2015-01-01

    All reliability models consisting of random time factors form stochastic processes. In this paper we recall the definitions of the most common point processes which are used for modelling of repairable systems. Particularly this paper presents stochastic processes as examples of reliability systems for the support of the maintenance related decisions. We consider the simplest one-unit system with a negligible repair or replacement time, i.e., the unit is operating and is repaired or replaced ...

  17. Polynomial chaos representation of a stochastic preconditioner

    Desceliers, Christophe; Ghanem, R; Soize, Christian

    2005-01-01

    A method is developed in this paper to accelerate the convergence in computing the solution of stochastic algebraic systems of equations. The method is based on computing, via statistical sampling, a polynomial chaos decomposition of a stochastic preconditioner to the system of equations. This preconditioner can subsequently be used in conjunction with either chaos representations of the solution or with approaches based on Monte Carlo sampling. In addition to presenting the supporting theory...

  18. Analog controllers using digital stochastic logic

    Quero Reboul, José Manuel; S. L. Toral; García Ortega, Juan de la Cruz; García Franquelo, Leopoldo

    1999-01-01

    Stochastic logic is based on digital processing of a random pulse stream, where the information is codified as the probability of a high level in a finite sequence. The probability of the pulse stream codifies a continuous time variable. Subsequently, this pulse stream can be digitally processed to perform analog operations. In this paper we propose a stochastic approach to the digital implementation of complex controllers. This is approach allows for the realization of the controllers, and A...

  19. Nonlinear diffusion regimes in stochastic magnetic fields

    The transport of collisional particles in stochastic magnetic fields is studied using the decorrelation trajectory method. The nonlinear effect of magnetic line trapping is considered together with particle collisions. The running diffusion coefficient is determined for arbitrary values of the statistical parameters of the stochastic magnetic field and of the collisional velocity. New diffusion regimes are found in the conditions for which the trapping of magnetic field lines is effective. (author)

  20. Stochastic transition model for pedestrian dynamics

    Schultz, Michael

    2012-01-01

    The proposed stochastic model for pedestrian dynamics is based on existing approaches using cellular automata, combined with substantial extensions, to compensate the deficiencies resulting of the discrete grid structure. This agent motion model is extended by both a grid-based path planning and mid-range agent interaction component. The stochastic model proves its capabilities for a quantitative reproduction of the characteristic shape of the common fundamental diagram of pedestrian dynamics...

  1. Stochastic modeling of nonisothermal antisolvent crystallization processes

    Cogoni, Giuseppe

    2013-01-01

    In this Thesis a stochastic approach to model antisolvent crystallization processes is addressed. The motivations to choice a stochastic approach instead of a population balance modeling has been developed to find a simple and an alternative way to describe the evolution of the Crystal Size Distribution (CSD), without consider complex thermodynamic and kinetic aspects of the process. An important parameter to consider in crystallization process is the shape of the CSD (in terms of varia...

  2. Stochastic resonance during a polymer translocation process

    Mondal, Debasish; Muthukumar, M.

    2016-04-01

    We have studied the occurrence of stochastic resonance when a flexible polymer chain undergoes a single-file translocation through a nano-pore separating two spherical cavities, under a time-periodic external driving force. The translocation of the chain is controlled by a free energy barrier determined by chain length, pore length, pore-polymer interaction, and confinement inside the donor and receiver cavities. The external driving force is characterized by a frequency and amplitude. By combining the Fokker-Planck formalism for polymer translocation and a two-state model for stochastic resonance, we have derived analytical formulas for criteria for emergence of stochastic resonance during polymer translocation. We show that no stochastic resonance is possible if the free energy barrier for polymer translocation is purely entropic in nature. The polymer chain exhibits stochastic resonance only in the presence of an energy threshold in terms of polymer-pore interactions. Once stochastic resonance is feasible, the chain entropy controls the optimal synchronization conditions significantly.

  3. Automated Flight Routing Using Stochastic Dynamic Programming

    Ng, Hok K.; Morando, Alex; Grabbe, Shon

    2010-01-01

    Airspace capacity reduction due to convective weather impedes air traffic flows and causes traffic congestion. This study presents an algorithm that reroutes flights in the presence of winds, enroute convective weather, and congested airspace based on stochastic dynamic programming. A stochastic disturbance model incorporates into the reroute design process the capacity uncertainty. A trajectory-based airspace demand model is employed for calculating current and future airspace demand. The optimal routes minimize the total expected traveling time, weather incursion, and induced congestion costs. They are compared to weather-avoidance routes calculated using deterministic dynamic programming. The stochastic reroutes have smaller deviation probability than the deterministic counterpart when both reroutes have similar total flight distance. The stochastic rerouting algorithm takes into account all convective weather fields with all severity levels while the deterministic algorithm only accounts for convective weather systems exceeding a specified level of severity. When the stochastic reroutes are compared to the actual flight routes, they have similar total flight time, and both have about 1% of travel time crossing congested enroute sectors on average. The actual flight routes induce slightly less traffic congestion than the stochastic reroutes but intercept more severe convective weather.

  4. Stochastic resonance during a polymer translocation process.

    Mondal, Debasish; Muthukumar, M

    2016-04-14

    We have studied the occurrence of stochastic resonance when a flexible polymer chain undergoes a single-file translocation through a nano-pore separating two spherical cavities, under a time-periodic external driving force. The translocation of the chain is controlled by a free energy barrier determined by chain length, pore length, pore-polymer interaction, and confinement inside the donor and receiver cavities. The external driving force is characterized by a frequency and amplitude. By combining the Fokker-Planck formalism for polymer translocation and a two-state model for stochastic resonance, we have derived analytical formulas for criteria for emergence of stochastic resonance during polymer translocation. We show that no stochastic resonance is possible if the free energy barrier for polymer translocation is purely entropic in nature. The polymer chain exhibits stochastic resonance only in the presence of an energy threshold in terms of polymer-pore interactions. Once stochastic resonance is feasible, the chain entropy controls the optimal synchronization conditions significantly. PMID:27083746

  5. Stochastic flux freezing and magnetic dynamo

    Magnetic flux conservation in turbulent plasmas at high magnetic Reynolds numbers is argued neither to hold in the conventional sense nor to be entirely broken, but instead to be valid in a statistical sense associated to the ''spontaneous stochasticity'' of Lagrangian particle trajectories. The latter phenomenon is due to the explosive separation of particles undergoing turbulent Richardson diffusion, which leads to a breakdown of Laplacian determinism for classical dynamics. Empirical evidence is presented for spontaneous stochasticity, including numerical results. A Lagrangian path-integral approach is then exploited to establish stochastic flux freezing for resistive hydromagnetic equations and to argue, based on the properties of Richardson diffusion, that flux conservation must remain stochastic at infinite magnetic Reynolds number. An important application of these results is the kinematic, fluctuation dynamo in nonhelical, incompressible turbulence at magnetic Prandtl number (Prm) equal to unity. Numerical results on the Lagrangian dynamo mechanisms by a stochastic particle method demonstrate a strong similarity between the Prm=1 and 0 dynamos. Stochasticity of field-line motion is an essential ingredient of both. Finally, some consequences for nonlinear magnetohydrodynamic turbulence, dynamo, and reconnection are briefly considered.

  6. Trajectory averaging for stochastic approximation MCMC algorithms

    Liang, Faming

    2010-10-01

    The subject of stochastic approximation was founded by Robbins and Monro [Ann. Math. Statist. 22 (1951) 400-407]. After five decades of continual development, it has developed into an important area in systems control and optimization, and it has also served as a prototype for the development of adaptive algorithms for on-line estimation and control of stochastic systems. Recently, it has been used in statistics with Markov chain Monte Carlo for solving maximum likelihood estimation problems and for general simulation and optimizations. In this paper, we first show that the trajectory averaging estimator is asymptotically efficient for the stochastic approximation MCMC (SAMCMC) algorithm under mild conditions, and then apply this result to the stochastic approximation Monte Carlo algorithm [Liang, Liu and Carroll J. Amer. Statist. Assoc. 102 (2007) 305-320]. The application of the trajectory averaging estimator to other stochastic approximationMCMC algorithms, for example, a stochastic approximation MLE algorithm for missing data problems, is also considered in the paper. © Institute of Mathematical Statistics, 2010.

  7. Postmodern string theory stochastic formulation

    Aurilia, A

    1994-01-01

    In this paper we study the dynamics of a statistical ensemble of strings, building on a recently proposed gauge theory of the string geodesic field. We show that this stochastic approach is equivalent to the Carath\\'eodory formulation of the Nambu-Goto action, supplemented by an averaging procedure over the family of classical string world-sheets which are solutions of the equation of motion. In this new framework, the string geodesic field is reinterpreted as the Gibbs current density associated with the string statistical ensemble. Next, we show that the classical field equations derived from the string gauge action, can be obtained as the semi-classical limit of the string functional wave equation. For closed strings, the wave equation itself is completely analogous to the Wheeler-DeWitt equation used in quantum cosmology. Thus, in the string case, the wave function has support on the space of all possible spatial loop configurations. Finally, we show that the string distribution induces a multi-phase, or ...

  8. Multidimensional stochastic approximation Monte Carlo.

    Zablotskiy, Sergey V; Ivanov, Victor A; Paul, Wolfgang

    2016-06-01

    Stochastic Approximation Monte Carlo (SAMC) has been established as a mathematically founded powerful flat-histogram Monte Carlo method, used to determine the density of states, g(E), of a model system. We show here how it can be generalized for the determination of multidimensional probability distributions (or equivalently densities of states) of macroscopic or mesoscopic variables defined on the space of microstates of a statistical mechanical system. This establishes this method as a systematic way for coarse graining a model system, or, in other words, for performing a renormalization group step on a model. We discuss the formulation of the Kadanoff block spin transformation and the coarse-graining procedure for polymer models in this language. We also apply it to a standard case in the literature of two-dimensional densities of states, where two competing energetic effects are present g(E_{1},E_{2}). We show when and why care has to be exercised when obtaining the microcanonical density of states g(E_{1}+E_{2}) from g(E_{1},E_{2}). PMID:27415383

  9. Stochastic models of intracellular transport

    Bressloff, Paul C.

    2013-01-09

    The interior of a living cell is a crowded, heterogenuous, fluctuating environment. Hence, a major challenge in modeling intracellular transport is to analyze stochastic processes within complex environments. Broadly speaking, there are two basic mechanisms for intracellular transport: passive diffusion and motor-driven active transport. Diffusive transport can be formulated in terms of the motion of an overdamped Brownian particle. On the other hand, active transport requires chemical energy, usually in the form of adenosine triphosphate hydrolysis, and can be direction specific, allowing biomolecules to be transported long distances; this is particularly important in neurons due to their complex geometry. In this review a wide range of analytical methods and models of intracellular transport is presented. In the case of diffusive transport, narrow escape problems, diffusion to a small target, confined and single-file diffusion, homogenization theory, and fractional diffusion are considered. In the case of active transport, Brownian ratchets, random walk models, exclusion processes, random intermittent search processes, quasi-steady-state reduction methods, and mean-field approximations are considered. Applications include receptor trafficking, axonal transport, membrane diffusion, nuclear transport, protein-DNA interactions, virus trafficking, and the self-organization of subcellular structures. © 2013 American Physical Society.

  10. Stochastic phase-change neurons

    Tuma, Tomas; Pantazi, Angeliki; Le Gallo, Manuel; Sebastian, Abu; Eleftheriou, Evangelos

    2016-08-01

    Artificial neuromorphic systems based on populations of spiking neurons are an indispensable tool in understanding the human brain and in constructing neuromimetic computational systems. To reach areal and power efficiencies comparable to those seen in biological systems, electroionics-based and phase-change-based memristive devices have been explored as nanoscale counterparts of synapses. However, progress on scalable realizations of neurons has so far been limited. Here, we show that chalcogenide-based phase-change materials can be used to create an artificial neuron in which the membrane potential is represented by the phase configuration of the nanoscale phase-change device. By exploiting the physics of reversible amorphous-to-crystal phase transitions, we show that the temporal integration of postsynaptic potentials can be achieved on a nanosecond timescale. Moreover, we show that this is inherently stochastic because of the melt-quench-induced reconfiguration of the atomic structure occurring when the neuron is reset. We demonstrate the use of these phase-change neurons, and their populations, in the detection of temporal correlations in parallel data streams and in sub-Nyquist representation of high-bandwidth signals.

  11. Stochastic metastability by spontaneous localisation

    Nonequilibrium, quasi-stationary states of a one-dimensional “hard” ϕ4 deterministic lattice, initially thermalised to a particular temperature, are investigated when brought into contact with a stochastic thermal bath at lower temperature. For lattice initial temperatures sufficiently higher than those of the bath, energy localisation through the formation of nonlinear excitations of the breather type during the cooling process occurs. These breathers keep the nonlinear lattice away from thermal equilibrium for relatively long times. In the course of time some breathers are destroyed by fluctuations, allowing thus the lattice to reach another nonequilibrium state of lower energy. The number of breathers thus reduces in time; the last remaining breather, however, exhibits amazingly long life-time demonstrated by extensive numerical simulations using a quasi-symplectic integration algorithm. For the single-breather states we have calculated the lattice velocity distribution unveiling non-Gaussian features describable in a closed functional form. Moreover, the influence of the coupling constant on the life-time of a single breather has been explored. The latter exhibits power-law behaviour as the coupling constant approaches the anticontinuous limit

  12. Fractal Geometry and Stochastics V

    Falconer, Kenneth; Zähle, Martina

    2015-01-01

    This book brings together leading contributions from the fifth conference on Fractal Geometry and Stochastics held in Tabarz, Germany, in March 2014. The book is divided into five sections covering different facets of this fast developing area: geometric measure theory, self-similar fractals and recurrent structures, analysis and algebra on fractals, multifractal theory, and random constructions. There are state-of-the-art surveys as well as papers highlighting more specific recent advances. The authors are world-experts who present their topics comprehensibly and attractively. The book provides an accessible gateway to the subject for newcomers as well as a reference for recent developments for specialists. Authors include: Krzysztof Barański, Julien Barral, Kenneth Falconer, De-Jun Feng, Peter J. Grabner, Rostislav Grigorchuk, Michael Hinz, Stéphane Jaffard, Maarit Järvenpää, Antti Käenmäki, Marc Kesseböhmer, Michel Lapidus, Klaus Mecke, Mark Pollicott,  Michał Rams, Pablo Shmerkin, and András Te...

  13. Numerical Stochastic Homogenization Method and Multiscale Stochastic Finite Element Method - A Paradigm for Multiscale Computation of Stochastic PDEs

    X. Frank Xu

    2010-03-30

    Multiscale modeling of stochastic systems, or uncertainty quantization of multiscale modeling is becoming an emerging research frontier, with rapidly growing engineering applications in nanotechnology, biotechnology, advanced materials, and geo-systems, etc. While tremendous efforts have been devoted to either stochastic methods or multiscale methods, little combined work had been done on integration of multiscale and stochastic methods, and there was no method formally available to tackle multiscale problems involving uncertainties. By developing an innovative Multiscale Stochastic Finite Element Method (MSFEM), this research has made a ground-breaking contribution to the emerging field of Multiscale Stochastic Modeling (MSM) (Fig 1). The theory of MSFEM basically decomposes a boundary value problem of random microstructure into a slow scale deterministic problem and a fast scale stochastic one. The slow scale problem corresponds to common engineering modeling practices where fine-scale microstructure is approximated by certain effective constitutive constants, which can be solved by using standard numerical solvers. The fast scale problem evaluates fluctuations of local quantities due to random microstructure, which is important for scale-coupling systems and particularly those involving failure mechanisms. The Green-function-based fast-scale solver developed in this research overcomes the curse-of-dimensionality commonly met in conventional approaches, by proposing a random field-based orthogonal expansion approach. The MSFEM formulated in this project paves the way to deliver the first computational tool/software on uncertainty quantification of multiscale systems. The applications of MSFEM on engineering problems will directly enhance our modeling capability on materials science (composite materials, nanostructures), geophysics (porous media, earthquake), biological systems (biological tissues, bones, protein folding). Continuous development of MSFEM will

  14. Forward-backward Stochastic Differential Equations and Backward Linear Quadratic Stochastic Optimal Control Problem

    ZHANG DE-TAO

    2009-01-01

    In this paper, we use the solutions of forward-backward stochastic differential equations to get the optimal control for backward stochastic linear quadratic optimal control problem. And we also give the linear feedback regulator for the optimal control problem by using the solutions of a group of Riccati equations.

  15. Stochastic Chaos with Its Control and Synchronization

    Zhang Ying; Xu Wei; Zhang Tianshu; Yang Xiaoli; Wu Cunli; Fang Tong

    2008-01-01

    The discovery of chaos in the sixties of last century was a breakthrough in concept,revealing the truth that some disorder behavior, called chaos, could happen even in a deterministic nonlinear system under barely deterministic disturbance. After a series of serious studies, people begin to acknowledge that chaos is a specific type of steady state motion other than the conventional periodic and quasi-periodic ones, featuring a sensitive dependence on initial conditions, resulting from the intrinsic randomness of a nonlinear system itself. In fact, chaos is a collective phenomenon consisting of massive individual chaotic responses, corresponding to different initial conditions in phase space. Any two adjacent individual chaotic responses repel each other, thus causing not only the sensitive dependence on initial conditions but also the existence of at least one positive top Lyapunov exponent (TLE) for chaos. Meanwhile, all the sample responses share one common invariant set on the Poincaré map, called chaotic attractor,which every sample response visits from time to time ergodically. So far, the existence of at least one positive TLE is a commonly acknowledged remarkable feature of chaos. We know that there are various forms of uncertainties in the real world. In theoretical studies, people often use stochastic models to describe these uncertainties, such as random variables or random processes.Systems with random variables as their parameters or with random processes as their excitations are often called stochastic systems. No doubt, chaotic phenomena also exist in stochastic systems, which we call stochastic chaos to distinguish it from deterministic chaos in the deterministic system. Stochastic chaos reflects not only the intrinsic randomness of the nonlinear system but also the external random effects of the random parameter or the random excitation.Hence, stochastic chaos is also a collective massive phenomenon, corresponding not only to different initial

  16. Calculation of the incidence of stochastic health effects in irradiated populations

    A procedure for the estimation of the numbers of stochastic health effects in irradiated populations is described. Three broad categories of health effects have been defined, which are summarised as fatal cancers, non-fatal cancers, and serious hereditary defects. The paper quantifies the risk coefficients and the time distributions of those risks for each identified category. These data are needed for assessments of both accidental and routine releases of radioactive materials to the environment. No attempt is made to ascribe factors to the relative importance of the three categories of health effect although this is clearly of significance when applying the results for decision-making or other purposes. (U.K.)

  17. Random musings on stochastics (Lorenz Lecture)

    Koutsoyiannis, D.

    2014-12-01

    In 1960 Lorenz identified the chaotic nature of atmospheric dynamics, thus highlighting the importance of the discovery of chaos by Poincare, 70 years earlier, in the motion of three bodies. Chaos in the macroscopic world offered a natural way to explain unpredictability, that is, randomness. Concurrently with Poincare's discovery, Boltzmann introduced statistical physics, while soon after Borel and Lebesgue laid the foundation of measure theory, later (in 1930s) used by Kolmogorov as the formal foundation of probability theory. Subsequently, Kolmogorov and Khinchin introduced the concepts of stochastic processes and stationarity, and advanced the concept of ergodicity. All these areas are now collectively described by the term "stochastics", which includes probability theory, stochastic processes and statistics. As paradoxical as it may seem, stochastics offers the tools to deal with chaos, even if it results from deterministic dynamics. As chaos entails uncertainty, it is more informative and effective to replace the study of exact system trajectories with that of probability densities. Also, as the exact laws of complex systems can hardly be deduced by synthesis of the detailed interactions of system components, these laws should inevitably be inferred by induction, based on observational data and using statistics. The arithmetic of stochastics is quite different from that of regular numbers. Accordingly, it needs the development of intuition and interpretations which differ from those built upon deterministic considerations. Using stochastic tools in a deterministic context may result in mistaken conclusions. In an attempt to contribute to a more correct interpretation and use of stochastic concepts in typical tasks of nonlinear systems, several examples are studied, which aim (a) to clarify the difference in the meaning of linearity in deterministic and stochastic context; (b) to contribute to a more attentive use of stochastic concepts (entropy, statistical

  18. Stochastic modeling of the auroral electrojet index

    Anh, V. V.; Yong, J. M.; Yu, Z. G.

    2008-10-01

    Substorms are often identified by bursts of activities in the magnetosphere-ionosphere system characterized by the auroral electrojet (AE) index. The highly complex nature of substorm-related bursts suggests that a stochastic approach would be needed. Stochastic models including fractional Brownian motion, linear fractional stable motion, Fokker-Planck equation and Itô-type stochastic differential equation have been suggested to model the AE index. This paper provides a stochastic model for the AE in the form of fractional stochastic differential equation. The long memory of the AE time series is represented by a fractional derivative, while its bursty behavior is modeled by a Lévy noise with inverse Gaussian marginal distribution. The equation has the form of the classical Stokes-Boussinesq-Basset equation of motion for a spherical particle in a fluid with retarded viscosity. Parameter estimation and approximation schemes are detailed for the simulation of the equation. The fractional order of the equation conforms with the previous finding that the fluctuations of the magnetosphere-ionosphere system as seen in the AE reflect the fluctuations in the solar wind: they both possess the same extent of long-range dependence. The introduction of a fractional derivative term into the equation to capture the extent of long-range dependence together with an inverse Gaussian noise input describe the right amount of intermittency inherent in the AE data.

  19. Memristors Empower Spiking Neurons With Stochasticity

    Al-Shedivat, Maruan

    2015-06-01

    Recent theoretical studies have shown that probabilistic spiking can be interpreted as learning and inference in cortical microcircuits. This interpretation creates new opportunities for building neuromorphic systems driven by probabilistic learning algorithms. However, such systems must have two crucial features: 1) the neurons should follow a specific behavioral model, and 2) stochastic spiking should be implemented efficiently for it to be scalable. This paper proposes a memristor-based stochastically spiking neuron that fulfills these requirements. First, the analytical model of the memristor is enhanced so it can capture the behavioral stochasticity consistent with experimentally observed phenomena. The switching behavior of the memristor model is demonstrated to be akin to the firing of the stochastic spike response neuron model, the primary building block for probabilistic algorithms in spiking neural networks. Furthermore, the paper proposes a neural soma circuit that utilizes the intrinsic nondeterminism of memristive switching for efficient spike generation. The simulations and analysis of the behavior of a single stochastic neuron and a winner-take-all network built of such neurons and trained on handwritten digits confirm that the circuit can be used for building probabilistic sampling and pattern adaptation machinery in spiking networks. The findings constitute an important step towards scalable and efficient probabilistic neuromorphic platforms. © 2011 IEEE.

  20. Stochastic growth of localized plasma waves

    Localized bursty plasma waves are detected by spacecraft in many space plasmas. The large spatiotemporal scales involved imply that beam and other instabilities relax to marginal stability and that mean wave energies are low. Stochastic wave growth occurs when ambient fluctuations perturb the system, causing fluctuations about marginal stability. This yields regions where growth is enhanced and others where damping is increased; bursts are associated with enhanced growth and can occur even when the mean growth rate is negative. In stochastic growth, energy loss from the source is suppressed relative to secular growth, preserving it far longer than otherwise possible. Linear stochastic growth can operate at wave levels below thresholds of nonlinear wave-clumping mechanisms such as strong-turbulence modulational instability and is not subject to their coherence and wavelength limits. These mechanisms can be distinguished by statistics of the fields, whose strengths are lognormally distributed if stochastically growing and power-law distributed in strong turbulence. Recent applications of stochastic growth theory (SGT) are described, involving bursty plasma waves and unstable particle distributions in type III solar radio sources, the Earth's foreshock, magnetosheath, and polar cap regions. It is shown that when combined with wave-wave processes, SGT also accounts for associated radio emissions

  1. Non-Markovian stochastic evolution equations

    Costanza, G.

    2014-05-01

    Non-Markovian continuum stochastic and deterministic equations are derived from a set of discrete stochastic and deterministic evolution equations. Examples are given of discrete evolution equations whose updating rules depend on two or more previous time steps. Among them, the continuum stochastic evolution equation of the Newton second law, the stochastic evolution equation of a wave equation, the stochastic evolution equation for the scalar meson field, etc. are obtained as special cases. Extension to systems of evolution equations and other extensions are considered and examples are given. The concept of isomorphism and almost isomorphism are introduced in order to compare the coefficients of the continuum evolution equations of two different smoothing procedures that arise from two different approaches. Usually these discrepancies arising from two sources: On the one hand, the use of different representations of the generalized functions appearing in the models and, on the other hand, the different approaches used to describe the models. These new concept allows to overcome controversies that were appearing during decades in the literature.

  2. Discrete analysis of stochastic NMR.II

    Wong, S. T. S.; Rods, M. S.; Newmark, R. D.; Budinger, T. F.

    Stochastic NMR is an efficient technique for high-field in vivo imaging and spectroscopic studies where the peak RF power required may be prohibitively high for conventional pulsed NMR techniques. A stochastic NMR experiment excites the spin system with a sequence of RF pulses where the flip angles or the phases of the pulses are samples of a discrete stochastic process. In a previous paper the stochastic experiment was analyzed and analytic expressions for the input-output cross-correlations, average signal power, and signal spectral density were obtained for a general stochastic RF excitation. In this paper specific cases of excitation with random phase, fixed flip angle, and excitation with two random components in quadrature are analyzed. The input-output cross-correlation for these two types of excitations is shown to be Lorentzian. Line broadening is the only spectral distortion as the RF excitation power is increased. The systematic noise power is inversely proportional to the number of data points N used in the spectral reconstruction. The use of a complete maximum length sequence (MLS) may improve the signal-to-systematic-noise ratio by 20 dB relative to random binary excitation, but peculiar features in the higher-order autocorrelations of MLS cause noise-like distortion in the reconstructed spectra when the excitation power is high. The amount of noise-like distortion depends on the choice of the MLS generator.

  3. Computational stochastic model of ions implantation

    Zmievskaya, Galina I., E-mail: zmi@gmail.ru; Bondareva, Anna L., E-mail: bal310775@yandex.ru [M.V. Keldysh Institute of Applied Mathematics RAS, 4,Miusskaya sq., 125047 Moscow (Russian Federation); Levchenko, Tatiana V., E-mail: tatlevchenko@mail.ru [VNII Geosystem Russian Federal Center, Varshavskoye roadway, 8, Moscow (Russian Federation); Maino, Giuseppe, E-mail: giuseppe.maino@enea.it [Scuola di Lettere e BeniCulturali, University di Bologna, sede di Ravenna, via Mariani 5, 48100 Ravenna (Italy)

    2015-03-10

    Implantation flux ions into crystal leads to phase transition /PT/ 1-st kind. Damaging lattice is associated with processes clustering vacancies and gaseous bubbles as well their brownian motion. System of stochastic differential equations /SDEs/ Ito for evolution stochastic dynamical variables corresponds to the superposition Wiener processes. The kinetic equations in partial derivatives /KE/, Kolmogorov-Feller and Einstein-Smolukhovskii, were formulated for nucleation into lattice of weakly soluble gases. According theory, coefficients of stochastic and kinetic equations uniquely related. Radiation stimulated phase transition are characterized by kinetic distribution functions /DFs/ of implanted clusters versus their sizes and depth of gas penetration into lattice. Macroscopic parameters of kinetics such as the porosity and stress calculated in thin layers metal/dielectric due to Xe{sup ++} irradiation are attracted as example. Predictions of porosity, important for validation accumulation stresses in surfaces, can be applied at restoring of objects the cultural heritage.

  4. Markovian stochastic approximation with expanding projections

    Andrieu, Christophe

    2011-01-01

    Stochastic approximation is a framework unifying many random iterative algorithms occurring in a diverse range of applications. The stability of the process is often difficult to verify in practical applications and the process may even be unstable without additional stabilisation techniques. We study a stochastic approximation procedure with expanding projections similar to Andrad\\'ottir [Oper. Res. 43 (2010) 1037--1048]. We focus on Markovian noise and show the stability and convergence under general conditions. Our framework also incorporates the possibility to use a random step size sequence, which allows us to consider settings with a non-smooth family of Markov kernels. We apply the theory to stochastic approximation expectation maximisation with particle independent Metropolis-Hastings sampling.

  5. Lectures on Topics in Spatial Stochastic Processes

    Capasso, Vincenzo; Ivanoff, B Gail; Dozzi, Marco; Dalang, Robert C; Mountford, Thomas S

    2003-01-01

    The theory of stochastic processes indexed by a partially ordered set has been the subject of much research over the past twenty years. The objective of this CIME International Summer School was to bring to a large audience of young probabilists the general theory of spatial processes, including the theory of set-indexed martingales and to present the different branches of applications of this theory, including stochastic geometry, spatial statistics, empirical processes, spatial estimators and survival analysis. This theory has a broad variety of applications in environmental sciences, social sciences, structure of material and image analysis. In this volume, the reader will find different approaches which foster the development of tools to modelling the spatial aspects of stochastic problems.

  6. Stochastic stability of a rotating shaft

    Pavlovic, Ratko; Kozic, Predrag; Mitic, Snezana; Pavlovic, Ivan [University of Nis, Mechanical Engineering Faculty, Nis (RS)

    2009-12-15

    The stochastic stability problem of an elastic, balanced rotating shaft subjected to action of axial forces at the ends is studied. The shaft is of circular cross-section, it rotates at a constant rate about its longitudinal axis of symmetry. The effect of rotatory inertia of the shaft cross-section is included in the present formulation. Each force consists of a constant part and a time-dependent stochastic function. Closed form analytical solutions are obtained for simply supported boundary conditions. By using the direct Liapunov method almost sure asymptotic stability conditions are obtained as the function of stochastic process variance, damping coefficient, damping ratio, angular velocity, mode number and geometric and physical parameters of the shaft. Numerical calculations are performed for the Gaussian process with a zero mean and as well as an harmonic process with random phase. (orig.)

  7. Computational stochastic model of ions implantation

    Implantation flux ions into crystal leads to phase transition /PT/ 1-st kind. Damaging lattice is associated with processes clustering vacancies and gaseous bubbles as well their brownian motion. System of stochastic differential equations /SDEs/ Ito for evolution stochastic dynamical variables corresponds to the superposition Wiener processes. The kinetic equations in partial derivatives /KE/, Kolmogorov-Feller and Einstein-Smolukhovskii, were formulated for nucleation into lattice of weakly soluble gases. According theory, coefficients of stochastic and kinetic equations uniquely related. Radiation stimulated phase transition are characterized by kinetic distribution functions /DFs/ of implanted clusters versus their sizes and depth of gas penetration into lattice. Macroscopic parameters of kinetics such as the porosity and stress calculated in thin layers metal/dielectric due to Xe++ irradiation are attracted as example. Predictions of porosity, important for validation accumulation stresses in surfaces, can be applied at restoring of objects the cultural heritage

  8. A closure for stochastic transport equations

    The problem of particle transport through a stochastic mixture of two immiscible materials is considered. The material mixing process is assumed to obey Markovian statistics. An ensemble average of this stochastic transport equations leads to two equations containing four different ensemble-averaged intensities. To close these equations to a set of two equations in two unknowns, certain rod geometry problems are considered. In this geometry, two distinct exact analyses are possible, namely a small correlation length analysis, and a non stochastic mean number of secondaries per collision analysis. The closure philosophy is to demand that the closed set of two equations reproduces these exact limiting behaviors. Numerical results are given which compare the predictions of this new closure with exact benchmark results as well as with the standard closure available in literature. (authors). 12 refs., 2 figs

  9. Stochastic approach to scientific development and innovations

    Eto, H. (The University of Tokyo, Tokyo (Japan))

    1992-03-19

    Scientific development and technological innovations were studied by stochastic methods, in particular, scientometric method. The Bradford law was discussed which describes the behavior of scientific and engineering documents in the view point of their development and innovations. It was found that scientific development and innovations were governed more strongly by cumulative advantage effect and selfmultiplicative or self-reproductive effect than natural development and economic innovations. The elite characteristics were also pointed out by which a relatively small number of eminent scientific resources was selected in order to go ahead the others. These stochastic efforts could not fully explain peculiar characteristics of the Bradford law, and this inability probably indicated the essential gap between natural or economic science and bibliometrics. In addition, several attempts in stochastic theories and several possible trends of scientific activities in various scientific fields and in developing countries were discussed which might dissolve above gap. 23 refs., 8 figs., 24 tabs.

  10. Online Stochastic Ad Allocation: Efficiency and Fairness

    Feldman, Jon; Korula, Nitish; Mirrokni, Vahab S; Stein, Cliff

    2010-01-01

    We study the efficiency and fairness of online stochastic display ad allocation algorithms from a theoretical and practical standpoint. In particular, we study the problem of maximizing efficiency in the presence of stochastic information. In this setting, each advertiser has a maximum demand for impressions of display ads that will arrive online. In our model, inspired by the concept of free disposal in economics, we assume that impressions that are given to an advertiser above her demand are given to her for free. Our main theoretical result is to present a training-based algorithm that achieves a (1-\\epsilon)-approximation guarantee in the random order stochastic model. In the corresponding online matching problem, we learn a dual variable for each advertiser, based on data obtained from a sample of impressions. We also discuss different fairness measures in online ad allocation, based on comparison to an ideal offline fair solution, and develop algorithms to compute "fair" allocations. We then discuss sev...

  11. Modeling stochasticity in biochemical reaction networks

    Constantino, P. H.; Vlysidis, M.; Smadbeck, P.; Kaznessis, Y. N.

    2016-03-01

    Small biomolecular systems are inherently stochastic. Indeed, fluctuations of molecular species are substantial in living organisms and may result in significant variation in cellular phenotypes. The chemical master equation (CME) is the most detailed mathematical model that can describe stochastic behaviors. However, because of its complexity the CME has been solved for only few, very small reaction networks. As a result, the contribution of CME-based approaches to biology has been very limited. In this review we discuss the approach of solving CME by a set of differential equations of probability moments, called moment equations. We present different approaches to produce and to solve these equations, emphasizing the use of factorial moments and the zero information entropy closure scheme. We also provide information on the stability analysis of stochastic systems. Finally, we speculate on the utility of CME-based modeling formalisms, especially in the context of synthetic biology efforts.

  12. Stochastic Modelling Of The Repairable System

    Andrzejczak Karol

    2015-11-01

    Full Text Available All reliability models consisting of random time factors form stochastic processes. In this paper we recall the definitions of the most common point processes which are used for modelling of repairable systems. Particularly this paper presents stochastic processes as examples of reliability systems for the support of the maintenance related decisions. We consider the simplest one-unit system with a negligible repair or replacement time, i.e., the unit is operating and is repaired or replaced at failure, where the time required for repair and replacement is negligible. When the repair or replacement is completed, the unit becomes as good as new and resumes operation. The stochastic modelling of recoverable systems constitutes an excellent method of supporting maintenance related decision-making processes and enables their more rational use.

  13. Stochastic simulation of supercritical fluid extraction processes

    F. T. Mizutani

    2000-09-01

    Full Text Available Process simulation involves the evaluation of output variables by the specification of input variables and process parameters. However, in a real process, input data and parameters cannot be known without uncertainty. This fact may limit the utilization of simulation results to predict plant behavior. In order to achieve a more realistic analysis, the procedure of stochastic simulation can be conducted. This technique is based on a large set of simulation runs where input variables and parameters are randomly selected according to adequate probability density functions. The objective of this work is to illustrate the application of a stochastic simulation procedure to the process of fractionation of orange essential oil, using supercritical carbon dioxide in a multistage extraction column. Analysis of the proposed example demonstrates the importance of the stochastic simulation to develop more reliable designs and operating conditions for a supercritical fluid extraction process.

  14. Stochastic analysis for finance with simulations

    Choe, Geon Ho

    2016-01-01

    This book is an introduction to stochastic analysis and quantitative finance; it includes both theoretical and computational methods. Topics covered are stochastic calculus, option pricing, optimal portfolio investment, and interest rate models. Also included are simulations of stochastic phenomena, numerical solutions of the Black–Scholes–Merton equation, Monte Carlo methods, and time series. Basic measure theory is used as a tool to describe probabilistic phenomena. The level of familiarity with computer programming is kept to a minimum. To make the book accessible to a wider audience, some background mathematical facts are included in the first part of the book and also in the appendices. This work attempts to bridge the gap between mathematics and finance by using diagrams, graphs and simulations in addition to rigorous theoretical exposition. Simulations are not only used as the computational method in quantitative finance, but they can also facilitate an intuitive and deeper understanding of theoret...

  15. Stochastic approach to equilibrium and nonequilibrium thermodynamics.

    Tomé, Tânia; de Oliveira, Mário J

    2015-04-01

    We develop the stochastic approach to thermodynamics based on stochastic dynamics, which can be discrete (master equation) and continuous (Fokker-Planck equation), and on two assumptions concerning entropy. The first is the definition of entropy itself and the second the definition of entropy production rate, which is non-negative and vanishes in thermodynamic equilibrium. Based on these assumptions, we study interacting systems with many degrees of freedom in equilibrium or out of thermodynamic equilibrium and how the macroscopic laws are derived from the stochastic dynamics. These studies include the quasiequilibrium processes; the convexity of the equilibrium surface; the monotonic time behavior of thermodynamic potentials, including entropy; the bilinear form of the entropy production rate; the Onsager coefficients and reciprocal relations; and the nonequilibrium steady states of chemical reactions. PMID:25974471

  16. Globally coupled chaotic maps and demographic stochasticity

    Kessler, David A.; Shnerb, Nadav M.

    2010-03-01

    The effect of noise on a system of globally coupled chaotic maps is considered. Demographic stochasticity is studied since it provides both noise and a natural definition for extinction. A two-step model is presented, where the intrapatch chaotic dynamics is followed by a migration step with global dispersal. The addition of noise to the already chaotic system is shown to dramatically change its behavior. The level of migration in which the system attains maximal sustainability is identified. This determines the optimal way to manipulate a fragmented habitat in order to conserve endangered species. The quasideterministic dynamics that appears in the large N limit of the stochastic system is analyzed. In the clustering phase, the infinite degeneracy of deterministic solutions emerges from the single steady state of the stochastic system via a mechanism that involves an almost defective Markov matrix.

  17. Stochastic resonance in geomagnetic polarity reversals.

    Consolini, Giuseppe; De Michelis, Paola

    2003-02-01

    Among noise-induced cooperative phenomena a peculiar relevance is played by stochastic resonance. In this paper we offer evidence that geomagnetic polarity reversals may be due to a stochastic resonance process. In detail, analyzing the distribution function P(tau) of polarity residence times (chrons), we found the evidence of a stochastic synchronization process, i.e., a series of peaks in the P(tau) at T(n) approximately (2n+1)T(Omega)/2 with n=0,1,...,j and T(omega) approximately 0.1 Myr. This result is discussed in connection with both the typical time scale of Earth's orbit eccentricity variation and the recent results on the typical time scale of climatic long-term variation. PMID:12633403

  18. Recursive Stochastic Effects in Valley Hybrid Inflation

    Levasseur, Laurence Perreault; Brandenberger, Robert

    2013-01-01

    Hybrid Inflation is a two-field model where inflation ends by a tachyonic instability, the duration of which is determined by stochastic effects and has important observational implications. Making use of the recursive approach to the stochastic formalism presented in Ref. [1], these effects are consistently computed. Through an analysis of back-reaction, this method is shown to converge in the valley but points toward an (expected) instability in the waterfall. It is further shown that quasi-stationarity of the auxiliary field distribution breaks down in the case of a short-lived waterfall. It is found that the typical dispersion of the waterfall field at the critical point is then diminished, thus increasing the duration of the waterfall phase and jeopardizing the possibility of a short transition. Finally, it is found that stochastic effects worsen the blue tilt of the curvature perturbations by an order one factor when compared with the usual slow-roll contribution.

  19. Collision probabilities in spatially stochastic media II

    An improved model for calculating collision probabilities in spatially stochastic media is described based upon a method developed by Cassell and Williams [Cassell, J.S., Williams, M.M.R., in press. An approximate method for solving radiation and neutron transport problems in spatially stochastic media. Annals of Nuclear Energy] and is applicable to three-dimensional problems. We shall show how to evaluate the collision probability in an arbitrarily shaped non-re-entrant lump, consisting of a random dispersal of two phases, for any form of autocorrelation function. Specific examples, with numerical values, are given for a sphere and a slab. In the case of the slab we allow the material to have different stochastic properties in the x, y and z directions

  20. Online Advertisement, Optimization and Stochastic Networks

    Bo,; Srikant, R

    2010-01-01

    In this paper, we propose a stochastic model to describe how modern search service providers charge client companies based on users' queries for their related "adwords" by using certain advertisement assignment strategies. We formulate an optimization problem to maximize the long-term average revenue for the service provider under each client's long-term average budget constraint, and design an online algorithm which captures the stochastic properties of users' queries and click-through behaviors. We solve the optimization problem by making connections to scheduling problems in wireless networks, queueing theory and stochastic networks. With a small customizable parameter $\\epsilon$ which is the step size used in each iteration of the online algorithm, we have shown that our online algorithm achieves a long-term average revenue which is within $O(\\epsilon)$ of the optimal revenue and the overdraft level of this algorithm is upper-bounded by $O(1/\\epsilon)$.

  1. SBAT. A stochastic BPMN analysis tool

    Herbert, Luke Thomas; Hansen, Zaza Nadja Lee; Jacobsen, Peter

    2014-01-01

    This paper presents SBAT, a tool framework for the modelling and analysis of complex business workflows. SBAT is applied to analyse an example from the Danish baked goods industry. Based upon the Business Process Modelling and Notation (BPMN) language for business process modelling, we describe...... a formalised variant of this language extended to support the addition of intention preserving stochastic branching and parameterised reward annotations. Building on previous work, we detail the design of SBAT, a software tool which allows for the analysis of BPMN models. Within SBAT, properties of interest...... are specified using the temporal logic Probabilistic Computation Tree Logic (PCTL) and we employ stochastic model checking, by means of the model checker PRISM, to compute their exact values. We present a simplified example of a distributed stochastic system where we determine a reachability property...

  2. Linear transport in correlated stochastic media

    The problem of linear transport in a stationary stochastic medium is examined in the context of stochastic geometry. Boolean models of stochastic media allow calculation of density correlations without use of Markovian assumptions. Most correlation functions are well represented by linear combinations of a few exponentials. Systems of integrodifferential equations are obtained either (a) by a perturbative treatment or (b) by truncation of the hierarchy of moments. The presence of an integral term (i.e., a nonlocal flux) can be avoided by the use of an approximate equivalence between the product of the transport Green function by an exponential with the transport Green function of a modified problem. Introduction of auxiliary unknowns gives rise to a system of coupled Boltzmann equations describing the ensemble average of the flux

  3. Stochastic partial differential equations an introduction

    Liu, Wei

    2015-01-01

    This book provides an introduction to the theory of stochastic partial differential equations (SPDEs) of evolutionary type. SPDEs are one of the main research directions in probability theory with several wide ranging applications. Many types of dynamics with stochastic influence in nature or man-made complex systems can be modelled by such equations. The theory of SPDEs is based both on the theory of deterministic partial differential equations, as well as on modern stochastic analysis. Whilst this volume mainly follows the ‘variational approach’, it also contains a short account on the ‘semigroup (or mild solution) approach’. In particular, the volume contains a complete presentation of the main existence and uniqueness results in the case of locally monotone coefficients. Various types of generalized coercivity conditions are shown to guarantee non-explosion, but also a systematic approach to treat SPDEs with explosion in finite time is developed. It is, so far, the only book where the latter and t...

  4. A Compositional Semantics for Stochastic Reo Connectors

    Moon, Young-Joo; Krause, Christian; Arbab, Farhad; 10.4204/EPTCS.30.7

    2010-01-01

    In this paper we present a compositional semantics for the channel-based coordination language Reo which enables the analysis of quality of service (QoS) properties of service compositions. For this purpose, we annotate Reo channels with stochastic delay rates and explicitly model data-arrival rates at the boundary of a connector, to capture its interaction with the services that comprise its environment. We propose Stochastic Reo automata as an extension of Reo automata, in order to compositionally derive a QoS-aware semantics for Reo. We further present a translation of Stochastic Reo automata to Continuous-Time Markov Chains (CTMCs). This translation enables us to use third-party CTMC verification tools to do an end-to-end performance analysis of service compositions.

  5. Weather Derivatives and Stochastic Modelling of Temperature

    Fred Espen Benth

    2011-01-01

    Full Text Available We propose a continuous-time autoregressive model for the temperature dynamics with volatility being the product of a seasonal function and a stochastic process. We use the Barndorff-Nielsen and Shephard model for the stochastic volatility. The proposed temperature dynamics is flexible enough to model temperature data accurately, and at the same time being analytically tractable. Futures prices for commonly traded contracts at the Chicago Mercantile Exchange on indices like cooling- and heating-degree days and cumulative average temperatures are computed, as well as option prices on them.

  6. On orthogonality preserving quadratic stochastic operators

    Mukhamedov, Farrukh; Taha, Muhammad Hafizuddin Mohd [Department of Computational and Theoretical Sciences, Faculty of Science International Islamic University Malaysia, P.O. Box 141, 25710 Kuantan, Pahang Malaysia (Malaysia)

    2015-05-15

    A quadratic stochastic operator (in short QSO) is usually used to present the time evolution of differing species in biology. Some quadratic stochastic operators have been studied by Lotka and Volterra. In the present paper, we first give a simple characterization of Volterra QSO in terms of absolutely continuity of discrete measures. Further, we introduce a notion of orthogonal preserving QSO, and describe such kind of operators defined on two dimensional simplex. It turns out that orthogonal preserving QSOs are permutations of Volterra QSO. The associativity of genetic algebras generated by orthogonal preserving QSO is studied too.

  7. Binomial moment equations for stochastic reaction systems.

    Barzel, Baruch; Biham, Ofer

    2011-04-15

    A highly efficient formulation of moment equations for stochastic reaction networks is introduced. It is based on a set of binomial moments that capture the combinatorics of the reaction processes. The resulting set of equations can be easily truncated to include moments up to any desired order. The number of equations is dramatically reduced compared to the master equation. This formulation enables the simulation of complex reaction networks, involving a large number of reactive species much beyond the feasibility limit of any existing method. It provides an equation-based paradigm to the analysis of stochastic networks, complementing the commonly used Monte Carlo simulations. PMID:21568538

  8. Thin and heavy tails in stochastic programming

    Kaňková, Vlasta; Houda, Michal

    2015-01-01

    Roč. 51, č. 3 (2015), s. 433-456. ISSN 0023-5954 R&D Projects: GA ČR GA13-14445S Institutional support: RVO:67985556 Keywords : stochastic programming problems * stability * Wasserstein metric * L1 norm * Lipschitz property * empirical estimates * convergence rate * linear and nonlinear dependence * probability and risk constraints * stochastic dominance Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 0.541, year: 2014 http://library.utia.cas.cz/separaty/2015/E/kankova-0447994.pdf

  9. Sensitivity Study of Stochastic Walking Load Models

    Pedersen, Lars; Frier, Christian

    2010-01-01

    serviceability limit state is assessed using a walking load model in which the walking parameters are modelled deterministically. However, the walking parameters are stochastic (for instance the weight of the pedestrian is not likely to be the same for every footbridge crossing), and a natural way forward is to...... employ a stochastic load model accounting for mean values and standard deviations for the walking load parameters, and to use this as a basis for estimation of structural response. This, however, requires decisions to be made in terms of statistical istributions and their parameters, and the paper...

  10. Pricing foreign equity option with stochastic volatility

    Sun, Qi; Xu, Weidong

    2015-11-01

    In this paper we propose a general foreign equity option pricing framework that unifies the vast foreign equity option pricing literature and incorporates the stochastic volatility into foreign equity option pricing. Under our framework, the time-changed Lévy processes are used to model the underlying assets price of foreign equity option and the closed form pricing formula is obtained through the use of characteristic function methodology. Numerical tests indicate that stochastic volatility has a dramatic effect on the foreign equity option prices.

  11. Stochastic 2-D Navier-Stokes Equation

    In this paper we prove the existence and uniqueness of strong solutions for the stochastic Navier-Stokes equation in bounded and unbounded domains. These solutions are stochastic analogs of the classical Lions-Prodi solutions to the deterministic Navier-Stokes equation. Local monotonicity of the nonlinearity is exploited to obtain the solutions in a given probability space and this significantly improves the earlier techniques for obtaining strong solutions, which depended on pathwise solutions to the Navier-Stokes martingale problem where the probability space is also obtained as a part of the solution

  12. Stochastic control optimal in the Kullback sense

    Šindelář, Jan; Vajda, Igor; Kárný, Miroslav

    2008-01-01

    Roč. 44, č. 1 (2008), s. 53-60. ISSN 0023-5954 R&D Projects: GA AV ČR(CZ) IAA200750802; GA MŠk 2C06001; GA AV ČR 1ET100750401 Institutional research plan: CEZ:AV0Z10750506 Keywords : Kullback divergence * Minimization * Stochastic control ler Subject RIV: BC - Control Systems Theory Impact factor: 0.281, year: 2008 http://library.utia.cas.cz/separaty/2008/SI/sindelar-stochastic control optimal in the kullback sense.pdf

  13. Level Crossing Methods in Stochastic Models

    Brill, Percy H

    2008-01-01

    Since its inception in 1974, the level crossing approach for analyzing a large class of stochastic models has become increasingly popular among researchers. This volume traces the evolution of level crossing theory for obtaining probability distributions of state variables and demonstrates solution methods in a variety of stochastic models including: queues, inventories, dams, renewal models, counter models, pharmacokinetics, and the natural sciences. Results for both steady-state and transient distributions are given, and numerous examples help the reader apply the method to solve problems fa

  14. Stochastic stability and instability of model ecosystems

    Ladde, G. S.; Siljak, D. D.

    1975-01-01

    In this work, we initiate a stability study of multispecies communities in stochastic environment by using Ito's differential equations as community models. By applying the direct method of Liapunov, we obtain sufficient conditions for stability and instability in the mean of the equilibrium populations. The conditions are expressed in terms of the dominant diagonal property of community matrices, which is a suitable mechanism for resolving the central problem of 'complexity vs stability' in model ecosystems. As a by-product of this analysis we exhibit important structural properties of the stochastic density-dependent models, and establish tolerance of community stability to a broad class of nonlinear time-varying perturbations.

  15. A Stochastic Measure for Eternal Inflation

    Li, Miao; Wang, Yi

    2007-01-01

    We use the stochastic approach to investigate the measure for slow roll eternal inflation. The probability for the universe of a given Hubble radius can be calculated in this framework. In a solvable model, it is shown that the probability for the universe to evolve from a state with a smaller Hubble radius to that of a larger Hubble radius is dominated by the classical probability without the stochastic source. While the probability for the universe to evolve from a larger Hubble radius to a...

  16. Stochastics introduction to probability and statistics

    Georgii, Hans-Otto

    2012-01-01

    This second revised and extended edition presents the fundamental ideas and results of both, probability theory and statistics, and comprises the material of a one-year course. It is addressed to students with an interest in the mathematical side of stochastics. Stochastic concepts, models and methods are motivated by examples and developed and analysed systematically. Some measure theory is included, but this is done at an elementary level that is in accordance with the introductory character of the book. A large number of problems offer applications and supplements to the text.

  17. Time Series Forecasting: A Multivariate Stochastic Approach

    Sello, Stefano

    1999-01-01

    This note deals with a multivariate stochastic approach to forecast the behaviour of a cyclic time series. Particular attention is devoted to the problem of the prediction of time behaviour of sunspot numbers for the current 23th cycle. The idea is to consider the previous known n cycles as n particular realizations of a given stochastic process. The aim is to predict the future behaviour of the current n+1th realization given a portion of the curve and the structure of the previous n realiza...

  18. The Separation Principle in Stochastic Control, Redux

    Georgiou, Tryphon T

    2011-01-01

    Over the last 50 years a steady stream of accounts have been written on the separation principle of stochastic control. Even in the context of the linear-quadratic regulator in continuous time with Gaussian white noise, subtle difficulties arise, unexpected by many, that are often overlooked. In this paper we provide a conceptual framework that clarifies pitfalls and possibilities. We also provide a generalizations of the separation theorem to a wide class of feedback laws, models and stochastic noise, including semimartingales with possible jumps.

  19. Stochastic models of oil spill processes

    This paper models the occurrence of an environmental accident as a stochastic event. In particular, the situation of an oil spill is explored. Characteristics of the ship operator, and the different types of the ship's operating environment determine a stochastic process governing the time patterns and size of spills. It is shown that both the time distribution of different types of oil spill and the distribution of spill size are affected by pollution control instruments such as fines, by enforcement effort, and by the alert level of the operating personnel. (Author)

  20. Monostable array-enhanced stochastic resonance.

    Lindner, J F; Breen, B J; Wills, M E; Bulsara, A R; Ditto, W L

    2001-05-01

    We present a simple nonlinear system that exhibits multiple distinct stochastic resonances. By adjusting the noise and coupling of an array of underdamped, monostable oscillators, we modify the array's natural frequencies so that the spectral response of a typical oscillator in an array of N oscillators exhibits N-1 different stochastic resonances. Such families of resonances may elucidate and facilitate a variety of noise-mediated cooperative phenomena, such as noise-enhanced propagation, in a broad class of similar nonlinear systems. PMID:11414887

  1. Kinetic effects in stochastic topologically nontrivial field

    The kinetic description of a particle system in an external stochastic field possessing helicity is examined. The kinetic equation is obtained, which contains an additional term proportional to the helicity. The solution describing evolution of the distribution function for arbitrary initial conditions is found. Particular examples of such an evolution and some new kinetic effects, connected with helicity, are discussed. It is shown, that a particle beam with inhomogeneous velocity profile in an external stochastic helical field generates a new particle flow in transverse direction. 13 refs. (author)

  2. Operation of Distributed Generation Under Stochastic Prices

    Siddiqui, Afzal S.; Marnay, Chris

    2005-11-30

    We model the operating decisions of a commercial enterprisethatneeds to satisfy its periodic electricity demand with either on-sitedistributed generation (DG) or purchases from the wholesale market. Whilethe former option involves electricity generation at relatively high andpossibly stochastic costs from a set of capacity-constrained DGtechnologies, the latter implies unlimited open-market transactions atstochastic prices. A stochastic dynamic programme (SDP) is used to solvethe resulting optimisation problem. By solving the SDP with and withoutthe availability of DG units, the implied option values of the DG unitsare obtained.

  3. Entangled solitons and stochastic Q-bits

    Stochastic realization of the wave function in quantum mechanics with the inclusion of soliton representation of extended particles is discussed. Two-solitons configurations are used for constructing entangled states in generalized quantum mechanics dealing with extended particles, endowed with nontrivial spin S. Entangled solitons construction being introduced in the nonlinear spinor field model, the Einstein-Podolsky-Rosen (EPR) correlation is calculated and shown to coincide with the quantum mechanical one for the 1/2-spin particles. The concept of stochastic q-bits is used for quantum computing modelling

  4. Communication nets stochastic message flow and delay

    Kleinrock, Leonard

    2007-01-01

    Considerable research has been devoted to the formulation and solution of problems involving flow within connected networks. Independent of these surveys, an extensive body of knowledge has accumulated on the subject of queues, particularly in regard to stochastic flow through single-node servicing facilities. This text combines studies of connected networks with those of stochastic flow, providing a basis for understanding the general behavior and operation of communication networks in realistic situations.Author Leonard Kleinrock of the Computer Science Department at UCLA created the basic p

  5. Stochastic Modeling of Traffic Air Pollution

    Thoft-Christensen, Palle

    2014-01-01

    In this paper, modeling of traffic air pollution is discussed with special reference to infrastructures. A number of subjects related to health effects of air pollution and the different types of pollutants are briefly presented. A simple model for estimating the social cost of traffic related air...... and using simple Monte Carlo techniques to obtain a stochastic estimate of the costs of traffic air pollution for infrastructures....... pollution is derived. Several authors have published papers on this very complicated subject, but no stochastic modelling procedure have obtained general acceptance. The subject is discussed basis of a deterministic model. However, it is straightforward to modify this model to include uncertain parameters...

  6. Path integrals for stochastic processes an introduction

    Wio, Horacio S

    2013-01-01

    This book provides an introductory albeit solid presentation of path integration techniques as applied to the field of stochastic processes. The subject began with the work of Wiener during the 1920's, corresponding to a sum over random trajectories, anticipating by two decades Feynman's famous work on the path integral representation of quantum mechanics. However, the true trigger for the application of these techniques within nonequilibrium statistical mechanics and stochastic processes was the work of Onsager and Machlup in the early 1950's. The last quarter of the 20th century has witnesse

  7. Stochastic processes in muon ionization cooling

    Errede, D.; Makino, K.; Berz, M.; Johnstone, C. J.; Van Ginneken, A.

    2004-02-01

    A muon ionization cooling channel consists of three major components: the magnet optics, an acceleration cavity, and an energy absorber. The absorber of liquid hydrogen contained by thin aluminum windows is the only component which introduces stochastic processes into the otherwise deterministic acceleration system. The scattering dynamics of the transverse coordinates is described by Gaussian distributions. The asymmetric energy loss function is represented by the Vavilov distribution characterized by the minimum number of collisions necessary for a particle undergoing loss of the energy distribution average resulting from the Bethe-Bloch formula. Examples of the interplay between stochastic processes and deterministic beam dynamics are given.

  8. Digital switching noise as a stochastic process

    Boselli, Giorgio; Trucco, Gabriella; Liberali, Valentino

    2007-06-01

    Switching activity of logic gates in a digital system is a deterministic process, depending on both circuit parameters and input signals. However, the huge number of logic blocks in a digital system makes digital switching a cognitively stochastic process. Switching activity is the source of the so-called "digital noise", which can be analyzed using a stochastic approach. For an asynchronous digital network, we can model digital switching currents as a shot noise process, deriving both its amplitude distribution and its power spectral density. From spectral distribution of digital currents, we can also calculate the spectral distribution and the power of disturbances injected into the on-chip power supply lines.

  9. Safety Analysis of Stochastic Dynamical Systems

    Sloth, Christoffer; Wisniewski, Rafael

    2015-01-01

    This paper presents a method for verifying the safety of a stochastic system. In particular, we show how to compute the largest set of initial conditions such that a given stochastic system is safe with probability p. To compute the set of initial conditions we rely on the moment method that via...... Haviland's theorem allows an infinite dimensional optimization problem on measures to be formulated as a polynomial optimization problem. Subsequently, the moment sequence is truncated (relaxed) to obtain a finite dimensional polynomial optimization problem. Finally, we provide an illustrative example that...

  10. A Jump-Diffusion Model with Stochastic Volatility and Durations

    Wei, Wei; Pelletier, Denis

    Market microstructure theories suggest that the durations between transactions carry information about volatility. This paper puts forward a model featuring stochastic volatility, stochastic conditional duration, and jumps to analyze high frequency returns and durations. Durations affect price...

  11. A Note on the Stochastic Nature of Feynman Quantum Paths

    L. Botelho, Luiz C.

    2016-06-01

    We propose a Fresnel stochastic white noise framework to analyze the stochastic nature of the Feynman paths entering on the Feynman Path Integral expression for the Feynman Propagator of a particle quantum mechanically moving under a time-independent potential.

  12. Absolute Value Boundedness, Operator Decomposition, and Stochastic Media and Equations

    Adomian, G.; Miao, C. C.

    1973-01-01

    The research accomplished during this period is reported. Published abstracts and technical reports are listed. Articles presented include: boundedness of absolute values of generalized Fourier coefficients, propagation in stochastic media, and stationary conditions for stochastic differential equations.

  13. Dimension Reduction and Discretization in Stochastic Problems by Regression Method

    Ditlevsen, Ove Dalager

    1996-01-01

    The chapter mainly deals with dimension reduction and field discretizations based directly on the concept of linear regression. Several examples of interesting applications in stochastic mechanics are also given.Keywords: Random fields discretization, Linear regression, Stochastic interpolation...

  14. Stochastic biomathematical models with applications to neuronal modeling

    Batzel, Jerry; Ditlevsen, Susanne

    2013-01-01

    Stochastic biomathematical models are becoming increasingly important as new light is shed on the role of noise in living systems. In certain biological systems, stochastic effects may even enhance a signal, thus providing a biological motivation for the noise observed in living systems. Recent advances in stochastic analysis and increasing computing power facilitate the analysis of more biophysically realistic models, and this book provides researchers in computational neuroscience and stochastic systems with an overview of recent developments. Key concepts are developed in chapters written by experts in their respective fields. Topics include: one-dimensional homogeneous diffusions and their boundary behavior, large deviation theory and its application in stochastic neurobiological models, a review of mathematical methods for stochastic neuronal integrate-and-fire models, stochastic partial differential equation models in neurobiology, and stochastic modeling of spreading cortical depression.

  15. Minimum uncertainty and squeezing in diffusion processes and stochastic quantization

    Demartino, S.; Desiena, S.; Illuminati, Fabrizo; Vitiello, Giuseppe

    1994-01-01

    We show that uncertainty relations, as well as minimum uncertainty coherent and squeezed states, are structural properties for diffusion processes. Through Nelson stochastic quantization we derive the stochastic image of the quantum mechanical coherent and squeezed states.

  16. Bilevel stochastic transportation problem with exponentially distributed demand

    Akdemir, Hande Günay; Tiryaki, Fatma

    2012-01-01

    In this paper, we consider a bilevel stochastic transportation problem (BSTP) which is a two level hierarchical program to determine optimal transportation plan assuming that customers’ demands are stochastic, in particular, exponentially distributed

  17. NONLINEAR STOCHASTIC DYNAMICS: A SURVEY OF RECENT DEVELOPMENTS

    朱位秋; 蔡国强

    2002-01-01

    This paper provides an overview of significant advances in nonlinearstochastic dynamics during the past two decades, including random response, stochas-tic stability, stochastic bifurcation, first passage problem and nonlinear stochasticcontrol. Topics for future research are also suggested.

  18. Assisting Whole-Farm Decision-Making through Stochastic Budgeting

    Lien, Gudbrand D.

    2002-01-01

    Stochastic budgeting is used to simulate the business and financial risk and the performance over a six-year planning horizon on a Norwegian dairy farm. A major difficulty with stochastic whole-farm budgeting lies in identifying and measuring dependency relationships between stochastic variables. Some methods to account for these stochastic dependencies are illustrated. The financial feasibility of different investment and management strategies is evaluated. In contrast with earlier studies w...

  19. Randomized Block Subgradient Methods for Convex Nonsmooth and Stochastic Optimization

    Deng, Qi; Lan, Guanghui; Rangarajan, Anand

    2015-01-01

    Block coordinate descent methods and stochastic subgradient methods have been extensively studied in optimization and machine learning. By combining randomized block sampling with stochastic subgradient methods based on dual averaging, we present stochastic block dual averaging (SBDA)---a novel class of block subgradient methods for convex nonsmooth and stochastic optimization. SBDA requires only a block of subgradients and updates blocks of variables and hence has significantly lower iterati...

  20. PRODUCTIVE GOVERNMENT EXPENDITURE IN A STOCHASTICALLY GROWING OPEN ECONOMY

    Haijun WANG; Shigeng HU

    2007-01-01

    This paper employs a stochastic endogenous growth model with productive government expenditure in a small open economy to analyze the optimal fiscal policy.First,a stochastic model of a small open economy is constructed.Second.the equilibrium solutions of the representative agent's stochastic optimization problem are derived.Third,we obtain the equilibrium solutions of the central planner's stochastic optimization problem and the optimal government expenditure policy.Finally,the optimal tax policy is characterized.