Bayesian decoding using unsorted spikes in the rat hippocampus
Kloosterman, Fabian; Layton, Stuart P.; Chen, Zhe; Wilson, Matthew A
2013-01-01
A fundamental task in neuroscience is to understand how neural ensembles represent information. Population decoding is a useful tool to extract information from neuronal populations based on the ensemble spiking activity. We propose a novel Bayesian decoding paradigm to decode unsorted spikes in the rat hippocampus. Our approach uses a direct mapping between spike waveform features and covariates of interest and avoids accumulation of spike sorting errors. Our decoding paradigm is nonparametr...
Kernel density compression for real-time Bayesian encoding/decoding of unsorted hippocampal spikes
Sodkomkham, Danaipat; Ciliberti, Davide; Wilson, Matthew A.; Fukui, Ken-ichi; Moriyama, Koichi; Numao, Masayuki; Kloosterman, Fabian
2015-01-01
To gain a better understanding of how neural ensembles communicate and process information, neural decoding algorithms are used to extract information encoded in their spiking activity. Bayesian decoding is one of the most used neural population decoding approaches to extract information from the ensemble spiking activity of rat hippocampal neurons. Recently it has been shown how Bayesian decoding can be implemented without the intermediate step of sorting spike waveforms into groups of singl...
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Elaine Astrand
Full Text Available Decoding neuronal information is important in neuroscience, both as a basic means to understand how neuronal activity is related to cerebral function and as a processing stage in driving neuroprosthetic effectors. Here, we compare the readout performance of six commonly used classifiers at decoding two different variables encoded by the spiking activity of the non-human primate frontal eye fields (FEF: the spatial position of a visual cue, and the instructed orientation of the animal's attention. While the first variable is exogenously driven by the environment, the second variable corresponds to the interpretation of the instruction conveyed by the cue; it is endogenously driven and corresponds to the output of internal cognitive operations performed on the visual attributes of the cue. These two variables were decoded using either a regularized optimal linear estimator in its explicit formulation, an optimal linear artificial neural network estimator, a non-linear artificial neural network estimator, a non-linear naïve Bayesian estimator, a non-linear Reservoir recurrent network classifier or a non-linear Support Vector Machine classifier. Our results suggest that endogenous information such as the orientation of attention can be decoded from the FEF with the same accuracy as exogenous visual information. All classifiers did not behave equally in the face of population size and heterogeneity, the available training and testing trials, the subject's behavior and the temporal structure of the variable of interest. In most situations, the regularized optimal linear estimator and the non-linear Support Vector Machine classifiers outperformed the other tested decoders.
Astrand, Elaine; Enel, Pierre; Ibos, Guilhem; Dominey, Peter Ford; Baraduc, Pierre; Ben Hamed, Suliann
2014-01-01
Decoding neuronal information is important in neuroscience, both as a basic means to understand how neuronal activity is related to cerebral function and as a processing stage in driving neuroprosthetic effectors. Here, we compare the readout performance of six commonly used classifiers at decoding two different variables encoded by the spiking activity of the non-human primate frontal eye fields (FEF): the spatial position of a visual cue, and the instructed orientation of the animal's attention. While the first variable is exogenously driven by the environment, the second variable corresponds to the interpretation of the instruction conveyed by the cue; it is endogenously driven and corresponds to the output of internal cognitive operations performed on the visual attributes of the cue. These two variables were decoded using either a regularized optimal linear estimator in its explicit formulation, an optimal linear artificial neural network estimator, a non-linear artificial neural network estimator, a non-linear naïve Bayesian estimator, a non-linear Reservoir recurrent network classifier or a non-linear Support Vector Machine classifier. Our results suggest that endogenous information such as the orientation of attention can be decoded from the FEF with the same accuracy as exogenous visual information. All classifiers did not behave equally in the face of population size and heterogeneity, the available training and testing trials, the subject's behavior and the temporal structure of the variable of interest. In most situations, the regularized optimal linear estimator and the non-linear Support Vector Machine classifiers outperformed the other tested decoders.
A Bayesian Bootstrap for a Finite Population
Lo, Albert Y.
1988-01-01
A Bayesian bootstrap for a finite population is introduced; its small-sample distributional properties are discussed and compared with those of the frequentist bootstrap for a finite population. It is also shown that the two are first-order asymptotically equivalent.
Bayesian Population Projections for the United Nations.
Raftery, Adrian E; Alkema, Leontine; Gerland, Patrick
2014-02-01
The United Nations regularly publishes projections of the populations of all the world's countries broken down by age and sex. These projections are the de facto standard and are widely used by international organizations, governments and researchers. Like almost all other population projections, they are produced using the standard deterministic cohort-component projection method and do not yield statements of uncertainty. We describe a Bayesian method for producing probabilistic population projections for most countries that the United Nations could use. It has at its core Bayesian hierarchical models for the total fertility rate and life expectancy at birth. We illustrate the method and show how it can be extended to address concerns about the UN's current assumptions about the long-term distribution of fertility. The method is implemented in the R packages bayesTFR, bayesLife, bayesPop and bayesDem.
Approximate Bayesian computation in population genetics.
Beaumont, Mark A; Zhang, Wenyang; Balding, David J.
2002-01-01
We propose a new method for approximate Bayesian statistical inference on the basis of summary statistics. The method is suited to complex problems that arise in population genetics, extending ideas developed in this setting by earlier authors. Properties of the posterior distribution of a parameter, such as its mean or density curve, are approximated without explicit likelihood calculations. This is achieved by fitting a local-linear regression of simulated parameter values on simulated summ...
Bayesian analysis of genetic differentiation between populations.
Corander, Jukka; Waldmann, Patrik; Sillanpää, Mikko J
2003-01-01
We introduce a Bayesian method for estimating hidden population substructure using multilocus molecular markers and geographical information provided by the sampling design. The joint posterior distribution of the substructure and allele frequencies of the respective populations is available in an analytical form when the number of populations is small, whereas an approximation based on a Markov chain Monte Carlo simulation approach can be obtained for a moderate or large number of populations. Using the joint posterior distribution, posteriors can also be derived for any evolutionary population parameters, such as the traditional fixation indices. A major advantage compared to most earlier methods is that the number of populations is treated here as an unknown parameter. What is traditionally considered as two genetically distinct populations, either recently founded or connected by considerable gene flow, is here considered as one panmictic population with a certain probability based on marker data and prior information. Analyses of previously published data on the Moroccan argan tree (Argania spinosa) and of simulated data sets suggest that our method is capable of estimating a population substructure, while not artificially enforcing a substructure when it does not exist. The software (BAPS) used for the computations is freely available from http://www.rni.helsinki.fi/~mjs. PMID:12586722
Book review: Bayesian analysis for population ecology
Link, William A.
2011-01-01
Brian Dennis described the field of ecology as “fertile, uncolonized ground for Bayesian ideas.” He continued: “The Bayesian propagule has arrived at the shore. Ecologists need to think long and hard about the consequences of a Bayesian ecology. The Bayesian outlook is a successful competitor, but is it a weed? I think so.” (Dennis 2004)
IQ Predicts Word Decoding Skills in Populations with Intellectual Disabilities
Levy, Yonata
2011-01-01
This is a study of word decoding in adolescents with Down syndrome and in adolescents with Intellectual Deficits of unknown etiology. It was designed as a replication of studies of word decoding in English speaking and in Hebrew speaking adolescents with Williams syndrome ([0230] and [0235]). Participants' IQ was matched to IQ in the groups with…
Emergence of optimal decoding of population codes through STDP.
Habenschuss, Stefan; Puhr, Helmut; Maass, Wolfgang
2013-06-01
The brain faces the problem of inferring reliable hidden causes from large populations of noisy neurons, for example, the direction of a moving object from spikes in area MT. It is known that a theoretically optimal likelihood decoding could be carried out by simple linear readout neurons if weights of synaptic connections were set to certain values that depend on the tuning functions of sensory neurons. We show here that such theoretically optimal readout weights emerge autonomously through STDP in conjunction with lateral inhibition between readout neurons. In particular, we identify a class of optimal STDP learning rules with homeostatic plasticity, for which the autonomous emergence of optimal readouts can be explained on the basis of a rigorous learning theory. This theory shows that the network motif we consider approximates expectation-maximization for creating internal generative models for hidden causes of high-dimensional spike inputs. Notably, we find that this optimal functionality can be well approximated by a variety of STDP rules beyond those predicted by theory. Furthermore, we show that this learning process is very stable and automatically adjusts weights to changes in the number of readout neurons, the tuning functions of sensory neurons, and the statistics of external stimuli. PMID:23517096
Brain Decoding-Classification of Hand Written Digits from fMRI Data Employing Bayesian Networks
Yargholi, Elahe'; Hossein-Zadeh, Gholam-Ali
2016-01-01
We are frequently exposed to hand written digits 0–9 in today's modern life. Success in decoding-classification of hand written digits helps us understand the corresponding brain mechanisms and processes and assists seriously in designing more efficient brain–computer interfaces. However, all digits belong to the same semantic category and similarity in appearance of hand written digits makes this decoding-classification a challenging problem. In present study, for the first time, augmented naïve Bayes classifier is used for classification of functional Magnetic Resonance Imaging (fMRI) measurements to decode the hand written digits which took advantage of brain connectivity information in decoding-classification. fMRI was recorded from three healthy participants, with an age range of 25–30. Results in different brain lobes (frontal, occipital, parietal, and temporal) show that utilizing connectivity information significantly improves decoding-classification and capability of different brain lobes in decoding-classification of hand written digits were compared to each other. In addition, in each lobe the most contributing areas and brain connectivities were determined and connectivities with short distances between their endpoints were recognized to be more efficient. Moreover, data driven method was applied to investigate the similarity of brain areas in responding to stimuli and this revealed both similarly active areas and active mechanisms during this experiment. Interesting finding was that during the experiment of watching hand written digits, there were some active networks (visual, working memory, motor, and language processing), but the most relevant one to the task was language processing network according to the voxel selection. PMID:27468261
Bayesian Variable Selection for Detecting Adaptive Genomic Differences Among Populations
Riebler, Andrea; Held, Leonhard; Stephan, Wolfgang
2008-01-01
We extend an Fst-based Bayesian hierarchical model, implemented via Markov chain Monte Carlo, for the detection of loci that might be subject to positive selection. This model divides the Fst-influencing factors into locus-specific effects, population-specific effects, and effects that are specific for the locus in combination with the population. We introduce a Bayesian auxiliary variable for each locus effect to automatically select nonneutral locus effects. As a by-product, the efficiency ...
Cuevas Rivera, Dario; Bitzer, Sebastian; Kiebel, Stefan J
2015-10-01
The olfactory information that is received by the insect brain is encoded in the form of spatiotemporal patterns in the projection neurons of the antennal lobe. These dense and overlapping patterns are transformed into a sparse code in Kenyon cells in the mushroom body. Although it is clear that this sparse code is the basis for rapid categorization of odors, it is yet unclear how the sparse code in Kenyon cells is computed and what information it represents. Here we show that this computation can be modeled by sequential firing rate patterns using Lotka-Volterra equations and Bayesian online inference. This new model can be understood as an 'intelligent coincidence detector', which robustly and dynamically encodes the presence of specific odor features. We found that the model is able to qualitatively reproduce experimentally observed activity in both the projection neurons and the Kenyon cells. In particular, the model explains mechanistically how sparse activity in the Kenyon cells arises from the dense code in the projection neurons. The odor classification performance of the model proved to be robust against noise and time jitter in the observed input sequences. As in recent experimental results, we found that recognition of an odor happened very early during stimulus presentation in the model. Critically, by using the model, we found surprising but simple computational explanations for several experimental phenomena. PMID:26451888
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Dario Cuevas Rivera
2015-10-01
Full Text Available The olfactory information that is received by the insect brain is encoded in the form of spatiotemporal patterns in the projection neurons of the antennal lobe. These dense and overlapping patterns are transformed into a sparse code in Kenyon cells in the mushroom body. Although it is clear that this sparse code is the basis for rapid categorization of odors, it is yet unclear how the sparse code in Kenyon cells is computed and what information it represents. Here we show that this computation can be modeled by sequential firing rate patterns using Lotka-Volterra equations and Bayesian online inference. This new model can be understood as an 'intelligent coincidence detector', which robustly and dynamically encodes the presence of specific odor features. We found that the model is able to qualitatively reproduce experimentally observed activity in both the projection neurons and the Kenyon cells. In particular, the model explains mechanistically how sparse activity in the Kenyon cells arises from the dense code in the projection neurons. The odor classification performance of the model proved to be robust against noise and time jitter in the observed input sequences. As in recent experimental results, we found that recognition of an odor happened very early during stimulus presentation in the model. Critically, by using the model, we found surprising but simple computational explanations for several experimental phenomena.
Cuevas Rivera, Dario; Bitzer, Sebastian; Kiebel, Stefan J.
2015-01-01
The olfactory information that is received by the insect brain is encoded in the form of spatiotemporal patterns in the projection neurons of the antennal lobe. These dense and overlapping patterns are transformed into a sparse code in Kenyon cells in the mushroom body. Although it is clear that this sparse code is the basis for rapid categorization of odors, it is yet unclear how the sparse code in Kenyon cells is computed and what information it represents. Here we show that this computation can be modeled by sequential firing rate patterns using Lotka-Volterra equations and Bayesian online inference. This new model can be understood as an ‘intelligent coincidence detector’, which robustly and dynamically encodes the presence of specific odor features. We found that the model is able to qualitatively reproduce experimentally observed activity in both the projection neurons and the Kenyon cells. In particular, the model explains mechanistically how sparse activity in the Kenyon cells arises from the dense code in the projection neurons. The odor classification performance of the model proved to be robust against noise and time jitter in the observed input sequences. As in recent experimental results, we found that recognition of an odor happened very early during stimulus presentation in the model. Critically, by using the model, we found surprising but simple computational explanations for several experimental phenomena. PMID:26451888
What is the `relevant population' in Bayesian forensic inference?
Brümmer, Niko; de Villiers, Edward
2014-01-01
In works discussing the Bayesian paradigm for presenting forensic evidence in court, the concept of a `relevant population' is often mentioned, without a clear definition of what is meant, and without recommendations of how to select such populations. This note is to try to better understand this concept. Our analysis is intended to be general enough to be applicable to different forensic technologies and we shall consider both DNA profiling and speaker recognition as examples.
Bayesian inference of population size history from multiple loci
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Drummond Alexei J
2008-10-01
Full Text Available Abstract Background Effective population size (Ne is related to genetic variability and is a basic parameter in many models of population genetics. A number of methods for inferring current and past population sizes from genetic data have been developed since JFC Kingman introduced the n-coalescent in 1982. Here we present the Extended Bayesian Skyline Plot, a non-parametric Bayesian Markov chain Monte Carlo algorithm that extends a previous coalescent-based method in several ways, including the ability to analyze multiple loci. Results Through extensive simulations we show the accuracy and limitations of inferring population size as a function of the amount of data, including recovering information about evolutionary bottlenecks. We also analyzed two real data sets to demonstrate the behavior of the new method; a single gene Hepatitis C virus data set sampled from Egypt and a 10 locus Drosophila ananassae data set representing 16 different populations. Conclusion The results demonstrate the essential role of multiple loci in recovering population size dynamics. Multi-locus data from a small number of individuals can precisely recover past bottlenecks in population size which can not be characterized by analysis of a single locus. We also demonstrate that sequence data quality is important because even moderate levels of sequencing errors result in a considerable decrease in estimation accuracy for realistic levels of population genetic variability.
Bayesian Optimization Algorithm, Population Sizing, and Time to Convergence
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Pelikan, M.; Goldberg, D.E.; Cantu-Paz, E.
2000-01-19
This paper analyzes convergence properties of the Bayesian optimization algorithm (BOA). It settles the BOA into the framework of problem decomposition used frequently in order to model and understand the behavior of simple genetic algorithms. The growth of the population size and the number of generations until convergence with respect to the size of a problem is theoretically analyzed. The theoretical results are supported by a number of experiments.
Bayesian Analysis of Multiple Populations I: Statistical and Computational Methods
Stenning, D C; Robinson, E; van Dyk, D A; von Hippel, T; Sarajedini, A; Stein, N
2016-01-01
We develop a Bayesian model for globular clusters composed of multiple stellar populations, extending earlier statistical models for open clusters composed of simple (single) stellar populations (vanDyk et al. 2009, Stein et al. 2013). Specifically, we model globular clusters with two populations that differ in helium abundance. Our model assumes a hierarchical structuring of the parameters in which physical properties---age, metallicity, helium abundance, distance, absorption, and initial mass---are common to (i) the cluster as a whole or to (ii) individual populations within a cluster, or are unique to (iii) individual stars. An adaptive Markov chain Monte Carlo (MCMC) algorithm is devised for model fitting that greatly improves convergence relative to its precursor non-adaptive MCMC algorithm. Our model and computational tools are incorporated into an open-source software suite known as BASE-9. We use numerical studies to demonstrate that our method can recover parameters of two-population clusters, and al...
Reference priors of nuisance parameters in Bayesian sequential population analysis
Bousquet, Nicolas
2010-01-01
Prior distributions elicited for modelling the natural fluctuations or the uncertainty on parameters of Bayesian fishery population models, can be chosen among a vast range of statistical laws. Since the statistical framework is defined by observational processes, observational parameters enter into the estimation and must be considered random, similarly to parameters or states of interest like population levels or real catches. The former are thus perceived as nuisance parameters whose values are intrinsically linked to the considered experiment, which also require noninformative priors. In fishery research Jeffreys methodology has been presented by Millar (2002) as a practical way to elicit such priors. However they can present wrong properties in multiparameter contexts. Therefore we suggest to use the elicitation method proposed by Berger and Bernardo to avoid paradoxical results raised by Jeffreys priors. These benchmark priors are derived here in the framework of sequential population analysis.
Bayesian variable selection for detecting adaptive genomic differences among populations.
Riebler, Andrea; Held, Leonhard; Stephan, Wolfgang
2008-03-01
We extend an F(st)-based Bayesian hierarchical model, implemented via Markov chain Monte Carlo, for the detection of loci that might be subject to positive selection. This model divides the F(st)-influencing factors into locus-specific effects, population-specific effects, and effects that are specific for the locus in combination with the population. We introduce a Bayesian auxiliary variable for each locus effect to automatically select nonneutral locus effects. As a by-product, the efficiency of the original approach is improved by using a reparameterization of the model. The statistical power of the extended algorithm is assessed with simulated data sets from a Wright-Fisher model with migration. We find that the inclusion of model selection suggests a clear improvement in discrimination as measured by the area under the receiver operating characteristic (ROC) curve. Additionally, we illustrate and discuss the quality of the newly developed method on the basis of an allozyme data set of the fruit fly Drosophila melanogaster and a sequence data set of the wild tomato Solanum chilense. For data sets with small sample sizes, high mutation rates, and/or long sequences, however, methods based on nucleotide statistics should be preferred. PMID:18245358
Modelling of population dynamics of red king crab using Bayesian approach
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Bakanev Sergey ...
2012-10-01
Modeling population dynamics based on the Bayesian approach enables to successfully resolve the above issues. The integration of the data from various studies into a unified model based on Bayesian parameter estimation method provides a much more detailed description of the processes occurring in the population.
Bayesian Analysis of Multiple Populations in Galactic Globular Clusters
Wagner-Kaiser, Rachel A.; Sarajedini, Ata; von Hippel, Ted; Stenning, David; Piotto, Giampaolo; Milone, Antonino; van Dyk, David A.; Robinson, Elliot; Stein, Nathan
2016-01-01
We use GO 13297 Cycle 21 Hubble Space Telescope (HST) observations and archival GO 10775 Cycle 14 HST ACS Treasury observations of Galactic Globular Clusters to find and characterize multiple stellar populations. Determining how globular clusters are able to create and retain enriched material to produce several generations of stars is key to understanding how these objects formed and how they have affected the structural, kinematic, and chemical evolution of the Milky Way. We employ a sophisticated Bayesian technique with an adaptive MCMC algorithm to simultaneously fit the age, distance, absorption, and metallicity for each cluster. At the same time, we also fit unique helium values to two distinct populations of the cluster and determine the relative proportions of those populations. Our unique numerical approach allows objective and precise analysis of these complicated clusters, providing posterior distribution functions for each parameter of interest. We use these results to gain a better understanding of multiple populations in these clusters and their role in the history of the Milky Way.Support for this work was provided by NASA through grant numbers HST-GO-10775 and HST-GO-13297 from the Space Telescope Science Institute, which is operated by AURA, Inc., under NASA contract NAS5-26555. This material is based upon work supported by the National Aeronautics and Space Administration under Grant NNX11AF34G issued through the Office of Space Science. This project was supported by the National Aeronautics & Space Administration through the University of Central Florida's NASA Florida Space Grant Consortium.
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Samir Kamel Ashour
2014-05-01
Full Text Available In this paper, Bayesian and non-Bayesian estimators have been obtained for two generalized exponential populations under joint type II censored scheme, which generalize results of Balakrishnan and Rasouli (2008 and Shafay et al. (2013. The maximum likelihood estimators (MLEs of the parameters and Bayes estimators have been developed under squared error loss function as well as under LINEX loss function. Moreover, approximate confidence region are also discussed and compared with two Bootstrap confidence regions. Also the MLE and three confidence intervals for the stress–strength parameter are explored. A numerical illustration for these new results is given.
Bremmer, Frank; Kaminiarz, Andre; Klingenhoefer, Steffen; Churan, Jan
2016-01-01
Primates perform saccadic eye movements in order to bring the image of an interesting target onto the fovea. Compared to stationary targets, saccades toward moving targets are computationally more demanding since the oculomotor system must use speed and direction information about the target as well as knowledge about its own processing latency to program an adequate, predictive saccade vector. In monkeys, different brain regions have been implicated in the control of voluntary saccades, among them the lateral intraparietal area (LIP). Here we asked, if activity in area LIP reflects the distance between fovea and saccade target, or the amplitude of an upcoming saccade, or both. We recorded single unit activity in area LIP of two macaque monkeys. First, we determined for each neuron its preferred saccade direction. Then, monkeys performed visually guided saccades along the preferred direction toward either stationary or moving targets in pseudo-randomized order. LIP population activity allowed to decode both, the distance between fovea and saccade target as well as the size of an upcoming saccade. Previous work has shown comparable results for saccade direction (Graf and Andersen, 2014a,b). Hence, LIP population activity allows to predict any two-dimensional saccade vector. Functional equivalents of macaque area LIP have been identified in humans. Accordingly, our results provide further support for the concept of activity from area LIP as neural basis for the control of an oculomotor brain-machine interface. PMID:27630547
Bayesian data analysis in population ecology: motivations, methods, and benefits
Dorazio, Robert
2016-01-01
During the 20th century ecologists largely relied on the frequentist system of inference for the analysis of their data. However, in the past few decades ecologists have become increasingly interested in the use of Bayesian methods of data analysis. In this article I provide guidance to ecologists who would like to decide whether Bayesian methods can be used to improve their conclusions and predictions. I begin by providing a concise summary of Bayesian methods of analysis, including a comparison of differences between Bayesian and frequentist approaches to inference when using hierarchical models. Next I provide a list of problems where Bayesian methods of analysis may arguably be preferred over frequentist methods. These problems are usually encountered in analyses based on hierarchical models of data. I describe the essentials required for applying modern methods of Bayesian computation, and I use real-world examples to illustrate these methods. I conclude by summarizing what I perceive to be the main strengths and weaknesses of using Bayesian methods to solve ecological inference problems.
Kernel Approximate Bayesian Computation for Population Genetic Inferences
Nakagome, Shigeki; Fukumizu, Kenji; Mano, Shuhei
2012-01-01
Approximate Bayesian computation (ABC) is a likelihood-free approach for Bayesian inferences based on a rejection algorithm method that applies a tolerance of dissimilarity between summary statistics from observed and simulated data. Although several improvements to the algorithm have been proposed, none of these improvements avoid the following two sources of approximation: 1) lack of sufficient statistics: sampling is not from the true posterior density given data but from an approximate po...
Bayesian parameter inference and model selection by population annealing in systems biology.
Murakami, Yohei
2014-01-01
Parameter inference and model selection are very important for mathematical modeling in systems biology. Bayesian statistics can be used to conduct both parameter inference and model selection. Especially, the framework named approximate Bayesian computation is often used for parameter inference and model selection in systems biology. However, Monte Carlo methods needs to be used to compute Bayesian posterior distributions. In addition, the posterior distributions of parameters are sometimes almost uniform or very similar to their prior distributions. In such cases, it is difficult to choose one specific value of parameter with high credibility as the representative value of the distribution. To overcome the problems, we introduced one of the population Monte Carlo algorithms, population annealing. Although population annealing is usually used in statistical mechanics, we showed that population annealing can be used to compute Bayesian posterior distributions in the approximate Bayesian computation framework. To deal with un-identifiability of the representative values of parameters, we proposed to run the simulations with the parameter ensemble sampled from the posterior distribution, named "posterior parameter ensemble". We showed that population annealing is an efficient and convenient algorithm to generate posterior parameter ensemble. We also showed that the simulations with the posterior parameter ensemble can, not only reproduce the data used for parameter inference, but also capture and predict the data which was not used for parameter inference. Lastly, we introduced the marginal likelihood in the approximate Bayesian computation framework for Bayesian model selection. We showed that population annealing enables us to compute the marginal likelihood in the approximate Bayesian computation framework and conduct model selection depending on the Bayes factor.
Individual organisms as units of analysis: Bayesian-clustering alternatives in population genetics.
Mank, Judith E; Avise, John C
2004-12-01
Population genetic analyses traditionally focus on the frequencies of alleles or genotypes in 'populations' that are delimited a priori. However, there are potential drawbacks of amalgamating genetic data into such composite attributes of assemblages of specimens: genetic information on individual specimens is lost or submerged as an inherent part of the analysis. A potential also exists for circular reasoning when a population's initial identification and subsequent genetic characterization are coupled. In principle, these problems are circumvented by some newer methods of population identification and individual assignment based on statistical clustering of specimen genotypes. Here we evaluate a recent method in this genre--Bayesian clustering--using four genotypic data sets involving different types of molecular markers in non-model organisms from nature. As expected, measures of population genetic structure (F(ST) and phiST) tended to be significantly greater in Bayesian a posteriori data treatments than in analyses where populations were delimited a priori. In the four biological contexts examined, which involved both geographic population structures and hybrid zones, Bayesian clustering was able to recover differentiated populations, and Bayesian assignments were able to identify likely population sources of specific individuals.
Mcdonnell, J. A. M.; Sullivan, K.
1992-01-01
The Long Duration Exposure Facility (LDEF) impact signature record and the size frequency distribution of craters and perforations offers a unique record of environmental data referenced conveniently to the geocentric reference frame. Chemical analysis of residues can offer only limited assistance. Hence, flux modeling has been developed to transform both geocentric orbital distributions and geocentrically unbounded interplanetary source distributions. This is applied to the foil and crater penetration records in the Ram (E), Trailing (W), and Space Pointing directions to offer the means of decoding the records. It shows that the mix of the components is size dependent. Parametric forms of the modeling transformations are presented for the orbital and unbounded populations.
Wen, Yen-Hsia; Wu, Shihn-Sheng; Lin, Chun-Hung Richard; Tsai, Jui-Hsiu; Yang, Pinchen; Chang, Yang-Pei; Tseng, Kuan-Hua
2016-05-01
Dementia is one of the most disabling and burdensome health conditions worldwide. In this study, we identified new potential risk factors for dementia from nationwide longitudinal population-based data by using Bayesian statistics.We first tested the consistency of the results obtained using Bayesian statistics with those obtained using classical frequentist probability for 4 recognized risk factors for dementia, namely severe head injury, depression, diabetes mellitus, and vascular diseases. Then, we used Bayesian statistics to verify 2 new potential risk factors for dementia, namely hearing loss and senile cataract, determined from the Taiwan's National Health Insurance Research Database.We included a total of 6546 (6.0%) patients diagnosed with dementia. We observed older age, female sex, and lower income as independent risk factors for dementia. Moreover, we verified the 4 recognized risk factors for dementia in the older Taiwanese population; their odds ratios (ORs) ranged from 3.469 to 1.207. Furthermore, we observed that hearing loss (OR = 1.577) and senile cataract (OR = 1.549) were associated with an increased risk of dementia.We found that the results obtained using Bayesian statistics for assessing risk factors for dementia, such as head injury, depression, DM, and vascular diseases, were consistent with those obtained using classical frequentist probability. Moreover, hearing loss and senile cataract were found to be potential risk factors for dementia in the older Taiwanese population. Bayesian statistics could help clinicians explore other potential risk factors for dementia and for developing appropriate treatment strategies for these patients.
Cornuet, Jean-Marie; Santos, Filipe; Beaumont, Mark A; Robert, Christian P.; Marin, Jean-Michel; Balding, David J.; Guillemaud, Thomas; Estoup, Arnaud
2008-01-01
Summary: Genetic data obtained on population samples convey information about their evolutionary history. Inference methods can extract part of this information but they require sophisticated statistical techniques that have been made available to the biologist community (through computer programs) only for simple and standard situations typically involving a small number of samples. We propose here a computer program (DIY ABC) for inference based on approximate Bayesian computation (ABC), in...
Bayesian Estimation of Population-Level Trends in Measures of Health Status
Mariel M. Finucane; Paciorek, Christopher J; Danaei, Goodarz; Ezzati, Majid
2014-01-01
Improving health worldwide will require rigorous quantification of population-level trends in health status. However, global-level surveys are not available, forcing researchers to rely on fragmentary country-specific data of varying quality. We present a Bayesian model that systematically combines disparate data to make country-, region- and global-level estimates of time trends in important health indicators. ¶ The model allows for time and age nonlinearity, and it borrows strength in...
Wagner-Kaiser, R; Sarajedini, A; von Hippel, T; van Dyk, D A; Robinson, E; Stein, N; Jefferys, W H
2016-01-01
We use Cycle 21 Hubble Space Telescope (HST) observations and HST archival ACS Treasury observations of 30 Galactic Globular Clusters to characterize two distinct stellar populations. A sophisticated Bayesian technique is employed to simultaneously sample the joint posterior distribution of age, distance, and extinction for each cluster, as well as unique helium values for two populations within each cluster and the relative proportion of those populations. We find the helium differences among the two populations in the clusters fall in the range of ~0.04 to 0.11. Because adequate models varying in CNO are not presently available, we view these spreads as upper limits and present them with statistical rather than observational uncertainties. Evidence supports previous studies suggesting an increase in helium content concurrent with increasing mass of the cluster and also find that the proportion of the first population of stars increases with mass as well. Our results are examined in the context of proposed g...
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Velimir Gayevskiy
Full Text Available Bayesian inference methods are extensively used to detect the presence of population structure given genetic data. The primary output of software implementing these methods are ancestry profiles of sampled individuals. While these profiles robustly partition the data into subgroups, currently there is no objective method to determine whether the fixed factor of interest (e.g. geographic origin correlates with inferred subgroups or not, and if so, which populations are driving this correlation. We present ObStruct, a novel tool to objectively analyse the nature of structure revealed in Bayesian ancestry profiles using established statistical methods. ObStruct evaluates the extent of structural similarity between sampled and inferred populations, tests the significance of population differentiation, provides information on the contribution of sampled and inferred populations to the observed structure and crucially determines whether the predetermined factor of interest correlates with inferred population structure. Analyses of simulated and experimental data highlight ObStruct's ability to objectively assess the nature of structure in populations. We show the method is capable of capturing an increase in the level of structure with increasing time since divergence between simulated populations. Further, we applied the method to a highly structured dataset of 1,484 humans from seven continents and a less structured dataset of 179 Saccharomyces cerevisiae from three regions in New Zealand. Our results show that ObStruct provides an objective metric to classify the degree, drivers and significance of inferred structure, as well as providing novel insights into the relationships between sampled populations, and adds a final step to the pipeline for population structure analyses.
Taboadai, Fernando G; Anadón, Ricardo
2016-04-01
Small pelagic fish species present complex dynamics that challenge population biologists and prevent effective management. Huge fluctuations in abundance have traditionally been associated with external environmental forcing on recruitment, exempting other processes from contributing to fisheries collapse. On the other hand, theory predicts that density dependence and overexploitation can increase the likelihood of population oscillations. Here, we combined nonlinear population modeling with Bayesian analysis to examine the importance of different regulatory mechanisms on the collapse of European anchovy (Engraulis encrasicolus) in the Bay of Biscay. The approach relied on detailed population data and in a careful characterization of changes in the environment experienced by anchovy early stages based mainly on satellite remote sensing. Alternative hypotheses about external forcing on recruitment determined prediction skill and provided alternative interpretations of the causes behind the collapse. Density dependence was weak and unable to generate huge oscillations. Instead, models considering changes in phytoplankton phenology or in larval drift presented the best prediction skill. Nevertheless, an extensive surrogate analysis showed that environmental fluctuations alone barely explain anchovy collapse without considering the impact of fishing. Our results highlight the effectiveness of a Bayesian approach to analyze the dynamics and collapse of managed populations. PMID:27411258
Dries, M.; Trager, S. C.; Koopmans, L. V. E.
2016-11-01
Recent studies based on the integrated light of distant galaxies suggest that the initial mass function (IMF) might not be universal. Variations of the IMF with galaxy type and/or formation time may have important consequences for our understanding of galaxy evolution. We have developed a new stellar population synthesis (SPS) code specifically designed to reconstruct the IMF. We implement a novel approach combining regularization with hierarchical Bayesian inference. Within this approach, we use a parametrized IMF prior to regulate a direct inference of the IMF. This direct inference gives more freedom to the IMF and allows the model to deviate from parametrized models when demanded by the data. We use Markov chain Monte Carlo sampling techniques to reconstruct the best parameters for the IMF prior, the age and the metallicity of a single stellar population. We present our code and apply our model to a number of mock single stellar populations with different ages, metallicities and IMFs. When systematic uncertainties are not significant, we are able to reconstruct the input parameters that were used to create the mock populations. Our results show that if systematic uncertainties do play a role, this may introduce a bias on the results. Therefore, it is important to objectively compare different ingredients of SPS models. Through its Bayesian framework, our model is well suited for this.
Palacios, Julia A; Minin, Vladimir N
2013-03-01
Changes in population size influence genetic diversity of the population and, as a result, leave a signature of these changes in individual genomes in the population. We are interested in the inverse problem of reconstructing past population dynamics from genomic data. We start with a standard framework based on the coalescent, a stochastic process that generates genealogies connecting randomly sampled individuals from the population of interest. These genealogies serve as a glue between the population demographic history and genomic sequences. It turns out that only the times of genealogical lineage coalescences contain information about population size dynamics. Viewing these coalescent times as a point process, estimating population size trajectories is equivalent to estimating a conditional intensity of this point process. Therefore, our inverse problem is similar to estimating an inhomogeneous Poisson process intensity function. We demonstrate how recent advances in Gaussian process-based nonparametric inference for Poisson processes can be extended to Bayesian nonparametric estimation of population size dynamics under the coalescent. We compare our Gaussian process (GP) approach to one of the state-of-the-art Gaussian Markov random field (GMRF) methods for estimating population trajectories. Using simulated data, we demonstrate that our method has better accuracy and precision. Next, we analyze two genealogies reconstructed from real sequences of hepatitis C and human Influenza A viruses. In both cases, we recover more believed aspects of the viral demographic histories than the GMRF approach. We also find that our GP method produces more reasonable uncertainty estimates than the GMRF method.
Directory of Open Access Journals (Sweden)
María Zubillaga
Full Text Available Understanding the mechanisms that drive population dynamics is fundamental for management of wild populations. The guanaco (Lama guanicoe is one of two wild camelid species in South America. We evaluated the effects of density dependence and weather variables on population regulation based on a time series of 36 years of population sampling of guanacos in Tierra del Fuego, Chile. The population density varied between 2.7 and 30.7 guanaco/km2, with an apparent monotonic growth during the first 25 years; however, in the last 10 years the population has shown large fluctuations, suggesting that it might have reached its carrying capacity. We used a Bayesian state-space framework and model selection to determine the effect of density and environmental variables on guanaco population dynamics. Our results show that the population is under density dependent regulation and that it is currently fluctuating around an average carrying capacity of 45,000 guanacos. We also found a significant positive effect of previous winter temperature while sheep density has a strong negative effect on the guanaco population growth. We conclude that there are significant density dependent processes and that climate as well as competition with domestic species have important effects determining the population size of guanacos, with important implications for management and conservation.
Zubillaga, María; Skewes, Oscar; Soto, Nicolás; Rabinovich, Jorge E.; Colchero, Fernando
2014-01-01
Understanding the mechanisms that drive population dynamics is fundamental for management of wild populations. The guanaco (Lama guanicoe) is one of two wild camelid species in South America. We evaluated the effects of density dependence and weather variables on population regulation based on a time series of 36 years of population sampling of guanacos in Tierra del Fuego, Chile. The population density varied between 2.7 and 30.7 guanaco/km2, with an apparent monotonic growth during the first 25 years; however, in the last 10 years the population has shown large fluctuations, suggesting that it might have reached its carrying capacity. We used a Bayesian state-space framework and model selection to determine the effect of density and environmental variables on guanaco population dynamics. Our results show that the population is under density dependent regulation and that it is currently fluctuating around an average carrying capacity of 45,000 guanacos. We also found a significant positive effect of previous winter temperature while sheep density has a strong negative effect on the guanaco population growth. We conclude that there are significant density dependent processes and that climate as well as competition with domestic species have important effects determining the population size of guanacos, with important implications for management and conservation. PMID:25514510
Connecting multiple spatial scales to decode the population activity of grid cells
Stemmler, Martin; Mathis, Alexander; Andreas V. M Herz
2015-01-01
Mammalian grid cells fire when an animal crosses the points of an imaginary hexagonal grid tessellating the environment. We show how animals can navigate by reading out a simple population vector of grid cell activity across multiple spatial scales, even though neural activity is intrinsically stochastic. This theory of dead reckoning explains why grid cells are organized into discrete modules within which all cells have the same lattice scale and orientation. The lattice scale changes from m...
Disambiguating the role of noise correlations when decoding neural populations together
Eyherabide, Hugo Gabriel
2016-01-01
Objective: Integrating information from populations of correlated neurons can become too complex even for the human brain. Ignoring correlations may simplify the process but also cause an information loss. This loss has been quantified using many methods, one of which has always been deemed exact due to its rigorous communication-theoretical foundations. However, we have recently shown that this method can overestimate the loss in real applications. Approach: To solve this problem, we disting...
A population-based Bayesian approach to the minimal model of glucose and insulin homeostasis
DEFF Research Database (Denmark)
Andersen, Kim Emil; Højbjerre, Malene
2005-01-01
-posed estimation problem, where the reconstruction most often has been done by non-linear least squares techniques separately for each entity. The minmal model was originally specified for a single individual and does not combine several individuals with the advantage of estimating the metabolic portrait...... to a population-based model. The estimation of the parameters are efficiently implemented in a Bayesian approach where posterior inference is made through the use of Markov chain Monte Carlo techniques. Hereby we obtain a powerful and flexible modelling framework for regularizing the ill-posed estimation problem...
Directory of Open Access Journals (Sweden)
Mariana Inés Pocovi
2015-06-01
Full Text Available Understanding the population structure and genetic diversity in sugarcane (Saccharum officinarum L. accessions from INTA germplasm bank (Argentina will be of great importance for germplasm collection and breeding improvement as it will identify diverse parental combinations to create segregating progenies with maximum genetic variability for further selection. A Bayesian approach, ordination methods (PCoA, Principal Coordinate Analysis and clustering analysis (UPGMA, Unweighted Pair Group Method with Arithmetic Mean were applied to this purpose. Sixty three INTA sugarcane hybrids were genotyped for 107 Simple Sequence Repeat (SSR and 136 Amplified Fragment Length Polymorphism (AFLP loci. Given the low probability values found with AFLP for individual assignment (4.7%, microsatellites seemed to perform better (54% for STRUCTURE analysis that revealed the germplasm to exist in five optimum groups with partly corresponding to their origin. However clusters shown high degree of admixture, F ST values confirmed the existence of differences among groups. Dissimilarity coefficients ranged from 0.079 to 0.651. PCoA separated sugarcane in groups that did not agree with those identified by STRUCTURE. The clustering including all genotypes neither showed resemblance to populations find by STRUCTURE, but clustering performed considering only individuals displaying a proportional membership > 0.6 in their primary population obtained with STRUCTURE showed close similarities. The Bayesian method indubitably brought more information on cultivar origins than classical PCoA and hierarchical clustering method.
Dries, M; Koopmans, L V E
2016-01-01
Recent studies based on the integrated light of distant galaxies suggest that the initial mass function (IMF) might not be universal. Variations of the IMF with galaxy type and/or formation time may have important consequences for our understanding of galaxy evolution. We have developed a new stellar population synthesis (SPS) code specifically designed to reconstruct the IMF. We implement a novel approach combining regularization with hierarchical Bayesian inference. Within this approach we use a parametrized IMF prior to regulate a direct inference of the IMF. This direct inference gives more freedom to the IMF and allows the model to deviate from parametrized models when demanded by the data. We use Markov Chain Monte Carlo sampling techniques to reconstruct the best parameters for the IMF prior, the age, and the metallicity of a single stellar population. We present our code and apply our model to a number of mock single stellar populations with different ages, metallicities, and IMFs. When systematic unc...
Connecting multiple spatial scales to decode the population activity of grid cells.
Stemmler, Martin; Mathis, Alexander; Herz, Andreas V M
2015-12-01
Mammalian grid cells fire when an animal crosses the points of an imaginary hexagonal grid tessellating the environment. We show how animals can navigate by reading out a simple population vector of grid cell activity across multiple spatial scales, even though neural activity is intrinsically stochastic. This theory of dead reckoning explains why grid cells are organized into discrete modules within which all cells have the same lattice scale and orientation. The lattice scale changes from module to module and should form a geometric progression with a scale ratio of around 3/2 to minimize the risk of making large-scale errors in spatial localization. Such errors should also occur if intermediate-scale modules are silenced, whereas knocking out the module at the smallest scale will only affect spatial precision. For goal-directed navigation, the allocentric grid cell representation can be readily transformed into the egocentric goal coordinates needed for planning movements. The goal location is set by nonlinear gain fields that act on goal vector cells. This theory predicts neural and behavioral correlates of grid cell readout that transcend the known link between grid cells of the medial entorhinal cortex and place cells of the hippocampus. PMID:26824061
Mike Lonergan; Dave Thompson; Len Thomas; Callan Duck
2011-01-01
1. For British grey seals, as with many pinniped species, population monitoring is implemented by aerial surveys of pups at breeding colonies. Scaling pup counts up to population estimates requires assumptions about population structure; this is straightforward when populations are growing exponentially, but not when growth slows, since it is unclear whether density dependence affects pup survival or fecundity. 2. We present an approximate Bayesian method for fitting pup trajectories, estimat...
Stenning, D. C.; Wagner-Kaiser, R.; Robinson, E.; van Dyk, D. A.; von Hippel, T.; Sarajedini, A.; Stein, N.
2016-07-01
We develop a Bayesian model for globular clusters composed of multiple stellar populations, extending earlier statistical models for open clusters composed of simple (single) stellar populations. Specifically, we model globular clusters with two populations that differ in helium abundance. Our model assumes a hierarchical structuring of the parameters in which physical properties—age, metallicity, helium abundance, distance, absorption, and initial mass—are common to (i) the cluster as a whole or to (ii) individual populations within a cluster, or are unique to (iii) individual stars. An adaptive Markov chain Monte Carlo (MCMC) algorithm is devised for model fitting that greatly improves convergence relative to its precursor non-adaptive MCMC algorithm. Our model and computational tools are incorporated into an open-source software suite known as BASE-9. We use numerical studies to demonstrate that our method can recover parameters of two-population clusters, and also show how model misspecification can potentially be identified. As a proof of concept, we analyze the two stellar populations of globular cluster NGC 5272 using our model and methods. (BASE-9 is available from GitHub: https://github.com/argiopetech/base/releases).
Full Bayesian hierarchical light curve modeling of core-collapse supernova populations
Sanders, Nathan; Betancourt, Michael; Soderberg, Alicia Margarita
2016-06-01
While wide field surveys have yielded remarkable quantities of photometry of transient objects, including supernovae, light curves reconstructed from this data suffer from several characteristic problems. Because most transients are discovered near the detection limit, signal to noise is generally poor; because coverage is limited to the observing season, light curves are often incomplete; and because temporal sampling can be uneven across filters, these problems can be exacerbated at any one wavelength. While the prevailing approach of modeling individual light curves independently is successful at recovering inferences for the objects with the highest quality observations, it typically neglects a substantial portion of the data and can introduce systematic biases. Joint modeling of the light curves of transient populations enables direct inference on population-level characteristics as well as superior measurements for individual objects. We present a new hierarchical Bayesian model for supernova light curves, where information inferred from observations of every individual light curve in a sample is partially pooled across objects to constrain population-level hyperparameters. Using an efficient Hamiltonian Monte Carlo sampling technique, the model posterior can be explored to enable marginalization over weakly-identified hyperparameters through full Bayesian inference. We demonstrate our technique on the Pan-STARRS1 (PS1) Type IIP supernova light curve sample published by Sanders et al. (2015), consisting of nearly 20,000 individual photometric observations of more than 70 supernovae in five photometric filters. We discuss the Stan probabilistic programming language used to implement the model, computational challenges, and prospects for future work including generalization to multiple supernova types. We also discuss scientific results from the PS1 dataset including a new relation between the peak magnitude and decline rate of SNe IIP, a new perspective on the
Lu, Yi; Montanari, Andrea
2007-01-01
'Tree pruning' (TP) is an algorithm for probabilistic inference on binary Markov random fields. It has been recently derived by Dror Weitz and used to construct the first fully polynomial approximation scheme for counting independent sets up to the `tree uniqueness threshold.' It can be regarded as a clever method for pruning the belief propagation computation tree, in such a way to exactly account for the effect of loops. In this paper we generalize the original algorithm to make it suitable for decoding linear codes, and discuss various schemes for pruning the computation tree. Further, we present the outcomes of numerical simulations on several linear codes, showing that tree pruning allows to interpolate continuously between belief propagation and maximum a posteriori decoding. Finally, we discuss theoretical implications of the new method.
International Nuclear Information System (INIS)
In making low-level radioactivity measurements of populations, it is commonly observed that a substantial portion of net results are negative. Furthermore, the observed variance of the measurement results arises from a combination of measurement uncertainty and population variability. This paper presents a method for disaggregating measurement uncertainty from population variability to produce a probability density function (PDF) of possibly true results. To do this, simple, justifiable, and reasonable assumptions are made about the relationship of the measurements to the measurands (the 'true values'). The measurements are assumed to be unbiased, that is, that their average value is the average of the measurands. Using traditional estimates of each measurement's uncertainty to disaggregate population variability from measurement uncertainty, a PDF of measurands for the population is produced. Then, using Bayes's theorem, the same assumptions, and all the data from the population of individuals, a prior PDF is computed for each individual's measurand. These PDFs are non-negative, and their average is equal to the average of the measurement results for the population. The uncertainty in these Bayesian posterior PDFs is all Berkson with no remaining classical component. The methods are applied to baseline bioassay data from the Hanford site. The data include 90Sr urinalysis measurements on 128 people, 137Cs in vivo measurements on 5,337 people, and 239Pu urinalysis measurements on 3,270 people. The method produces excellent results for the 90Sr and 137Cs measurements, since there are nonzero concentrations of these global fallout radionuclides in people who have not been occupationally exposed. The method does not work for the 239Pu measurements in non-occupationally exposed people because the population average is essentially zero.
Woodman, R. F.
1983-01-01
Decoding techniques and equipment used by MST radars are described and some recommendations for new systems are presented. Decoding can be done either by software in special-purpose (array processors, etc.) or general-purpose computers or in specially designed digital decoders. Both software and hardware decoders are discussed and the special case of decoding for bistatic radars is examined.
cosmoabc: Likelihood-free inference via Population Monte Carlo Approximate Bayesian Computation
Ishida, E E O; Penna-Lima, M; Cisewski, J; de Souza, R S; Trindade, A M M; Cameron, E
2015-01-01
Approximate Bayesian Computation (ABC) enables parameter inference for complex physical systems in cases where the true likelihood function is unknown, unavailable, or computationally too expensive. It relies on the forward simulation of mock data and comparison between observed and synthetic catalogues. Here we present cosmoabc, a Python ABC sampler featuring a Population Monte Carlo (PMC) variation of the original ABC algorithm, which uses an adaptive importance sampling scheme. The code is very flexible and can be easily coupled to an external simulator, while allowing to incorporate arbitrary distance and prior functions. As an example of practical application, we coupled cosmoabc with the numcosmo library and demonstrate how it can be used to estimate posterior probability distributions over cosmological parameters based on measurements of galaxy clusters number counts without computing the likelihood function. cosmoabc is published under the GPLv3 license on PyPI and GitHub and documentation is availabl...
Bazin, Eric; Dawson, Kevin J; Beaumont, Mark A
2010-06-01
We address the problem of finding evidence of natural selection from genetic data, accounting for the confounding effects of demographic history. In the absence of natural selection, gene genealogies should all be sampled from the same underlying distribution, often approximated by a coalescent model. Selection at a particular locus will lead to a modified genealogy, and this motivates a number of recent approaches for detecting the effects of natural selection in the genome as "outliers" under some models. The demographic history of a population affects the sampling distribution of genealogies, and therefore the observed genotypes and the classification of outliers. Since we cannot see genealogies directly, we have to infer them from the observed data under some model of mutation and demography. Thus the accuracy of an outlier-based approach depends to a greater or a lesser extent on the uncertainty about the demographic and mutational model. A natural modeling framework for this type of problem is provided by Bayesian hierarchical models, in which parameters, such as mutation rates and selection coefficients, are allowed to vary across loci. It has proved quite difficult computationally to implement fully probabilistic genealogical models with complex demographies, and this has motivated the development of approximations such as approximate Bayesian computation (ABC). In ABC the data are compressed into summary statistics, and computation of the likelihood function is replaced by simulation of data under the model. In a hierarchical setting one may be interested both in hyperparameters and parameters, and there may be very many of the latter--for example, in a genetic model, these may be parameters describing each of many loci or populations. This poses a problem for ABC in that one then requires summary statistics for each locus, which, if used naively, leads to a consequent difficulty in conditional density estimation. We develop a general method for applying
Strom, Daniel J; Joyce, Kevin E; MacLellan, Jay A; Watson, David J; Lynch, Timothy P; Antonio, Cheryl L; Birchall, Alan; Anderson, Kevin K; Zharov, Peter A
2012-04-01
In making low-level radioactivity measurements of populations, it is commonly observed that a substantial portion of net results is negative. Furthermore, the observed variance of the measurement results arises from a combination of measurement uncertainty and population variability. This paper presents a method for disaggregating measurement uncertainty from population variability to produce a probability density function (PDF) of possibly true results. To do this, simple, justifiable and reasonable assumptions are made about the relationship of the measurements to the measurands (the 'true values'). The measurements are assumed to be unbiased, that is, that their average value is the average of the measurands. Using traditional estimates of each measurement's uncertainty, a likelihood PDF for each individual's measurand is produced. Then using the same assumptions and all the data from the population of individuals, a prior PDF of measurands for the population is produced. The prior PDF is non-negative, and the average is equal to the average of the measurement results for the population. Using Bayes's theorem, posterior PDFs of each individual measurand are calculated. The uncertainty in these bayesian posterior PDFs appears to be all Berkson with no remaining classical component. The method is applied to baseline bioassay data from the Hanford site. The data include (90)Sr urinalysis measurements of 128 people, (137)Cs in vivo measurements of 5337 people and (239)Pu urinalysis measurements of 3270 people. The method produces excellent results for the (90)Sr and (137)Cs measurements, since there are non-zero concentrations of these global fallout radionuclides in people who have not been occupationally exposed. The method does not work for the (239)Pu measurements in non-occupationally exposed people because the population average is essentially zero relative to the sensitivity of the measurement technique. The method is shown to give results similar to
Directory of Open Access Journals (Sweden)
Mike Lonergan
2011-01-01
Full Text Available For British grey seals, as with many pinniped species, population monitoring is implemented by aerial surveys of pups at breeding colonies. Scaling pup counts up to population estimates requires assumptions about population structure; this is straightforward when populations are growing exponentially but not when growth slows, since it is unclear whether density dependence affects pup survival or fecundity. We present an approximate Bayesian method for fitting pup trajectories, estimating adult population size and investigating alternative biological models. The method is equivalent to fitting a density-dependent Leslie matrix model, within a Bayesian framework, but with the forms of the density-dependent effects as outputs rather than assumptions. It requires fewer assumptions than the state space models currently used and produces similar estimates. We discuss the potential and limitations of the method and suggest that this approach provides a useful tool for at least the preliminary analysis of similar datasets.
Enhanced Bayesian modelling in BAPS software for learning genetic structures of populations
Directory of Open Access Journals (Sweden)
Sirén Jukka
2008-12-01
Full Text Available Abstract Background During the most recent decade many Bayesian statistical models and software for answering questions related to the genetic structure underlying population samples have appeared in the scientific literature. Most of these methods utilize molecular markers for the inferences, while some are also capable of handling DNA sequence data. In a number of earlier works, we have introduced an array of statistical methods for population genetic inference that are implemented in the software BAPS. However, the complexity of biological problems related to genetic structure analysis keeps increasing such that in many cases the current methods may provide either inappropriate or insufficient solutions. Results We discuss the necessity of enhancing the statistical approaches to face the challenges posed by the ever-increasing amounts of molecular data generated by scientists over a wide range of research areas and introduce an array of new statistical tools implemented in the most recent version of BAPS. With these methods it is possible, e.g., to fit genetic mixture models using user-specified numbers of clusters and to estimate levels of admixture under a genetic linkage model. Also, alleles representing a different ancestry compared to the average observed genomic positions can be tracked for the sampled individuals, and a priori specified hypotheses about genetic population structure can be directly compared using Bayes' theorem. In general, we have improved further the computational characteristics of the algorithms behind the methods implemented in BAPS facilitating the analyses of large and complex datasets. In particular, analysis of a single dataset can now be spread over multiple computers using a script interface to the software. Conclusion The Bayesian modelling methods introduced in this article represent an array of enhanced tools for learning the genetic structure of populations. Their implementations in the BAPS software are
Directory of Open Access Journals (Sweden)
Li Sen
2012-03-01
Full Text Available Abstract Background The Approximate Bayesian Computation (ABC approach has been used to infer demographic parameters for numerous species, including humans. However, most applications of ABC still use limited amounts of data, from a small number of loci, compared to the large amount of genome-wide population-genetic data which have become available in the last few years. Results We evaluated the performance of the ABC approach for three 'population divergence' models - similar to the 'isolation with migration' model - when the data consists of several hundred thousand SNPs typed for multiple individuals by simulating data from known demographic models. The ABC approach was used to infer demographic parameters of interest and we compared the inferred values to the true parameter values that was used to generate hypothetical "observed" data. For all three case models, the ABC approach inferred most demographic parameters quite well with narrow credible intervals, for example, population divergence times and past population sizes, but some parameters were more difficult to infer, such as population sizes at present and migration rates. We compared the ability of different summary statistics to infer demographic parameters, including haplotype and LD based statistics, and found that the accuracy of the parameter estimates can be improved by combining summary statistics that capture different parts of information in the data. Furthermore, our results suggest that poor choices of prior distributions can in some circumstances be detected using ABC. Finally, increasing the amount of data beyond some hundred loci will substantially improve the accuracy of many parameter estimates using ABC. Conclusions We conclude that the ABC approach can accommodate realistic genome-wide population genetic data, which may be difficult to analyze with full likelihood approaches, and that the ABC can provide accurate and precise inference of demographic parameters from
A Bayesian integrated population dynamics model to analyze data for protected species
Directory of Open Access Journals (Sweden)
Hoyle, S. D.
2004-06-01
Full Text Available Managing wildlife-human interactions demands reliable information about the likely consequences of management actions. This requirement is a general one, whatever the taxonomic group. We describe a method for estimating population dynamics and decision analysis that is generally applicable, extremely flexible, uses data efficiently, and gives answers in a useful format. Our case study involves bycatch of a protected species, the Northeastern Offshore Spotted Dolphin (Stenella attenuata, in the tuna fishery of the eastern Pacific Ocean. Informed decision-making requires quantitative analyses taking all relevant information into account, assessing how bycatch affects these species and how regulations affect the fisheries, and describing the uncertainty in analyses. Bayesian analysis is an ideal framework for delivering information on uncertainty to the decision-making process. It also allows information from other populations or species or expert judgment to be included in the analysis, if appropriate. Integrated analysis attempts to include all relevant data for a population into one analysis by combining analyses, sharing parameters, and simultaneously estimating all parameters, using a combined objective function. It ensures that model assumptions and parameter estimates are consistent throughout the analysis, that uncertainty is propagated through the analysis, and that the correlations among parameters are preserved. Perhaps the most important aspect of integrated analysis is the way it both enables and forces consideration of the system as a whole, so that inconsistencies can be observed and resolved.
Billari, Francesco C; Graziani, Rebecca; Melilli, Eugenio
2014-10-01
This article suggests a procedure to derive stochastic population forecasts adopting an expert-based approach. As in previous work by Billari et al. (2012), experts are required to provide evaluations, in the form of conditional and unconditional scenarios, on summary indicators of the demographic components determining the population evolution: that is, fertility, mortality, and migration. Here, two main purposes are pursued. First, the demographic components are allowed to have some kind of dependence. Second, as a result of the existence of a body of shared information, possible correlations among experts are taken into account. In both cases, the dependence structure is not imposed by the researcher but rather is indirectly derived through the scenarios elicited from the experts. To address these issues, the method is based on a mixture model, within the so-called Supra-Bayesian approach, according to which expert evaluations are treated as data. The derived posterior distribution for the demographic indicators of interest is used as forecasting distribution, and a Markov chain Monte Carlo algorithm is designed to approximate this posterior. This article provides the questionnaire designed by the authors to collect expert opinions. Finally, an application to the forecast of the Italian population from 2010 to 2065 is proposed.
Bayesian time series analysis of segments of the Rocky Mountain trumpeter swan population
Wright, Christopher K.; Sojda, Richard S.; Goodman, Daniel
2002-01-01
A Bayesian time series analysis technique, the dynamic linear model, was used to analyze counts of Trumpeter Swans (Cygnus buccinator) summering in Idaho, Montana, and Wyoming from 1931 to 2000. For the Yellowstone National Park segment of white birds (sub-adults and adults combined) the estimated probability of a positive growth rate is 0.01. The estimated probability of achieving the Subcommittee on Rocky Mountain Trumpeter Swans 2002 population goal of 40 white birds for the Yellowstone segment is less than 0.01. Outside of Yellowstone National Park, Wyoming white birds are estimated to have a 0.79 probability of a positive growth rate with a 0.05 probability of achieving the 2002 objective of 120 white birds. In the Centennial Valley in southwest Montana, results indicate a probability of 0.87 that the white bird population is growing at a positive rate with considerable uncertainty. The estimated probability of achieving the 2002 Centennial Valley objective of 160 white birds is 0.14 but under an alternative model falls to 0.04. The estimated probability that the Targhee National Forest segment of white birds has a positive growth rate is 0.03. In Idaho outside of the Targhee National Forest, white birds are estimated to have a 0.97 probability of a positive growth rate with a 0.18 probability of attaining the 2002 goal of 150 white birds.
Zhou, Haiming; Hanson, Timothy; Knapp, Roland
2015-12-01
The global emergence of Batrachochytrium dendrobatidis (Bd) has caused the extinction of hundreds of amphibian species worldwide. It has become increasingly important to be able to precisely predict time to Bd arrival in a population. The data analyzed herein present a unique challenge in terms of modeling because there is a strong spatial component to Bd arrival time and the traditional proportional hazards assumption is grossly violated. To address these concerns, we develop a novel marginal Bayesian nonparametric survival model for spatially correlated right-censored data. This class of models assumes that the logarithm of survival times marginally follow a mixture of normal densities with a linear-dependent Dirichlet process prior as the random mixing measure, and their joint distribution is induced by a Gaussian copula model with a spatial correlation structure. To invert high-dimensional spatial correlation matrices, we adopt a full-scale approximation that can capture both large- and small-scale spatial dependence. An efficient Markov chain Monte Carlo algorithm with delayed rejection is proposed for posterior computation, and an R package spBayesSurv is provided to fit the model. This approach is first evaluated through simulations, then applied to threatened frog populations in Sequoia-Kings Canyon National Park. PMID:26148536
Fajardo, Alvaro; Soñora, Martín; Moreno, Pilar; Moratorio, Gonzalo; Cristina, Juan
2016-10-01
Zika virus (ZIKV) is a member of the family Flaviviridae. In 2015, ZIKV triggered an epidemic in Brazil and spread across Latin America. By May of 2016, the World Health Organization warns over spread of ZIKV beyond this region. Detailed studies on the mode of evolution of ZIKV strains are extremely important for our understanding of the emergence and spread of ZIKV populations. In order to gain insight into these matters, a Bayesian coalescent Markov Chain Monte Carlo analysis of complete genome sequences of recently isolated ZIKV strains was performed. The results of these studies revealed a mean rate of evolution of 1.20 × 10(-3) nucleotide substitutions per site per year (s/s/y) for ZIKV strains enrolled in this study. Several variants isolated in China are grouped together with all strains isolated in Latin America. Another genetic group composed exclusively by Chinese strains were also observed, suggesting the co-circulation of different genetic lineages in China. These findings indicate a high level of diversification of ZIKV populations. Strains isolated from microcephaly cases do not share amino acid substitutions, suggesting that other factors besides viral genetic differences may play a role for the proposed pathogenesis caused by ZIKV infection. J. Med. Virol. 88:1672-1676, 2016. © 2016 Wiley Periodicals, Inc. PMID:27278855
Fajardo, Alvaro; Soñora, Martín; Moreno, Pilar; Moratorio, Gonzalo; Cristina, Juan
2016-10-01
Zika virus (ZIKV) is a member of the family Flaviviridae. In 2015, ZIKV triggered an epidemic in Brazil and spread across Latin America. By May of 2016, the World Health Organization warns over spread of ZIKV beyond this region. Detailed studies on the mode of evolution of ZIKV strains are extremely important for our understanding of the emergence and spread of ZIKV populations. In order to gain insight into these matters, a Bayesian coalescent Markov Chain Monte Carlo analysis of complete genome sequences of recently isolated ZIKV strains was performed. The results of these studies revealed a mean rate of evolution of 1.20 × 10(-3) nucleotide substitutions per site per year (s/s/y) for ZIKV strains enrolled in this study. Several variants isolated in China are grouped together with all strains isolated in Latin America. Another genetic group composed exclusively by Chinese strains were also observed, suggesting the co-circulation of different genetic lineages in China. These findings indicate a high level of diversification of ZIKV populations. Strains isolated from microcephaly cases do not share amino acid substitutions, suggesting that other factors besides viral genetic differences may play a role for the proposed pathogenesis caused by ZIKV infection. J. Med. Virol. 88:1672-1676, 2016. © 2016 Wiley Periodicals, Inc.
Institute of Scientific and Technical Information of China (English)
MING Zhimao; TAO Junyong; ZHANG Yunan; YI Xiaoshan; CHEN Xun
2009-01-01
New armament systems are subjected to the method for dealing with multi-stage system reliability-growth statistical problems of diverse population in order to improve reliability before starting mass production. Aiming at the test process which is high expense and small sample-size in the development of complex system, the specific methods are studied on how to process the statistical information of Bayesian reliability growth regarding diverse populations. Firstly, according to the characteristics of reliability growth during product development, the Bayesian method is used to integrate the testing information of multi-stage and the order relations of distribution parameters. And then a Gamma-Beta prior distribution is proposed based on non-homogeneous Poisson process(NHPP) corresponding to the reliability growth process. The posterior distribution of reliability parameters is obtained regarding different stages of product, and the reliability parameters are evaluated based on the posterior distribution. Finally, Bayesian approach proposed in this paper for multi-stage reliability growth test is applied to the test process which is small sample-size in the astronautics filed. The results of a numerical example show that the presented model can make use of the diverse information synthetically, and pave the way for the application of the Bayesian model for multi-stage reliability growth test evaluation with small sample-size. The method is useful for evaluating multi-stage system reliability and making reliability growth plan rationally.
Directory of Open Access Journals (Sweden)
Fang-Rong Yan
Full Text Available This article provides a fully bayesian approach for modeling of single-dose and complete pharmacokinetic data in a population pharmacokinetic (PK model. To overcome the impact of outliers and the difficulty of computation, a generalized linear model is chosen with the hypothesis that the errors follow a multivariate Student t distribution which is a heavy-tailed distribution. The aim of this study is to investigate and implement the performance of the multivariate t distribution to analyze population pharmacokinetic data. Bayesian predictive inferences and the Metropolis-Hastings algorithm schemes are used to process the intractable posterior integration. The precision and accuracy of the proposed model are illustrated by the simulating data and a real example of theophylline data.
Wagner-Kaiser, R.; Stenning, D. C.; Robinson, E.; von Hippel, T.; Sarajedini, A.; van Dyk, D. A.; Stein, N.; Jefferys, W. H.
2016-07-01
We use Cycle 21 Hubble Space Telescope (HST) observations and HST archival Advanced Camera for Surveys Treasury observations of Galactic Globular Clusters to find and characterize two stellar populations in NGC 5024 (M53), NGC 5272 (M3), and NGC 6352. For these three clusters, both single and double-population analyses are used to determine a best fit isochrone(s). We employ a sophisticated Bayesian analysis technique to simultaneously fit the cluster parameters (age, distance, absorption, and metallicity) that characterize each cluster. For the two-population analysis, unique population level helium values are also fit to each distinct population of the cluster and the relative proportions of the populations are determined. We find differences in helium ranging from ∼0.05 to 0.11 for these three clusters. Model grids with solar α-element abundances ([α/Fe] = 0.0) and enhanced α-elements ([α/Fe] = 0.4) are adopted.
Wagner-Kaiser, R.; Stenning, D. C.; Robinson, E.; von Hippel, T.; Sarajedini, A.; van Dyk, D. A.; Stein, N.; Jefferys, W. H.
2016-07-01
We use Cycle 21 Hubble Space Telescope (HST) observations and HST archival Advanced Camera for Surveys Treasury observations of Galactic Globular Clusters to find and characterize two stellar populations in NGC 5024 (M53), NGC 5272 (M3), and NGC 6352. For these three clusters, both single and double-population analyses are used to determine a best fit isochrone(s). We employ a sophisticated Bayesian analysis technique to simultaneously fit the cluster parameters (age, distance, absorption, and metallicity) that characterize each cluster. For the two-population analysis, unique population level helium values are also fit to each distinct population of the cluster and the relative proportions of the populations are determined. We find differences in helium ranging from ˜0.05 to 0.11 for these three clusters. Model grids with solar α-element abundances ([α/Fe] = 0.0) and enhanced α-elements ([α/Fe] = 0.4) are adopted.
Wagner-Kaiser, R; Robinson, E; von Hippel, T; Sarajedini, A; van Dyk, D A; Stein, N; Jefferys, W H
2016-01-01
We use Cycle 21 Hubble Space Telescope (HST) observations and HST archival ACS Treasury observations of Galactic Globular Clusters to find and characterize two stellar populations in NGC 5024 (M53), NGC 5272 (M3), and NGC 6352. For these three clusters, both single and double-population analyses are used to determine a best fit isochrone(s). We employ a sophisticated Bayesian analysis technique to simultaneously fit the cluster parameters (age, distance, absorption, and metallicity) that characterize each cluster. For the two-population analysis, unique population level helium values are also fit to each distinct population of the cluster and the relative proportions of the populations are determined. We find differences in helium ranging from $\\sim$0.05 to 0.11 for these three clusters. Model grids with solar $\\alpha$-element abundances ([$\\alpha$/Fe] =0.0) and enhanced $\\alpha$-elements ([$\\alpha$/Fe]=0.4) are adopted.
DEFF Research Database (Denmark)
Justesen, Jørn; Høholdt, Tom; Hjaltason, Johan
2005-01-01
We analyze the relation between iterative decoding and the extended parity check matrix. By considering a modified version of bit flipping, which produces a list of decoded words, we derive several relations between decodable error patterns and the parameters of the code. By developing a tree...
DEFF Research Database (Denmark)
Heller, Rasmus; Chikhi, Lounes; Siegismund, Hans
2013-01-01
when it is violated. Among the most widely applied demographic inference methods are Bayesian skyline plots (BSPs), which are used across a range of biological fields. Violations of the panmixia assumption are to be expected in many biological systems, but the consequences for skyline plot inferences...
Directory of Open Access Journals (Sweden)
Salvidio Sebastiano
2010-02-01
Full Text Available Abstract Background It has been suggested that Plethodontid salamanders are excellent candidates for indicating ecosystem health. However, detailed, long-term data sets of their populations are rare, limiting our understanding of the demographic processes underlying their population fluctuations. Here we present a demographic analysis based on a 1996 - 2008 data set on an underground population of Speleomantes strinatii (Aellen in NW Italy. We utilised a Bayesian state-space approach allowing us to parameterise a stage-structured Lefkovitch model. We used all the available population data from annual temporary removal experiments to provide us with the baseline data on the numbers of juveniles, subadults and adult males and females present at any given time. Results Sampling the posterior chains of the converged state-space model gives us the likelihood distributions of the state-specific demographic rates and the associated uncertainty of these estimates. Analysing the resulting parameterised Lefkovitch matrices shows that the population growth is very close to 1, and that at population equilibrium we expect half of the individuals present to be adults of reproductive age which is what we also observe in the data. Elasticity analysis shows that adult survival is the key determinant for population growth. Conclusion This analysis demonstrates how an understanding of population demography can be gained from structured population data even in a case where following marked individuals over their whole lifespan is not practical.
Kocabas, Verda; Dragicevic, Suzana
2013-10-01
Land-use change models grounded in complexity theory such as agent-based models (ABMs) are increasingly being used to examine evolving urban systems. The objective of this study is to develop a spatial model that simulates land-use change under the influence of human land-use choice behavior. This is achieved by integrating the key physical and social drivers of land-use change using Bayesian networks (BNs) coupled with agent-based modeling. The BNAS model, integrated Bayesian network-based agent system, presented in this study uses geographic information systems, ABMs, BNs, and influence diagram principles to model population change on an irregular spatial structure. The model is parameterized with historical data and then used to simulate 20 years of future population and land-use change for the City of Surrey, British Columbia, Canada. The simulation results identify feasible new urban areas for development around the main transportation corridors. The obtained new development areas and the projected population trajectories with the“what-if” scenario capabilities can provide insights into urban planners for better and more informed land-use policy or decision-making processes.
Forced Sequence Sequential Decoding
DEFF Research Database (Denmark)
Jensen, Ole Riis
In this thesis we describe a new concatenated decoding scheme based on iterations between an inner sequentially decoded convolutional code of rate R=1/4 and memory M=23, and block interleaved outer Reed-Solomon codes with non-uniform profile. With this scheme decoding with good performance is pos...... of computational overflow. Analytical results for the probability that the first Reed-Solomon word is decoded after C computations are presented. This is supported by simulation results that are also extended to other parameters....
Directory of Open Access Journals (Sweden)
Lauren L. Hulsman Hanna
2014-12-01
Full Text Available The objectives of this study were to 1 compare four models for breeding value prediction using genomic or pedigree information and 2 evaluate the impact of fixed effects that account for family structure. Comparisons were made in a Nellore-Angus population comprising F2, F3 and half-siblings to embryo transfer F2 calves with records for overall temperament at weaning (TEMP; n = 769 and Warner-Bratzler shear force (WBSF; n = 387. After quality control, there were 34,913 whole genome SNP markers remaining. Bayesian methods employed were BayesB ( π = 0.995 or 0.997 for WBSF or TEMP, respectively and BayesC (π = 0 and π, where π is the ideal proportion of markers not included. Direct genomic values (DGV from single trait Bayesian analyses were compared to conventional pedigree-based animal model breeding values. Numerically, BayesC procedures (using π had the highest accuracy of all models for WBSF and TEMP ( ρgg = 0.843 and 0.923, respectively, but BayesB had the least bias (regression of performance on prediction closest to 1, βy,x = 2.886 and 1.755, respectively. Accounting for family structure decreased accuracy and increased bias in prediction of DGV indicating a detrimental impact when used in these prediction methods that simultaneously fit many markers.
Directory of Open Access Journals (Sweden)
Cheng J
2016-10-01
Full Text Available Ji Cheng,1,2 Alfonso Iorio,2,3 Maura Marcucci,4 Vadim Romanov,5 Eleanor M Pullenayegum,6,7 John K Marshall,3,8 Lehana Thabane1,2 1Biostatistics Unit, St Joseph’s Healthcare Hamilton, 2Department of Clinical Epidemiology and Biostatistics, 3Department of Medicine, McMaster University, Hamilton, ON, Canada; 4Geriatrics, Fondazione Ca’ Granda Ospedale Maggiore Policlinico, Università degli Studi di Milano, Milan, Italy; 5Baxter HealthCare, Global Medical Affairs, Westlake Village, CA, USA; 6Child Health Evaluation Sciences, Hospital for Sick Children, 7Dalla Lana School of Public Health, University of Toronto, Toronto, 8Division of Gastroenterology, Hamilton Health Science, Hamilton, ON, Canada Background: Developing inhibitors is a rare event during the treatment of hemophilia A. The multifacets and uncertainty surrounding the development of inhibitors further complicate the process of estimating inhibitor rate from the limited data. Bayesian statistical modeling provides a useful tool in generating, enhancing, and exploring the evidence through incorporating all the available information.Methods: We built our Bayesian analysis using three study cases to estimate the inhibitor rates of patients with hemophilia A in three different scenarios: Case 1, a single cohort of previously treated patients (PTPs or previously untreated patients; Case 2, a meta-analysis of PTP cohorts; and Case 3, a previously unexplored patient population – patients with baseline low-titer inhibitor or history of inhibitor development. The data used in this study were extracted from three published ADVATE (antihemophilic factor [recombinant] is a product of Baxter for treating hemophilia A post-authorization surveillance studies. Noninformative and informative priors were applied to Bayesian standard (Case 1 or random-effects (Case 2 and Case 3 logistic models. Bayesian probabilities of satisfying three meaningful thresholds of the risk of developing a clinical
Berg, van den S.; Calus, M.P.L.; Meuwissen, T.H.E.; Wientjes, Y.C.J.
2015-01-01
Background: The use of information across populations is an attractive approach to increase the accuracy of genomic prediction for numerically small populations. However, accuracies of across population genomic prediction, in which reference and selection individuals are from different population
Zubillaga, María; Skewes, Oscar; Soto, Nicolás; Jorge E Rabinovich; Colchero, Fernando
2014-01-01
Understanding the mechanisms that drive population dynamics is fundamental for management of wild populations. The guanaco (Lama guanicoe) is one of two wild camelid species in South America. We evaluated the effects of density dependence and weather variables on population regulation based on a time series of 36 years of population sampling of guanacos in Tierra del Fuego, Chile. The population density varied between 2.7 and 30.7 guanaco/km2, with an apparent monotonic growth during the firs...
High Speed Viterbi Decoder Architecture
DEFF Research Database (Denmark)
Paaske, Erik; Andersen, Jakob Dahl
1998-01-01
The fastest commercially available Viterbi decoders for the (171,133) standard rate 1/2 code operate with a decoding speed of 40-50 Mbit/s (net data rate). In this paper we present a suitable architecture for decoders operating with decoding speeds of 150-300 Mbit/s.......The fastest commercially available Viterbi decoders for the (171,133) standard rate 1/2 code operate with a decoding speed of 40-50 Mbit/s (net data rate). In this paper we present a suitable architecture for decoders operating with decoding speeds of 150-300 Mbit/s....
Forced Sequence Sequential Decoding
DEFF Research Database (Denmark)
Jensen, Ole Riis; Paaske, Erik
1998-01-01
the iteration process provides the sequential decoders with side information that allows a smaller average load and minimizes the probability of computational overflow. Analytical results for the probability that the first RS word is decoded after C computations are presented. These results are supported...
Institute of Scientific and Technical Information of China (English)
LiuLiu
2004-01-01
Mai Jia has won great success with his novel Decoding overnight. As a mominee of the Mao Dun Literary Prize in 2003 and one of the five candidates for the final of "The 6th China's Books Award", Decoding was ranked the first one in "The chart of China's novels in 2002". It was not only reprinted in 27 newspapers and
Transductive neural decoding for unsorted neuronal spikes of rat hippocampus
Chen, Zhe; Kloosterman, Fabian; Layton, Stuart; Wilson, Matthew A
2012-01-01
Neural decoding is an important approach for extracting information from population codes. We previously proposed a novel transductive neural decoding paradigm and applied it to reconstruct the rat’s position during navigation based on unsorted rat hippocampal ensemble spiking activity. Here, we investigate several important technical issues of this new paradigm using one data set of one animal. Several extensions of our decoding method are discussed.
Lyons, James E.; Kendall, William; Royle, J. Andrew; Converse, Sarah J.; Andres, Brad A.; Buchanan, Joseph B.
2016-01-01
We present a novel formulation of a mark–recapture–resight model that allows estimation of population size, stopover duration, and arrival and departure schedules at migration areas. Estimation is based on encounter histories of uniquely marked individuals and relative counts of marked and unmarked animals. We use a Bayesian analysis of a state–space formulation of the Jolly–Seber mark–recapture model, integrated with a binomial model for counts of unmarked animals, to derive estimates of population size and arrival and departure probabilities. We also provide a novel estimator for stopover duration that is derived from the latent state variable representing the interim between arrival and departure in the state–space model. We conduct a simulation study of field sampling protocols to understand the impact of superpopulation size, proportion marked, and number of animals sampled on bias and precision of estimates. Simulation results indicate that relative bias of estimates of the proportion of the population with marks was low for all sampling scenarios and never exceeded 2%. Our approach does not require enumeration of all unmarked animals detected or direct knowledge of the number of marked animals in the population at the time of the study. This provides flexibility and potential application in a variety of sampling situations (e.g., migratory birds, breeding seabirds, sea turtles, fish, pinnipeds, etc.). Application of the methods is demonstrated with data from a study of migratory sandpipers.
Directory of Open Access Journals (Sweden)
Allen Rodrigo
2006-01-01
Full Text Available Using the structured serial coalescent with Bayesian MCMC and serial samples, we estimate population size when some demes are not sampled or are hidden, ie ghost demes. It is found that even with the presence of a ghost deme, accurate inference was possible if the parameters are estimated with the true model. However with an incorrect model, estimates were biased and can be positively misleading. We extend these results to the case where there are sequences from the ghost at the last time sample. This case can arise in HIV patients, when some tissue samples and viral sequences only become available after death. When some sequences from the ghost deme are available at the last sampling time, estimation bias is reduced and accurate estimation of parameters associated with the ghost deme is possible despite sampling bias. Migration rates for this case are also shown to be good estimates when migration values are low.
Directory of Open Access Journals (Sweden)
Simon Boitard
2016-03-01
Full Text Available Inferring the ancestral dynamics of effective population size is a long-standing question in population genetics, which can now be tackled much more accurately thanks to the massive genomic data available in many species. Several promising methods that take advantage of whole-genome sequences have been recently developed in this context. However, they can only be applied to rather small samples, which limits their ability to estimate recent population size history. Besides, they can be very sensitive to sequencing or phasing errors. Here we introduce a new approximate Bayesian computation approach named PopSizeABC that allows estimating the evolution of the effective population size through time, using a large sample of complete genomes. This sample is summarized using the folded allele frequency spectrum and the average zygotic linkage disequilibrium at different bins of physical distance, two classes of statistics that are widely used in population genetics and can be easily computed from unphased and unpolarized SNP data. Our approach provides accurate estimations of past population sizes, from the very first generations before present back to the expected time to the most recent common ancestor of the sample, as shown by simulations under a wide range of demographic scenarios. When applied to samples of 15 or 25 complete genomes in four cattle breeds (Angus, Fleckvieh, Holstein and Jersey, PopSizeABC revealed a series of population declines, related to historical events such as domestication or modern breed creation. We further highlight that our approach is robust to sequencing errors, provided summary statistics are computed from SNPs with common alleles.
Burgess, Ralph; Yang, Ziheng
2008-09-01
Estimation of population parameters for the common ancestors of humans and the great apes is important in understanding our evolutionary history. In particular, inference of population size for the human-chimpanzee common ancestor may shed light on the process by which the 2 species separated and on whether the human population experienced a severe size reduction in its early evolutionary history. In this study, the Bayesian method of ancestral inference of Rannala and Yang (2003. Bayes estimation of species divergence times and ancestral population sizes using DNA sequences from multiple loci. Genetics. 164:1645-1656) was extended to accommodate variable mutation rates among loci and random species-specific sequencing errors. The model was applied to analyze a genome-wide data set of approximately 15,000 neutral loci (7.4 Mb) aligned for human, chimpanzee, gorilla, orangutan, and macaque. We obtained robust and precise estimates for effective population sizes along the hominoid lineage extending back approximately 30 Myr to the cercopithecoid divergence. The results showed that ancestral populations were 5-10 times larger than modern humans along the entire hominoid lineage. The estimates were robust to the priors used and to model assumptions about recombination. The unusually low X chromosome divergence between human and chimpanzee could not be explained by variation in the male mutation bias or by current models of hybridization and introgression. Instead, our parameter estimates were consistent with a simple instantaneous process for human-chimpanzee speciation but showed a major reduction in X chromosome effective population size peculiar to the human-chimpanzee common ancestor, possibly due to selective sweeps on the X prior to separation of the 2 species. PMID:18603620
DEFF Research Database (Denmark)
Zubillaga, Maria; Skewes, Oscar; Soto, Nicolás;
2014-01-01
on a time series of 36 years of population sampling of guanacos in Tierra del Fuego, Chile. The population density varied between 2.7 and 30.7 guanaco/km², with an apparent monotonic growth during the first 25 years; however, in the last 10 years the population has shown large fluctuations, suggesting...
Storz, Jay F; Beaumont, Mark A; Alberts, Susan C
2002-11-01
The purpose of this study was to test for evidence that savannah baboons (Papio cynocephalus) underwent a population expansion in concert with a hypothesized expansion of African human and chimpanzee populations during the late Pleistocene. The rationale is that any type of environmental event sufficient to cause simultaneous population expansions in African humans and chimpanzees would also be expected to affect other codistributed mammals. To test for genetic evidence of population expansion or contraction, we performed a coalescent analysis of multilocus microsatellite data using a hierarchical Bayesian model. Markov chain Monte Carlo (MCMC) simulations were used to estimate the posterior probability density of demographic and genealogical parameters. The model was designed to allow interlocus variation in mutational and demographic parameters, which made it possible to detect aberrant patterns of variation at individual loci that could result from heterogeneity in mutational dynamics or from the effects of selection at linked sites. Results of the MCMC simulations were consistent with zero variance in demographic parameters among loci, but there was evidence for a 10- to 20-fold difference in mutation rate between the most slowly and most rapidly evolving loci. Results of the model provided strong evidence that savannah baboons have undergone a long-term historical decline in population size. The mode of the highest posterior density for the joint distribution of current and ancestral population size indicated a roughly eightfold contraction over the past 1,000 to 250,000 years. These results indicate that savannah baboons apparently did not share a common demographic history with other codistributed primate species. PMID:12411607
Directory of Open Access Journals (Sweden)
Mario A Pardo
Full Text Available We inferred the population densities of blue whales (Balaenoptera musculus and short-beaked common dolphins (Delphinus delphis in the Northeast Pacific Ocean as functions of the water-column's physical structure by implementing hierarchical models in a Bayesian framework. This approach allowed us to propagate the uncertainty of the field observations into the inference of species-habitat relationships and to generate spatially explicit population density predictions with reduced effects of sampling heterogeneity. Our hypothesis was that the large-scale spatial distributions of these two cetacean species respond primarily to ecological processes resulting from shoaling and outcropping of the pycnocline in regions of wind-forced upwelling and eddy-like circulation. Physically, these processes affect the thermodynamic balance of the water column, decreasing its volume and thus the height of the absolute dynamic topography (ADT. Biologically, they lead to elevated primary productivity and persistent aggregation of low-trophic-level prey. Unlike other remotely sensed variables, ADT provides information about the structure of the entire water column and it is also routinely measured at high spatial-temporal resolution by satellite altimeters with uniform global coverage. Our models provide spatially explicit population density predictions for both species, even in areas where the pycnocline shoals but does not outcrop (e.g. the Costa Rica Dome and the North Equatorial Countercurrent thermocline ridge. Interannual variations in distribution during El Niño anomalies suggest that the population density of both species decreases dramatically in the Equatorial Cold Tongue and the Costa Rica Dome, and that their distributions retract to particular areas that remain productive, such as the more oceanic waters in the central California Current System, the northern Gulf of California, the North Equatorial Countercurrent thermocline ridge, and the more
Pardo, Mario A; Gerrodette, Tim; Beier, Emilio; Gendron, Diane; Forney, Karin A; Chivers, Susan J; Barlow, Jay; Palacios, Daniel M
2015-01-01
We inferred the population densities of blue whales (Balaenoptera musculus) and short-beaked common dolphins (Delphinus delphis) in the Northeast Pacific Ocean as functions of the water-column's physical structure by implementing hierarchical models in a Bayesian framework. This approach allowed us to propagate the uncertainty of the field observations into the inference of species-habitat relationships and to generate spatially explicit population density predictions with reduced effects of sampling heterogeneity. Our hypothesis was that the large-scale spatial distributions of these two cetacean species respond primarily to ecological processes resulting from shoaling and outcropping of the pycnocline in regions of wind-forced upwelling and eddy-like circulation. Physically, these processes affect the thermodynamic balance of the water column, decreasing its volume and thus the height of the absolute dynamic topography (ADT). Biologically, they lead to elevated primary productivity and persistent aggregation of low-trophic-level prey. Unlike other remotely sensed variables, ADT provides information about the structure of the entire water column and it is also routinely measured at high spatial-temporal resolution by satellite altimeters with uniform global coverage. Our models provide spatially explicit population density predictions for both species, even in areas where the pycnocline shoals but does not outcrop (e.g. the Costa Rica Dome and the North Equatorial Countercurrent thermocline ridge). Interannual variations in distribution during El Niño anomalies suggest that the population density of both species decreases dramatically in the Equatorial Cold Tongue and the Costa Rica Dome, and that their distributions retract to particular areas that remain productive, such as the more oceanic waters in the central California Current System, the northern Gulf of California, the North Equatorial Countercurrent thermocline ridge, and the more southern portion of the
van der Meer, Aize Franciscus; Touw, Daniel J.; Marcus, Marco A. E.; Neef, Cornelis; Proost, Johannes H.
2012-01-01
Background: Observational data sets can be used for population pharmacokinetic (PK) modeling. However, these data sets are generally less precisely recorded than experimental data sets. This article aims to investigate the influence of erroneous records on population PK modeling and individual maxim
Bayesian estimates of male and female African lion mortality for future use in population management
DEFF Research Database (Denmark)
Barthold, Julia A; Loveridge, Andrew; Macdonald, David;
2016-01-01
1. The global population size of African lions is plummeting, and many small fragmented populations face local extinction. Extinction risks are amplified through the common practice of trophy hunting for males, which makes setting sustainable hunting quotas a vital task. 2. Various demographic...... models evaluate consequences of hunting on lion population growth. However, none of the models use unbiased estimates of male age-specific mortality because such estimates do not exist. Until now, estimating mortality from resighting records of marked males has been impossible due to the uncertain fates...... of disappeared individuals: dispersal or death. 3. We develop a new method and infer mortality for male and female lions from two popula- tions that are typical with respect to their experienced levels of human impact. 4. We found that mortality of both sexes differed between the populations and that males had...
Rodhouse, T.J.; Irvine, K.M.; Vierling, K.T.; Vierling, L.A.
2011-01-01
Monitoring programs that evaluate restoration and inform adaptive management are important for addressing environmental degradation. These efforts may be well served by spatially explicit hierarchical approaches to modeling because of unavoidable spatial structure inherited from past land use patterns and other factors. We developed Bayesian hierarchical models to estimate trends from annual density counts observed in a spatially structured wetland forb (Camassia quamash [camas]) population following the cessation of grazing and mowing on the study area, and in a separate reference population of camas. The restoration site was bisected by roads and drainage ditches, resulting in distinct subpopulations ("zones") with different land use histories. We modeled this spatial structure by fitting zone-specific intercepts and slopes. We allowed spatial covariance parameters in the model to vary by zone, as in stratified kriging, accommodating anisotropy and improving computation and biological interpretation. Trend estimates provided evidence of a positive effect of passive restoration, and the strength of evidence was influenced by the amount of spatial structure in the model. Allowing trends to vary among zones and accounting for topographic heterogeneity increased precision of trend estimates. Accounting for spatial autocorrelation shifted parameter coefficients in ways that varied among zones depending on strength of statistical shrinkage, autocorrelation and topographic heterogeneity-a phenomenon not widely described. Spatially explicit estimates of trend from hierarchical models will generally be more useful to land managers than pooled regional estimates and provide more realistic assessments of uncertainty. The ability to grapple with historical contingency is an appealing benefit of this approach.
DEFF Research Database (Denmark)
Martins, Bo; Forchhammer, Søren
1999-01-01
MPEG-2 video decoding is examined. A unified approach to quality improvement, chrominance upsampling, de-interlacing and superresolution is presented. The information over several frames is combined as part of the processing.......MPEG-2 video decoding is examined. A unified approach to quality improvement, chrominance upsampling, de-interlacing and superresolution is presented. The information over several frames is combined as part of the processing....
MASSIVE: A Bayesian analysis of giant planet populations around low-mass stars
Lannier, J; Lagrange, A M; Borgniet, S; Rameau, J; Schlieder, J E; Gagné, J; Bonavita, M A; Malo, L; Chauvin, G; Bonnefoy, M; Girard, J H
2016-01-01
Direct imaging has led to the discovery of several giant planet and brown dwarf companions. These imaged companions populate a mass, separation and age domain (mass>1MJup, orbits>5AU, age2MJup might be independent from the mass of the host star.
A Bayesian integrated population dynamics model to analyze data for protected species
Hoyle, S. D.; Maunder, M. N.
2004-01-01
Managing wildlife-human interactions demands reliable information about the likely consequences of management actions. This requirement is a general one, whatever the taxonomic group. We describe a method for estimating population dynamics and decision analysis that is generally applicable, extremely flexible, uses data efficiently, and gives answers in a useful format. Our case study involves bycatch of a protected species, the Northeastern Offshore Spotted Dolphin (Stenella attenuata), in t...
Directory of Open Access Journals (Sweden)
Thomas J Rodhouse
Full Text Available Monitoring programs that evaluate restoration and inform adaptive management are important for addressing environmental degradation. These efforts may be well served by spatially explicit hierarchical approaches to modeling because of unavoidable spatial structure inherited from past land use patterns and other factors. We developed bayesian hierarchical models to estimate trends from annual density counts observed in a spatially structured wetland forb (Camassia quamash [camas] population following the cessation of grazing and mowing on the study area, and in a separate reference population of camas. The restoration site was bisected by roads and drainage ditches, resulting in distinct subpopulations ("zones" with different land use histories. We modeled this spatial structure by fitting zone-specific intercepts and slopes. We allowed spatial covariance parameters in the model to vary by zone, as in stratified kriging, accommodating anisotropy and improving computation and biological interpretation. Trend estimates provided evidence of a positive effect of passive restoration, and the strength of evidence was influenced by the amount of spatial structure in the model. Allowing trends to vary among zones and accounting for topographic heterogeneity increased precision of trend estimates. Accounting for spatial autocorrelation shifted parameter coefficients in ways that varied among zones depending on strength of statistical shrinkage, autocorrelation and topographic heterogeneity--a phenomenon not widely described. Spatially explicit estimates of trend from hierarchical models will generally be more useful to land managers than pooled regional estimates and provide more realistic assessments of uncertainty. The ability to grapple with historical contingency is an appealing benefit of this approach.
Institute of Scientific and Technical Information of China (English)
Konstantinos ANGELIS; Mario DOS REIS
2015-01-01
Although the effects of the coalescent process on sequence divergence and genealogies are well understood, the vir-tual majority of studies that use molecular sequences to estimate times of divergence among species have failed to account for the coalescent process. Here we study the impact of ancestral population size and incomplete lineage sorting on Bayesian estimates of species divergence times under the molecular clock when the inference model ignores the coalescent process. Using a combi-nation of mathematical analysis, computer simulations and analysis of real data, we find that the errors on estimates of times and the molecular rate can be substantial when ancestral populations are large and when there is substantial incomplete lineage sort-ing. For example, in a simple three-species case, we find that if the most precise fossil calibration is placed on the root of the phylogeny, the age of the internal node is overestimated, while if the most precise calibration is placed on the internal node, then the age of the root is underestimated. In both cases, the molecular rate is overestimated. Using simulations on a phylogeny of nine species, we show that substantial errors in time and rate estimates can be obtained even when dating ancient divergence events. We analyse the hominoid phylogeny and show that estimates of the neutral mutation rate obtained while ignoring the coalescent are too high. Using a coalescent-based technique to obtain geological times of divergence, we obtain estimates of the mutation rate that are within experimental estimates and we also obtain substantially older divergence times within the phylogeny [Current Zoology 61 (5): 874–885, 2015].
Interpretability in Linear Brain Decoding
Kia, Seyed Mostafa; Passerini, Andrea
2016-01-01
Improving the interpretability of brain decoding approaches is of primary interest in many neuroimaging studies. Despite extensive studies of this type, at present, there is no formal definition for interpretability of brain decoding models. As a consequence, there is no quantitative measure for evaluating the interpretability of different brain decoding methods. In this paper, we present a simple definition for interpretability of linear brain decoding models. Then, we propose to combine the...
DEFF Research Database (Denmark)
Nielsen, Rasmus Refslund
2002-01-01
This paper describes an efficient decoding method for a recent construction of good linear codes as well as an extension to the construction. Furthermore, asymptotic properties and list decoding of the codes are discussed.......This paper describes an efficient decoding method for a recent construction of good linear codes as well as an extension to the construction. Furthermore, asymptotic properties and list decoding of the codes are discussed....
Bayesian Lensing Shear Measurement
Bernstein, Gary M
2013-01-01
We derive an estimator of weak gravitational lensing shear from background galaxy images that avoids noise-induced biases through a rigorous Bayesian treatment of the measurement. The Bayesian formalism requires a prior describing the (noiseless) distribution of the target galaxy population over some parameter space; this prior can be constructed from low-noise images of a subsample of the target population, attainable from long integrations of a fraction of the survey field. We find two ways to combine this exact treatment of noise with rigorous treatment of the effects of the instrumental point-spread function and sampling. The Bayesian model fitting (BMF) method assigns a likelihood of the pixel data to galaxy models (e.g. Sersic ellipses), and requires the unlensed distribution of galaxies over the model parameters as a prior. The Bayesian Fourier domain (BFD) method compresses galaxies to a small set of weighted moments calculated after PSF correction in Fourier space. It requires the unlensed distributi...
正态总体方差的贝叶斯区间估计%Normal Population Variance of the Bayesian Interval Estimation
Institute of Scientific and Technical Information of China (English)
王宜静; 华守亮
2011-01-01
区间估计是数理统计的一个重要内容，分析了贝叶斯可信区间估计与经典置信区间估计的区别，推出了正态总体方差的贝叶斯可信区间。数值分析表明可信区间比置信区间更精确。%interval estimation is an important mathematical statistics content, this paper Bayesian interval estimation and interval estimation of the difference between classic, introduced the normal population variance of the Bayesian confidence interval, numerical analysis shows that CI Confidence interval more accurate.
Kolb Ayre, Kimberley; Caldwell, Colleen A.; Stinson, Jonah; Landis, Wayne G.
2014-01-01
Introduction and spread of the parasite Myxobolus cerebralis, the causative agent of whirling disease, has contributed to the collapse of wild trout populations throughout the intermountain west. Of concern is the risk the disease may have on conservation and recovery of native cutthroat trout. We employed a Bayesian belief network to assess probability of whirling disease in Colorado River and Rio Grande cutthroat trout (Oncorhynchus clarkii pleuriticus and Oncorhynchus clarkii virginalis, respectively) within their current ranges in the southwest United States. Available habitat (as defined by gradient and elevation) for intermediate oligochaete worm host, Tubifex tubifex, exerted the greatest influence on the likelihood of infection, yet prevalence of stream barriers also affected the risk outcome. Management areas that had the highest likelihood of infected Colorado River cutthroat trout were in the eastern portion of their range, although the probability of infection was highest for populations in the southern, San Juan subbasin. Rio Grande cutthroat trout had a relatively low likelihood of infection, with populations in the southernmost Pecos management area predicted to be at greatest risk. The Bayesian risk assessment model predicted the likelihood of whirling disease infection from its principal transmission vector, fish movement, and suggested that barriers may be effective in reducing risk of exposure to native trout populations. Data gaps, especially with regard to location of spawning, highlighted the importance in developing monitoring plans that support future risk assessments and adaptive management for subspecies of cutthroat trout.
Nolan, L A; Kaban, Ata; Raychaudhuri, S
2006-01-01
We present the results of a novel application of Bayesian modelling techniques, which, although purely data driven, have a physically interpretable result, and will be useful as an efficient data mining tool. We base our studies on the UV-to-optical spectra (observed and synthetic) of early-type galaxies. A probabilistic latent variable architecture is formulated, and a rigorous Bayesian methodology is employed for solving the inverse modelling problem from the available data. A powerful aspect of our formalism is that it allows us to recover a limited fraction of missing data due to incomplete spectral coverage, as well as to handle observational errors in a principled way. We apply this method to a sample of 21 well-studied early-type spectra, with known star-formation histories. We find that our data-driven Bayesian modelling allows us to identify those early-types which contain a significant stellar population <~ 1 Gyr old. This method would therefore be a very useful tool for automatically discovering...
CERN. Geneva. Audiovisual Unit; Antonerakis, S E
2002-01-01
Decoding the Human genome is a very up-to-date topic, raising several questions besides purely scientific, in view of the two competing teams (public and private), the ethics of using the results, and the fact that the project went apparently faster and easier than expected. The lecture series will address the following chapters: Scientific basis and challenges. Ethical and social aspects of genomics.
Decoding Stacked Denoising Autoencoders
Sonoda, Sho; Murata, Noboru
2016-01-01
Data representation in a stacked denoising autoencoder is investigated. Decoding is a simple technique for translating a stacked denoising autoencoder into a composition of denoising autoencoders in the ground space. In the infinitesimal limit, a composition of denoising autoencoders is reduced to a continuous denoising autoencoder, which is rich in analytic properties and geometric interpretation. For example, the continuous denoising autoencoder solves the backward heat equation and transpo...
Decoding the Golay code with Venn diagrams
Blaum, Mario; Bruck, Jehoshua
1990-01-01
A decoding algorithm, based on Venn diagrams, for decoding the [23, 12, 7] Golay code is presented. The decoding algorithm is based on the design properties of the parity sets of the code. As for other decoding algorithms for the Golay code, decoding can be easily done by hand.
Decoding Neuronal Ensembles in the Human Hippocampus
Hassabis, D; Chu, C; Rees, G.; Weiskopf, N.; Molyneux, P.D.; Maguire, E. A.
2009-01-01
Summary Background The hippocampus underpins our ability to navigate, to form and recollect memories, and to imagine future experiences. How activity across millions of hippocampal neurons supports these functions is a fundamental question in neuroscience, wherein the size, sparseness, and organization of the hippocampal neural code are debated. Results Here, by using multivariate pattern classification and high spatial resolution functional MRI, we decoded activity across the population of n...
Comparing offline decoding performance in physiologically defined neuronal classes
Best, Matthew D.; Takahashi, Kazutaka; Suminski, Aaron J.; Ethier, Christian; Miller, Lee E.; Hatsopoulos, Nicholas G.
2016-04-01
Objective: Recently, several studies have documented the presence of a bimodal distribution of spike waveform widths in primary motor cortex. Although narrow and wide spiking neurons, corresponding to the two modes of the distribution, exhibit different response properties, it remains unknown if these differences give rise to differential decoding performance between these two classes of cells. Approach: We used a Gaussian mixture model to classify neurons into narrow and wide physiological classes. Using similar-size, random samples of neurons from these two physiological classes, we trained offline decoding models to predict a variety of movement features. We compared offline decoding performance between these two physiologically defined populations of cells. Main results: We found that narrow spiking neural ensembles decode motor parameters better than wide spiking neural ensembles including kinematics, kinetics, and muscle activity. Significance: These findings suggest that the utility of neural ensembles in brain machine interfaces may be predicted from their spike waveform widths.
Lesaffre, Emmanuel
2012-01-01
The growth of biostatistics has been phenomenal in recent years and has been marked by considerable technical innovation in both methodology and computational practicality. One area that has experienced significant growth is Bayesian methods. The growing use of Bayesian methodology has taken place partly due to an increasing number of practitioners valuing the Bayesian paradigm as matching that of scientific discovery. In addition, computational advances have allowed for more complex models to be fitted routinely to realistic data sets. Through examples, exercises and a combination of introd
Draper, D.
2001-01-01
© 2012 Springer Science+Business Media, LLC. All rights reserved. Article Outline: Glossary Definition of the Subject and Introduction The Bayesian Statistical Paradigm Three Examples Comparison with the Frequentist Statistical Paradigm Future Directions Bibliography
Kechagia, Irene-Ariadne; Dokoumetzidis, Aristides
2015-06-01
A preliminary population pharmacokinetic (PopPK) model of valacyclovir in children was developed from non-compartmental analysis (NCA) parameter values from literature, including several age groups, combined with Bayesian priors from a PopPK model of acyclovir, the active metabolite of valacyclovir, from literature too. Also a simulation study was carried out to evaluate the performance of various modelling choices related to the estimation of model parameters from NCA parameters originating from sparse PK studies. Assuming a one-compartment model with first order absorption, a mixed effects, meta-analysis approach was utilized which allows accounting the random intergroup variability, the detection of covariates and the application of informative Bayesian priors on the parameters. The conclusions from the simulation study calculating bias and precision for various cases, were that a model which takes explicitly into account the sampling schedule, performs better than a model using the theoretical expressions of calculating the NCA parameters. Also by using the geometric rather than the arithmetic means of NCA parameters, less biased results are obtained. These findings guided the choices for the valacyclovir model, for which informative priors from a PopPK model of acyclovir were applied for some of the parameters, in order to include a richer covariate model for clearance, not supported by the NCA dataset and a value for bioavailability. This preliminary valacyclovir model can be used in simulations to provide dosage recommendations for children of various ages and to help design more efficiently prospective clinical trials. PMID:25821006
Astrophysics Decoding the cosmos
Irwin, Judith A
2007-01-01
Astrophysics: Decoding the Cosmos is an accessible introduction to the key principles and theories underlying astrophysics. This text takes a close look at the radiation and particles that we receive from astronomical objects, providing a thorough understanding of what this tells us, drawing the information together using examples to illustrate the process of astrophysics. Chapters dedicated to objects showing complex processes are written in an accessible manner and pull relevant background information together to put the subject firmly into context. The intention of the author is that the book will be a 'tool chest' for undergraduate astronomers wanting to know the how of astrophysics. Students will gain a thorough grasp of the key principles, ensuring that this often-difficult subject becomes more accessible.
The Formal Specifications for Protocols of Decoders
Institute of Scientific and Technical Information of China (English)
YUAN Meng-ting; WU Guo-qing; SHU Feng-di
2004-01-01
This paper presents a formal approach, FSPD (Formal Specifications for Protocols of Decoders), to specify decoder communication protocols. Based on axiomatic, FSPD is a precise language with which programmers could use only one suitable driver to handle various types of decoders. FSPD is helpful for programmers to get high adaptability and reusability of decoder-driver software.
Decoding by Embedding: Correct Decoding Radius and DMT Optimality
Ling, Cong; Luzzi, Laura; Stehle, Damien
2011-01-01
In lattice-coded multiple-input multiple-output (MIMO) systems, optimal decoding amounts to solving the closest vector problem (CVP). Embedding is a powerful technique for the approximate CVP, yet its remarkable performance is not well understood. In this paper, we analyze the embedding technique from a bounded distance decoding (BDD) viewpoint. We prove that the Lenstra, Lenstra and Lov\\'az (LLL) algorithm can achieve 1/(2{\\gamma}) -BDD for {\\gamma} \\approx O(2^(n/4)), yielding a polynomial-complexity decoding algorithm performing exponentially better than Babai's which achieves {\\gamma} = O(2^(n/2)). This substantially improves the existing result {\\gamma} = O(2^(n)) for embedding decoding. We also prove that BDD of the regularized lattice is optimal in terms of the diversity-multiplexing gain tradeoff (DMT).
Decoding OvTDM with sphere-decoding algorithm
Institute of Scientific and Technical Information of China (English)
无
2008-01-01
Overlapped time division multiplexing (OvTDM) is a new type of transmission scheme with high spectrum efficiency and low threshold signal-to-noise ratio (SNR). In this article, the structure of OvTDM is introduced and the sphere-decoding algorithm of complex domain is proposed for OvTDM. Simulations demonstrate that the proposed algorithm can achieve maximum likelihood (ML) decoding with lower complexity as compared to traditional maximum likelihood sequence demodulation (MLSD) or viterbi algorithm (VA).
Directory of Open Access Journals (Sweden)
Julie Vercelloni
Full Text Available Recently, attempts to improve decision making in species management have focussed on uncertainties associated with modelling temporal fluctuations in populations. Reducing model uncertainty is challenging; while larger samples improve estimation of species trajectories and reduce statistical errors, they typically amplify variability in observed trajectories. In particular, traditional modelling approaches aimed at estimating population trajectories usually do not account well for nonlinearities and uncertainties associated with multi-scale observations characteristic of large spatio-temporal surveys. We present a Bayesian semi-parametric hierarchical model for simultaneously quantifying uncertainties associated with model structure and parameters, and scale-specific variability over time. We estimate uncertainty across a four-tiered spatial hierarchy of coral cover from the Great Barrier Reef. Coral variability is well described; however, our results show that, in the absence of additional model specifications, conclusions regarding coral trajectories become highly uncertain when considering multiple reefs, suggesting that management should focus more at the scale of individual reefs. The approach presented facilitates the description and estimation of population trajectories and associated uncertainties when variability cannot be attributed to specific causes and origins. We argue that our model can unlock value contained in large-scale datasets, provide guidance for understanding sources of uncertainty, and support better informed decision making.
Directory of Open Access Journals (Sweden)
Stephen J Jacquemin
Full Text Available We combine evolutionary biology and community ecology to test whether two species traits, body size and geographic range, explain long term variation in local scale freshwater stream fish assemblages. Body size and geographic range are expected to influence several aspects of fish ecology, via relationships with niche breadth, dispersal, and abundance. These traits are expected to scale inversely with niche breadth or current abundance, and to scale directly with dispersal potential. However, their utility to explain long term temporal patterns in local scale abundance is not known. Comparative methods employing an existing molecular phylogeny were used to incorporate evolutionary relatedness in a test for covariation of body size and geographic range with long term (1983 - 2010 local scale population variation of fishes in West Fork White River (Indiana, USA. The Bayesian model incorporating phylogenetic uncertainty and correlated predictors indicated that neither body size nor geographic range explained significant variation in population fluctuations over a 28 year period. Phylogenetic signal data indicated that body size and geographic range were less similar among taxa than expected if trait evolution followed a purely random walk. We interpret this as evidence that local scale population variation may be influenced less by species-level traits such as body size or geographic range, and instead may be influenced more strongly by a taxon's local scale habitat and biotic assemblages.
List Decoding of Algebraic Codes
DEFF Research Database (Denmark)
Nielsen, Johan Sebastian Rosenkilde
We investigate three paradigms for polynomial-time decoding of Reed–Solomon codes beyond half the minimum distance: the Guruswami–Sudan algorithm, Power decoding and the Wu algorithm. The main results concern shaping the computational core of all three methods to a problem solvable by module...... minimisation; by applying the fastest known algorithms for this general problem, we then obtain realisations of each paradigm which are as fast or faster than all previously known methods. An element of this is the “2D key equation”, a heavily generalised form of the classical key equation, and we show how...... to solve such using module minimisation, or using our new Demand–Driven algorithm which is also based on module minimisation. The decoding paradigms are all derived and analysed in a self-contained manner, often in new ways or examined in greater depth than previously. Among a number of new results, we...
Gentaz, Edouard; Sprenger-Charolles, Liliane; Theurel, Anne
2015-01-01
Based on the assumption that good decoding skills constitute a bootstrapping mechanism for reading comprehension, the present study investigated the relative contribution of the former skill to the latter compared to that of three other predictors of reading comprehension (listening comprehension, vocabulary and phonemic awareness) in 392 French-speaking first graders from low SES families. This large sample was split into three groups according to their level of decoding skills assessed by pseudoword reading. Using a cutoff of 1 SD above or below the mean of the entire population, there were 63 good decoders, 267 average decoders and 62 poor decoders. 58% of the variance in reading comprehension was explained by our four predictors, with decoding skills proving to be the best predictor (12.1%, 7.3% for listening comprehension, 4.6% for vocabulary and 3.3% for phonemic awareness). Interaction between group versus decoding skills, listening comprehension and phonemic awareness accounted for significant additional variance (3.6%, 1.1% and 1.0%, respectively). The effects on reading comprehension of decoding skills and phonemic awareness were higher in poor and average decoders than in good decoders whereas listening comprehension accounted for more variance in good and average decoders than in poor decoders. Furthermore, the percentage of children with impaired reading comprehension skills was higher in the group of poor decoders (55%) than in the two other groups (average decoders: 7%; good decoders: 0%) and only 6 children (1.5%) had impaired reading comprehension skills with unimpaired decoding skills, listening comprehension or vocabulary. These results challenge the outcomes of studies on "poor comprehenders" by showing that, at least in first grade, poor reading comprehension is strongly linked to the level of decoding skills.
Directory of Open Access Journals (Sweden)
Edouard Gentaz
Full Text Available Based on the assumption that good decoding skills constitute a bootstrapping mechanism for reading comprehension, the present study investigated the relative contribution of the former skill to the latter compared to that of three other predictors of reading comprehension (listening comprehension, vocabulary and phonemic awareness in 392 French-speaking first graders from low SES families. This large sample was split into three groups according to their level of decoding skills assessed by pseudoword reading. Using a cutoff of 1 SD above or below the mean of the entire population, there were 63 good decoders, 267 average decoders and 62 poor decoders. 58% of the variance in reading comprehension was explained by our four predictors, with decoding skills proving to be the best predictor (12.1%, 7.3% for listening comprehension, 4.6% for vocabulary and 3.3% for phonemic awareness. Interaction between group versus decoding skills, listening comprehension and phonemic awareness accounted for significant additional variance (3.6%, 1.1% and 1.0%, respectively. The effects on reading comprehension of decoding skills and phonemic awareness were higher in poor and average decoders than in good decoders whereas listening comprehension accounted for more variance in good and average decoders than in poor decoders. Furthermore, the percentage of children with impaired reading comprehension skills was higher in the group of poor decoders (55% than in the two other groups (average decoders: 7%; good decoders: 0% and only 6 children (1.5% had impaired reading comprehension skills with unimpaired decoding skills, listening comprehension or vocabulary. These results challenge the outcomes of studies on "poor comprehenders" by showing that, at least in first grade, poor reading comprehension is strongly linked to the level of decoding skills.
Montijn, Jorrit S; Vinck, Martin; Pennartz, Cyriel M A
2014-01-01
The primary visual cortex is an excellent model system for investigating how neuronal populations encode information, because of well-documented relationships between stimulus characteristics and neuronal activation patterns. We used two-photon calcium imaging data to relate the performance of different methods for studying population coding (population vectors, template matching, and Bayesian decoding algorithms) to their underlying assumptions. We show that the variability of neuronal responses may hamper the decoding of population activity, and that a normalization to correct for this variability may be of critical importance for correct decoding of population activity. Second, by comparing noise correlations and stimulus tuning we find that these properties have dissociated anatomical correlates, even though noise correlations have been previously hypothesized to reflect common synaptic input. We hypothesize that noise correlations arise from large non-specific increases in spiking activity acting on many weak synapses simultaneously, while neuronal stimulus response properties are dependent on more reliable connections. Finally, this paper provides practical guidelines for further research on population coding and shows that population coding cannot be approximated by a simple summation of inputs, but is heavily influenced by factors such as input reliability and noise correlation structure.
McCune, Jeannine S.; Bemer, Meagan J.; Barrett, Jeffrey S.; Baker, K. Scott; Gamis, Alan S.; Holford, Nicholas H.G.
2014-01-01
Purpose Personalizing intravenous (IV) busulfan doses to a target plasma concentration at steady state (Css) is an essential component of hematopoietic cell transplantation (HCT). We sought to develop a population pharmacokinetic model to predict IV busulfan doses over a wide age spectrum (0.1 – 66 years) that accounts for differences in age and body size. Experimental design A population pharmacokinetic model based on normal fat mass and maturation based on post-menstrual age was built from 12,380 busulfan concentration-time points obtained after IV busulfan administration in 1,610 HCT recipients. Subsequently, simulation results of the initial dose necessary to achieve a target Css with this model were compared with pediatric-only models. Results A two-compartment model with first-order elimination best fit the data. The population busulfan clearance was 12.4 L/h for an adult male with 62kg normal fat mass (equivalent to 70kg total body weight). Busulfan clearance, scaled to body size – specifically normal fat mass, is predicted to be 95% of the adult clearance at 2.5 years post-natal age. With a target Css of 770 ng/mL, a higher proportion of initial doses achieved the therapeutic window with this age- and size-dependent model (72%) compared to dosing recommended by the Food and Drug Administration (57%) or the European Medicines Agency (70%). Conclusion This is the first population pharmacokinetic model developed to predict initial IV busulfan doses and personalize to a target Css over a wide age spectrum, ranging from infants to adults. PMID:24218510
Soft-decision decoding of RS codes
DEFF Research Database (Denmark)
Justesen, Jørn
2005-01-01
By introducing a few simplifying assumptions we derive a simple condition for successful decoding using the Koetter-Vardy algorithm for soft-decision decoding of RS codes. We show that the algorithm has a significant advantage over hard decision decoding when the code rate is low, when two or more...
Social Intelligence and Decoding of Nonverbal Cues.
Barnes, Michael L.; Sternberg, Robert J.
1989-01-01
The relationship between non-verbal decoding ability and social intelligence, defined as the ability to decode social information accurately, was studied using 40 adults. Results are discussed in the framework of R. J. Sternberg's triarchic theory of human intelligence. Decoding skills appeared to be an important part of social intelligence. (SLD)
Bessiere, Pierre; Ahuactzin, Juan Manuel; Mekhnacha, Kamel
2013-01-01
Probability as an Alternative to Boolean LogicWhile logic is the mathematical foundation of rational reasoning and the fundamental principle of computing, it is restricted to problems where information is both complete and certain. However, many real-world problems, from financial investments to email filtering, are incomplete or uncertain in nature. Probability theory and Bayesian computing together provide an alternative framework to deal with incomplete and uncertain data. Decision-Making Tools and Methods for Incomplete and Uncertain DataEmphasizing probability as an alternative to Boolean
SYNTHESIZED EXPECTED BAYESIAN METHOD OF PARAMETRIC ESTIMATE
Institute of Scientific and Technical Information of China (English)
Ming HAN; Yuanyao DING
2004-01-01
This paper develops a new method of parametric estimate, which is named as "synthesized expected Bayesian method". When samples of products are tested and no failure events occur, thedefinition of expected Bayesian estimate is introduced and the estimates of failure probability and failure rate are provided. After some failure information is introduced by making an extra-test, a synthesized expected Bayesian method is defined and used to estimate failure probability, failure rateand some other parameters in exponential distribution and Weibull distribution of populations. Finally,calculations are performed according to practical problems, which show that the synthesized expected Bayesian method is feasible and easy to operate.
Decoding intention at sensorimotor timescales.
Directory of Open Access Journals (Sweden)
Mathew Salvaris
Full Text Available The ability to decode an individual's intentions in real time has long been a 'holy grail' of research on human volition. For example, a reliable method could be used to improve scientific study of voluntary action by allowing external probe stimuli to be delivered at different moments during development of intention and action. Several Brain Computer Interface applications have used motor imagery of repetitive actions to achieve this goal. These systems are relatively successful, but only if the intention is sustained over a period of several seconds; much longer than the timescales identified in psychophysiological studies for normal preparation for voluntary action. We have used a combination of sensorimotor rhythms and motor imagery training to decode intentions in a single-trial cued-response paradigm similar to those used in human and non-human primate motor control research. Decoding accuracy of over 0.83 was achieved with twelve participants. With this approach, we could decode intentions to move the left or right hand at sub-second timescales, both for instructed choices instructed by an external stimulus and for free choices generated intentionally by the participant. The implications for volition are considered.
Dynamics of intracellular information decoding
Kobayashi, Tetsuya J.; Kamimura, Atsushi
2011-10-01
A variety of cellular functions are robust even to substantial intrinsic and extrinsic noise in intracellular reactions and the environment that could be strong enough to impair or limit them. In particular, of substantial importance is cellular decision-making in which a cell chooses a fate or behavior on the basis of information conveyed in noisy external signals. For robust decoding, the crucial step is filtering out the noise inevitably added during information transmission. As a minimal and optimal implementation of such an information decoding process, the autocatalytic phosphorylation and autocatalytic dephosphorylation (aPadP) cycle was recently proposed. Here, we analyze the dynamical properties of the aPadP cycle in detail. We describe the dynamical roles of the stationary and short-term responses in determining the efficiency of information decoding and clarify the optimality of the threshold value of the stationary response and its information-theoretical meaning. Furthermore, we investigate the robustness of the aPadP cycle against the receptor inactivation time and intrinsic noise. Finally, we discuss the relationship among information decoding with information-dependent actions, bet-hedging and network modularity.
Bayesian demography 250 years after Bayes.
Bijak, Jakub; Bryant, John
2016-01-01
Bayesian statistics offers an alternative to classical (frequentist) statistics. It is distinguished by its use of probability distributions to describe uncertain quantities, which leads to elegant solutions to many difficult statistical problems. Although Bayesian demography, like Bayesian statistics more generally, is around 250 years old, only recently has it begun to flourish. The aim of this paper is to review the achievements of Bayesian demography, address some misconceptions, and make the case for wider use of Bayesian methods in population studies. We focus on three applications: demographic forecasts, limited data, and highly structured or complex models. The key advantages of Bayesian methods are the ability to integrate information from multiple sources and to describe uncertainty coherently. Bayesian methods also allow for including additional (prior) information next to the data sample. As such, Bayesian approaches are complementary to many traditional methods, which can be productively re-expressed in Bayesian terms. PMID:26902889
Bayesian demography 250 years after Bayes.
Bijak, Jakub; Bryant, John
2016-01-01
Bayesian statistics offers an alternative to classical (frequentist) statistics. It is distinguished by its use of probability distributions to describe uncertain quantities, which leads to elegant solutions to many difficult statistical problems. Although Bayesian demography, like Bayesian statistics more generally, is around 250 years old, only recently has it begun to flourish. The aim of this paper is to review the achievements of Bayesian demography, address some misconceptions, and make the case for wider use of Bayesian methods in population studies. We focus on three applications: demographic forecasts, limited data, and highly structured or complex models. The key advantages of Bayesian methods are the ability to integrate information from multiple sources and to describe uncertainty coherently. Bayesian methods also allow for including additional (prior) information next to the data sample. As such, Bayesian approaches are complementary to many traditional methods, which can be productively re-expressed in Bayesian terms.
Variational bayesian method of estimating variance components.
Arakawa, Aisaku; Taniguchi, Masaaki; Hayashi, Takeshi; Mikawa, Satoshi
2016-07-01
We developed a Bayesian analysis approach by using a variational inference method, a so-called variational Bayesian method, to determine the posterior distributions of variance components. This variational Bayesian method and an alternative Bayesian method using Gibbs sampling were compared in estimating genetic and residual variance components from both simulated data and publically available real pig data. In the simulated data set, we observed strong bias toward overestimation of genetic variance for the variational Bayesian method in the case of low heritability and low population size, and less bias was detected with larger population sizes in both methods examined. The differences in the estimates of variance components between the variational Bayesian and the Gibbs sampling were not found in the real pig data. However, the posterior distributions of the variance components obtained with the variational Bayesian method had shorter tails than those obtained with the Gibbs sampling. Consequently, the posterior standard deviations of the genetic and residual variances of the variational Bayesian method were lower than those of the method using Gibbs sampling. The computing time required was much shorter with the variational Bayesian method than with the method using Gibbs sampling.
On Decoding Interleaved Chinese Remainder Codes
DEFF Research Database (Denmark)
Li, Wenhui; Sidorenko, Vladimir; Nielsen, Johan Sebastian Rosenkilde
2013-01-01
We model the decoding of Interleaved Chinese Remainder codes as that of finding a short vector in a Z-lattice. Using the LLL algorithm, we obtain an efficient decoding algorithm, correcting errors beyond the unique decoding bound and having nearly linear complexity. The algorithm can fail...... with a probability dependent on the number of errors, and we give an upper bound for this. Simulation results indicate that the bound is close to the truth. We apply the proposed decoding algorithm for decoding a single CR code using the idea of “Power” decoding, suggested for Reed-Solomon codes. A combination...... of these two methods can be used to decode low-rate Interleaved Chinese Remainder codes....
GENETIC ALGORITHM FOR DECODING LINEAR CODES OVER AWGN AND FADING CHANNELS
Directory of Open Access Journals (Sweden)
H. BERBIA
2011-08-01
Full Text Available This paper introduces a decoder for binary linear codes based on Genetic Algorithm (GA over the Gaussian and Rayleigh flat fading channel. The performances and compututional complexity of our decoder applied to BCH and convolutional codes are good compared to Chase-2 and Viterbi algorithm respectively. It show that our algorithm is less complex for linear block codes of large block length; furthermore it's performances can be improved by tuning the decoder's parameters, in particular the number of individuals by population and the number of generations
Frequency-Accommodating Manchester Decoder
Vasquez, Mario J.
1988-01-01
No adjustment necessary to cover a 10:1 frequency range. Decoding circuit converts biphase-level pulse-code modulation to nonreturn-to-zero (NRZ)-level pulse-code modulation plus clock signal. Circuit accommodates input data rate of 50 to 500 kb/s. Tracks gradual changes in rate automatically, eliminating need for extra circuits and manual switching to adjust to different rates.
Fingerprinting with Minimum Distance Decoding
Lin, Shih-Chun; Gamal, Hesham El
2007-01-01
This work adopts an information theoretic framework for the design of collusion-resistant coding/decoding schemes for digital fingerprinting. More specifically, the minimum distance decision rule is used to identify 1 out of t pirates. Achievable rates, under this detection rule, are characterized in two distinct scenarios. First, we consider the averaging attack where a random coding argument is used to show that the rate 1/2 is achievable with t=2 pirates. Our study is then extended to the general case of arbitrary $t$ highlighting the underlying complexity-performance tradeoff. Overall, these results establish the significant performance gains offered by minimum distance decoding as compared to other approaches based on orthogonal codes and correlation detectors. In the second scenario, we characterize the achievable rates, with minimum distance decoding, under any collusion attack that satisfies the marking assumption. For t=2 pirates, we show that the rate $1-H(0.25)\\approx 0.188$ is achievable using an ...
Towards joint decoding of Tardos fingerprinting codes
Meerwald, Peter
2011-01-01
The class of joint decoder of probabilistic fingerprinting codes is of utmost importance in theoretical papers to establish the concept of fingerprint capacity. However, no implementation supporting a large user base is known to date. This paper presents an iterative decoder which is, as far as we are aware of, the first practical attempt towards joint decoding. The discriminative feature of the scores benefits on one hand from the side-information of previously accused users, and on the other hand, from recently introduced universal linear decoders for compound channels. Neither the code construction nor the decoder make precise assumptions about the collusion (size or strategy). The extension to incorporate soft outputs from the watermarking layer is straightforward. An intensive experimental work benchmarks the very good performances and offers a clear comparison with previous state-of-the-art decoders.
A class of Sudan-decodable codes
DEFF Research Database (Denmark)
Nielsen, Rasmus Refslund
2000-01-01
In this article, Sudan's algorithm is modified into an efficient method to list-decode a class of codes which can be seen as a generalization of Reed-Solomon codes. The algorithm is specialized into a very efficient method for unique decoding. The code construction can be generalized based...... on algebraic-geometry codes and the decoding algorithms are generalized accordingly. Comparisons with Reed-Solomon and Hermitian codes are made....
Introduction to Bayesian statistics
Bolstad, William M
2016-01-01
There is a strong upsurge in the use of Bayesian methods in applied statistical analysis, yet most introductory statistics texts only present frequentist methods. Bayesian statistics has many important advantages that students should learn about if they are going into fields where statistics will be used. In this Third Edition, four newly-added chapters address topics that reflect the rapid advances in the field of Bayesian staistics. The author continues to provide a Bayesian treatment of introductory statistical topics, such as scientific data gathering, discrete random variables, robust Bayesian methods, and Bayesian approaches to inferenfe cfor discrete random variables, bionomial proprotion, Poisson, normal mean, and simple linear regression. In addition, newly-developing topics in the field are presented in four new chapters: Bayesian inference with unknown mean and variance; Bayesian inference for Multivariate Normal mean vector; Bayesian inference for Multiple Linear RegressionModel; and Computati...
Turbo-DFH Coding and Iterative Decoding
Institute of Scientific and Technical Information of China (English)
PEI Xiao-dong; HE Zun-wen; KUANG Jing-ming
2006-01-01
We combine the Turbo codes with differential frequency hopping (DFH) technique and propose a new technique called Turbo-DFH coding. The structures of Turbo-DFH encoder and decoder are given. Taking aim at characteristic of Turbo-DFH system, the modified Log-MAP algorithm is presented and used for iterative decoding of Turbo-DFH decoder. Simulation results show that because the ideas of random coding and iterative decoding are used, the bit error rate (BER) performance of the Turbo-DFH system is better than that of the conventional DFH system.
Application of Beyond Bound Decoding for High Speed Optical Communications
Li, Bomin; Larsen, Knud J.; Vegas Olmos, Juan José; Zibar, Darko; Tafur Monroy, Idelfonso
2013-01-01
This paper studies the application of beyond bound decoding method for high speed optical communications. This hard-decision decoding method outperforms traditional minimum distance decoding method, with a total net coding gain of 10.36 dB.
Bayesian artificial intelligence
Korb, Kevin B
2003-01-01
As the power of Bayesian techniques has become more fully realized, the field of artificial intelligence has embraced Bayesian methodology and integrated it to the point where an introduction to Bayesian techniques is now a core course in many computer science programs. Unlike other books on the subject, Bayesian Artificial Intelligence keeps mathematical detail to a minimum and covers a broad range of topics. The authors integrate all of Bayesian net technology and learning Bayesian net technology and apply them both to knowledge engineering. They emphasize understanding and intuition but also provide the algorithms and technical background needed for applications. Software, exercises, and solutions are available on the authors' website.
Bayesian artificial intelligence
Korb, Kevin B
2010-01-01
Updated and expanded, Bayesian Artificial Intelligence, Second Edition provides a practical and accessible introduction to the main concepts, foundation, and applications of Bayesian networks. It focuses on both the causal discovery of networks and Bayesian inference procedures. Adopting a causal interpretation of Bayesian networks, the authors discuss the use of Bayesian networks for causal modeling. They also draw on their own applied research to illustrate various applications of the technology.New to the Second EditionNew chapter on Bayesian network classifiersNew section on object-oriente
DEFF Research Database (Denmark)
Dashab, Golam Reza; Kadri, Naveen Kumar; Mahdi Shariati, Mohammad;
2012-01-01
) Mixed model analysis (MMA), 2) Random haplotype model (RHM), 3) Genealogy-based mixed model (GENMIX), and 4) Bayesian variable selection (BVS). The data consisted of phenotypes of 2000 animals from 20 sire families and were genotyped with 9990 SNPs on five chromosomes. Results: Out of the eight...
Application of Beyond Bound Decoding for High Speed Optical Communications
DEFF Research Database (Denmark)
Li, Bomin; Larsen, Knud J.; Vegas Olmos, Juan José;
2013-01-01
This paper studies the application of beyond bound decoding method for high speed optical communications. This hard-decision decoding method outperforms traditional minimum distance decoding method, with a total net coding gain of 10.36 dB.......This paper studies the application of beyond bound decoding method for high speed optical communications. This hard-decision decoding method outperforms traditional minimum distance decoding method, with a total net coding gain of 10.36 dB....
Statistical coding and decoding of heartbeat intervals.
Directory of Open Access Journals (Sweden)
Fausto Lucena
Full Text Available The heart integrates neuroregulatory messages into specific bands of frequency, such that the overall amplitude spectrum of the cardiac output reflects the variations of the autonomic nervous system. This modulatory mechanism seems to be well adjusted to the unpredictability of the cardiac demand, maintaining a proper cardiac regulation. A longstanding theory holds that biological organisms facing an ever-changing environment are likely to evolve adaptive mechanisms to extract essential features in order to adjust their behavior. The key question, however, has been to understand how the neural circuitry self-organizes these feature detectors to select behaviorally relevant information. Previous studies in computational perception suggest that a neural population enhances information that is important for survival by minimizing the statistical redundancy of the stimuli. Herein we investigate whether the cardiac system makes use of a redundancy reduction strategy to regulate the cardiac rhythm. Based on a network of neural filters optimized to code heartbeat intervals, we learn a population code that maximizes the information across the neural ensemble. The emerging population code displays filter tuning proprieties whose characteristics explain diverse aspects of the autonomic cardiac regulation, such as the compromise between fast and slow cardiac responses. We show that the filters yield responses that are quantitatively similar to observed heart rate responses during direct sympathetic or parasympathetic nerve stimulation. Our findings suggest that the heart decodes autonomic stimuli according to information theory principles analogous to how perceptual cues are encoded by sensory systems.
On minimizing the maximum broadcast decoding delay for instantly decodable network coding
Douik, Ahmed S.
2014-09-01
In this paper, we consider the problem of minimizing the maximum broadcast decoding delay experienced by all the receivers of generalized instantly decodable network coding (IDNC). Unlike the sum decoding delay, the maximum decoding delay as a definition of delay for IDNC allows a more equitable distribution of the delays between the different receivers and thus a better Quality of Service (QoS). In order to solve this problem, we first derive the expressions for the probability distributions of maximum decoding delay increments. Given these expressions, we formulate the problem as a maximum weight clique problem in the IDNC graph. Although this problem is known to be NP-hard, we design a greedy algorithm to perform effective packet selection. Through extensive simulations, we compare the sum decoding delay and the max decoding delay experienced when applying the policies to minimize the sum decoding delay and our policy to reduce the max decoding delay. Simulations results show that our policy gives a good agreement among all the delay aspects in all situations and outperforms the sum decoding delay policy to effectively minimize the sum decoding delay when the channel conditions become harsher. They also show that our definition of delay significantly improve the number of served receivers when they are subject to strict delay constraints.
Blending Wheels: Tools for Decoding Practice
Lane, Holly; Pullen, Paige Cullen
2015-01-01
Decoding practice significantly improves students' reading proficiency and is particularly beneficial for those who have or who are at risk for reading difficulties. Finding effective ways to provide decoding practice for struggling readers can be a challenge for teachers. Still, this goal is essential for developing reading proficiency. The…
Decomposition Methods for Large Scale LP Decoding
Barman, Siddharth; Draper, Stark C; Recht, Benjamin
2012-01-01
When binary linear error-correcting codes are used over symmetric channels, a relaxed version of the maximum likelihood decoding problem can be stated as a linear program (LP). This LP decoder can be used to decode at bit-error-rates comparable to state-of-the-art belief propagation (BP) decoders, but with significantly stronger theoretical guarantees. However, LP decoding when implemented with standard LP solvers does not easily scale to the block lengths of modern error correcting codes. In this paper we draw on decomposition methods from optimization theory, specifically the Alternating Directions Method of Multipliers (ADMM), to develop efficient distributed algorithms for LP decoding. The key enabling technical result is a nearly linear time algorithm for two-norm projection onto the parity polytope. This allows us to use LP decoding, with all its theoretical guarantees, to decode large-scale error correcting codes efficiently. We present numerical results for two LDPC codes. The first is the rate-0.5 [2...
Gelman, Andrew; Stern, Hal S; Dunson, David B; Vehtari, Aki; Rubin, Donald B
2013-01-01
FUNDAMENTALS OF BAYESIAN INFERENCEProbability and InferenceSingle-Parameter Models Introduction to Multiparameter Models Asymptotics and Connections to Non-Bayesian ApproachesHierarchical ModelsFUNDAMENTALS OF BAYESIAN DATA ANALYSISModel Checking Evaluating, Comparing, and Expanding ModelsModeling Accounting for Data Collection Decision AnalysisADVANCED COMPUTATION Introduction to Bayesian Computation Basics of Markov Chain Simulation Computationally Efficient Markov Chain Simulation Modal and Distributional ApproximationsREGRESSION MODELS Introduction to Regression Models Hierarchical Linear
Yuan, Ying; MacKinnon, David P.
2009-01-01
This article proposes Bayesian analysis of mediation effects. Compared to conventional frequentist mediation analysis, the Bayesian approach has several advantages. First, it allows researchers to incorporate prior information into the mediation analysis, thus potentially improving the efficiency of estimates. Second, under the Bayesian mediation analysis, inference is straightforward and exact, which makes it appealing for studies with small samples. Third, the Bayesian approach is conceptua...
Bayesian Games with Intentions
Bjorndahl, Adam; Halpern, Joseph Y.; Pass, Rafael
2016-01-01
We show that standard Bayesian games cannot represent the full spectrum of belief-dependent preferences. However, by introducing a fundamental distinction between intended and actual strategies, we remove this limitation. We define Bayesian games with intentions, generalizing both Bayesian games and psychological games, and prove that Nash equilibria in psychological games correspond to a special class of equilibria as defined in our setting.
Bayesian Classification in Medicine: The Transferability Question *
Zagoria, Ronald J.; Reggia, James A.; Price, Thomas R.; Banko, Maryann
1981-01-01
Using probabilities derived from a geographically distant patient population, we applied Bayesian classification to categorize stroke patients by etiology. Performance was assessed both by error rate and with a new linear accuracy coefficient. This approach to patient classification was found to be surprisingly accurate when compared to classification by two neurologists and to classification by the Bayesian method using “low cost” local and subjective probabilities. We conclude that for some...
DEFF Research Database (Denmark)
Jensen, Finn Verner; Nielsen, Thomas Dyhre
2016-01-01
Mathematically, a Bayesian graphical model is a compact representation of the joint probability distribution for a set of variables. The most frequently used type of Bayesian graphical models are Bayesian networks. The structural part of a Bayesian graphical model is a graph consisting of nodes...... and edges. The nodes represent variables, which may be either discrete or continuous. An edge between two nodes A and B indicates a direct influence between the state of A and the state of B, which in some domains can also be interpreted as a causal relation. The wide-spread use of Bayesian networks...... is largely due to the availability of efficient inference algorithms for answering probabilistic queries about the states of the variables in the network. Furthermore, to support the construction of Bayesian network models, learning algorithms are also available. We give an overview of the Bayesian network...
Turbo decoding using two soft output values
Institute of Scientific and Technical Information of China (English)
李建平; 潘申富; 梁庆林
2004-01-01
It is well known that turbo decoding always begins from the first component decoder and supposes that the apriori information is "0" at the first iterative decoding. To alternatively start decoding at two component decoders, we can gain two soft output values for the received observation of an input bit. It is obvious that two soft output values comprise more sufficient extrinsic information than only one output value obtained in the conventional scheme since different start points of decoding result in different combinations of the a priori information and the input codewords with different symbol orders due to the permutation of an interleaver. Summarizing two soft output values for erery bit before making hard decisions, we can correct more errors due to their complement. Consequently, turbo codes can achieve better error correcting performance than before in this way. Simulation results show that the performance of turbo codes using the novel proposed decoding scheme can get a growing improvement with the increment of SNR in general compared to the conventional scheme. When the bit error probability is 10-5, the proposed scheme can achieve 0.5 dB asymptotic coding gain or so under the given simulation conditions.
Application of RS Codes in Decoding QR Code
Institute of Scientific and Technical Information of China (English)
Zhu Suxia(朱素霞); Ji Zhenzhou; Cao Zhiyan
2003-01-01
The QR Code is a 2-dimensional matrix code with high error correction capability. It employs RS codes to generate error correction codewords in encoding and recover errors and damages in decoding. This paper presents several QR Code's virtues, analyzes RS decoding algorithm and gives a software flow chart of decoding the QR Code with RS decoding algorithm.
An Encoder/Decoder Scheme of OCDMA Based on Waveguide
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
A new encoder/decoder scheme of OCDMA based on waveguide isproposed in this paper. The principle as well as the structure of waveguide encoder/decoder is given. It can be seen that all-optical OCDMA encoder/decoder can be realized by the proposed scheme of the waveguide encoder/decoder. It can also make the OCDMA encoder/decoder integrated easily and the access controlled easily. The system based on this scheme can work under the entirely asynchronous condition.
On Rational Interpolation-Based List-Decoding and List-Decoding Binary Goppa Codes
DEFF Research Database (Denmark)
Beelen, Peter; Høholdt, Tom; Nielsen, Johan Sebastian Rosenkilde;
2013-01-01
We derive the Wu list-decoding algorithm for generalized Reed–Solomon (GRS) codes by using Gröbner bases over modules and the Euclidean algorithm as the initial algorithm instead of the Berlekamp–Massey algorithm. We present a novel method for constructing the interpolation polynomial fast. We give...... a new application of the Wu list decoder by decoding irreducible binary Goppa codes up to the binary Johnson radius. Finally, we point out a connection between the governing equations of the Wu algorithm and the Guruswami–Sudan algorithm, immediately leading to equality in the decoding range...
Decoding Algorithms for Random Linear Network Codes
DEFF Research Database (Denmark)
Heide, Janus; Pedersen, Morten Videbæk; Fitzek, Frank
2011-01-01
achieve a high coding throughput, and reduce energy consumption.We use an on-the-fly version of the Gauss-Jordan algorithm as a baseline, and provide several simple improvements to reduce the number of operations needed to perform decoding. Our tests show that the improvements can reduce the number......We consider the problem of efficient decoding of a random linear code over a finite field. In particular we are interested in the case where the code is random, relatively sparse, and use the binary finite field as an example. The goal is to decode the data using fewer operations to potentially...... of operations used during decoding with 10-20% on average depending on the code parameters....
Low Power Decoding of LDPC Codes
Mohamed Ismail; Imran Ahmed; Justin Coon
2013-01-01
Wireless sensor networks are used in many diverse application scenarios that require the network designer to trade off different factors. Two such factors of importance in many wireless sensor networks are communication reliability and battery life. This paper describes an efficient, low complexity, high throughput channel decoder suited to decoding low-density parity-check (LDPC) codes. LDPC codes have demonstrated excellent error-correcting ability such that a number of recent wireless stan...
Simplified decoding techniques for linear block codes
Srivastava, Shraddha
2013-01-01
Error correcting codes are combinatorial objects, designed to enable reliable transmission of digital data over noisy channels. They are ubiquitously used in communication, data storage etc. Error correction allows reconstruction of the original data from received word. The classical decoding algorithms are constrained to output just one codeword. However, in the late 50’s researchers proposed a relaxed error correction model for potentially large error rates known as list decoding. The resea...
International Nuclear Information System (INIS)
We have developed a comprehensive, Bayesian, PBPK model-based analysis of the population toxicokinetics of trichloroethylene (TCE) and its metabolites in mice, rats, and humans, considering a wider range of physiological, chemical, in vitro, and in vivo data than any previously published analysis of TCE. The toxicokinetics of the 'population average,' its population variability, and their uncertainties are characterized in an approach that strives to be maximally transparent and objective. Estimates of experimental variability and uncertainty were also included in this analysis. The experimental database was expanded to include virtually all available in vivo toxicokinetic data, which permitted, in rats and humans, the specification of separate datasets for model calibration and evaluation. The total combination of these approaches and PBPK analysis provides substantial support for the model predictions. In addition, we feel confident that the approach employed also yields an accurate characterization of the uncertainty in metabolic pathways for which available data were sparse or relatively indirect, such as GSH conjugation and respiratory tract metabolism. Key conclusions from the model predictions include the following: (1) as expected, TCE is substantially metabolized, primarily by oxidation at doses below saturation; (2) GSH conjugation and subsequent bioactivation in humans appear to be 10- to 100-fold greater than previously estimated; and (3) mice had the greatest rate of respiratory tract oxidative metabolism as compared to rats and humans. In a situation such as TCE in which there is large database of studies coupled with complex toxicokinetics, the Bayesian approach provides a systematic method of simultaneously estimating model parameters and characterizing their uncertainty and variability. However, care needs to be taken in its implementation to ensure biological consistency, transparency, and objectivity.
Understanding Computational Bayesian Statistics
Bolstad, William M
2011-01-01
A hands-on introduction to computational statistics from a Bayesian point of view Providing a solid grounding in statistics while uniquely covering the topics from a Bayesian perspective, Understanding Computational Bayesian Statistics successfully guides readers through this new, cutting-edge approach. With its hands-on treatment of the topic, the book shows how samples can be drawn from the posterior distribution when the formula giving its shape is all that is known, and how Bayesian inferences can be based on these samples from the posterior. These ideas are illustrated on common statistic
Bayesian statistics an introduction
Lee, Peter M
2012-01-01
Bayesian Statistics is the school of thought that combines prior beliefs with the likelihood of a hypothesis to arrive at posterior beliefs. The first edition of Peter Lee’s book appeared in 1989, but the subject has moved ever onwards, with increasing emphasis on Monte Carlo based techniques. This new fourth edition looks at recent techniques such as variational methods, Bayesian importance sampling, approximate Bayesian computation and Reversible Jump Markov Chain Monte Carlo (RJMCMC), providing a concise account of the way in which the Bayesian approach to statistics develops as wel
On Decoding Irregular Tanner Codes
Even, Guy
2011-01-01
We present a new combinatorial characterization for local-optimality of a codeword in irregular Tanner codes. This characterization is a generalization of [Arora, Daskalakis, Steurer; 2009] and [Vontobel; 2010]. The main novelty in this characterization is that it is based on a conical combination of subtrees in the computation trees. These subtrees may have any degree in the local-code nodes and may have any height (even greater than the girth). We prove that local-optimality in this new characterization implies Maximum-Likelihood (ML) optimality and LP-optimality. We also show that it is possible to compute efficiently a certificate for the local-optimality of a codeword given the channel output. We apply this characterization to regular Tanner codes. We prove a lower bound on the noise threshold in channels such as BSC and AWGNC. When the noise is below this lower bound, the probability that LP decoding fails diminishes doubly exponentially in the girth of the Tanner graph. We use local optimality also to ...
Sphere decoding complexity exponent for decoding full rate codes over the quasi-static MIMO channel
Jalden, Joakim
2011-01-01
In the setting of quasi-static multiple-input multiple-output (MIMO) channels, we consider the high signal-to-noise ratio (SNR) asymptotic complexity required by the sphere decoding (SD) algorithm for decoding a large class of full rate linear space-time codes. With SD complexity having random fluctuations induced by the random channel, noise and codeword realizations, the introduced SD complexity exponent manages to concisely describe the computational reserves required by the SD algorithm to achieve arbitrarily close to optimal decoding performance. Bounds and exact expressions for the SD complexity exponent are obtained for the decoding of large families of codes with arbitrary performance characteristics. For the particular example of decoding the recently introduced threaded cyclic division algebra (CDA) based codes -- the only currently known explicit designs that are uniformly optimal with respect to the diversity multiplexing tradeoff (DMT) -- the SD complexity exponent is shown to take a particularly...
Frühwirth-Schnatter, Sylvia
1990-01-01
In the paper at hand we apply it to Bayesian statistics to obtain "Fuzzy Bayesian Inference". In the subsequent sections we will discuss a fuzzy valued likelihood function, Bayes' theorem for both fuzzy data and fuzzy priors, a fuzzy Bayes' estimator, fuzzy predictive densities and distributions, and fuzzy H.P.D .-Regions. (author's abstract)
Yuan, Ying; MacKinnon, David P.
2009-01-01
In this article, we propose Bayesian analysis of mediation effects. Compared with conventional frequentist mediation analysis, the Bayesian approach has several advantages. First, it allows researchers to incorporate prior information into the mediation analysis, thus potentially improving the efficiency of estimates. Second, under the Bayesian…
Exponential Lower Bound for 2-Query Locally Decodable Codes
Kerenidis, I; Kerenidis, Iordanis; Wolf, Ronald de
2002-01-01
We prove exponential lower bounds on the length of 2-query locally decodable codes. Goldreich et al. recently proved such bounds for the special case of linear locally decodable codes. Our proof shows that a 2-query locally decodable code can be decoded with only 1 quantum query, and then proves an exponential lower bound for such 1-query locally quantum-decodable codes. We also exhibit q-query locally quantum-decodable codes that are much shorter than the best known q-query classical codes. Finally, we give some new lower bounds for (not necessarily linear) private information retrieval systems.
von der Linden, Wolfgang; Dose, Volker; von Toussaint, Udo
2014-06-01
Preface; Part I. Introduction: 1. The meaning of probability; 2. Basic definitions; 3. Bayesian inference; 4. Combinatrics; 5. Random walks; 6. Limit theorems; 7. Continuous distributions; 8. The central limit theorem; 9. Poisson processes and waiting times; Part II. Assigning Probabilities: 10. Transformation invariance; 11. Maximum entropy; 12. Qualified maximum entropy; 13. Global smoothness; Part III. Parameter Estimation: 14. Bayesian parameter estimation; 15. Frequentist parameter estimation; 16. The Cramer-Rao inequality; Part IV. Testing Hypotheses: 17. The Bayesian way; 18. The frequentist way; 19. Sampling distributions; 20. Bayesian vs frequentist hypothesis tests; Part V. Real World Applications: 21. Regression; 22. Inconsistent data; 23. Unrecognized signal contributions; 24. Change point problems; 25. Function estimation; 26. Integral equations; 27. Model selection; 28. Bayesian experimental design; Part VI. Probabilistic Numerical Techniques: 29. Numerical integration; 30. Monte Carlo methods; 31. Nested sampling; Appendixes; References; Index.
FPGA implementation of low complexity LDPC iterative decoder
Verma, Shivani; Sharma, Sanjay
2016-07-01
Low-density parity-check (LDPC) codes, proposed by Gallager, emerged as a class of codes which can yield very good performance on the additive white Gaussian noise channel as well as on the binary symmetric channel. LDPC codes have gained lots of importance due to their capacity achieving property and excellent performance in the noisy channel. Belief propagation (BP) algorithm and its approximations, most notably min-sum, are popular iterative decoding algorithms used for LDPC and turbo codes. The trade-off between the hardware complexity and the decoding throughput is a critical factor in the implementation of the practical decoder. This article presents introduction to LDPC codes and its various decoding algorithms followed by realisation of LDPC decoder by using simplified message passing algorithm and partially parallel decoder architecture. Simplified message passing algorithm has been proposed for trade-off between low decoding complexity and decoder performance. It greatly reduces the routing and check node complexity of the decoder. Partially parallel decoder architecture possesses high speed and reduced complexity. The improved design of the decoder possesses a maximum symbol throughput of 92.95 Mbps and a maximum of 18 decoding iterations. The article presents implementation of 9216 bits, rate-1/2, (3, 6) LDPC decoder on Xilinx XC3D3400A device from Spartan-3A DSP family.
Word-decoding as a function of temporal processing in the visual system.
Directory of Open Access Journals (Sweden)
Steven R Holloway
Full Text Available This study explored the relation between visual processing and word-decoding ability in a normal reading population. Forty participants were recruited at Arizona State University. Flicker fusion thresholds were assessed with an optical chopper using the method of limits by a 1-deg diameter green (543 nm test field. Word decoding was measured using reading-word and nonsense-word decoding tests. A non-linguistic decoding measure was obtained using a computer program that consisted of Landolt C targets randomly presented in four cardinal orientations, at 3-radial distances from a focus point, for eight compass points, in a circular pattern. Participants responded by pressing the arrow key on the keyboard that matched the direction the target was facing. The results show a strong correlation between critical flicker fusion thresholds and scores on the reading-word, nonsense-word, and non-linguistic decoding measures. The data suggests that the functional elements of the visual system involved with temporal modulation and spatial processing may affect the ease with which people read.
Completion time reduction in instantly decodable network coding through decoding delay control
Douik, Ahmed S.
2014-12-01
For several years, the completion time and the decoding delay problems in Instantly Decodable Network Coding (IDNC) were considered separately and were thought to completely act against each other. Recently, some works aimed to balance the effects of these two important IDNC metrics but none of them studied a further optimization of one by controlling the other. In this paper, we study the effect of controlling the decoding delay to reduce the completion time below its currently best known solution. We first derive the decoding-delay-dependent expressions of the users\\' and their overall completion times. Although using such expressions to find the optimal overall completion time is NP-hard, we use a heuristic that minimizes the probability of increasing the maximum of these decoding-delay-dependent completion time expressions after each transmission through a layered control of their decoding delays. Simulation results show that this new algorithm achieves both a lower mean completion time and mean decoding delay compared to the best known heuristic for completion time reduction. The gap in performance becomes significant for harsh erasure scenarios.
Online Testable Decoder using Reversible Logic
Directory of Open Access Journals (Sweden)
Hemalatha. K. N. Manjula B. B. Girija. S
2012-02-01
Full Text Available The project proposes to design and test 2 to 4 reversible Decoder circuit with arbitrary number of gates to an online testable reversible one and is independent of the type of reversible gate used. The constructed circuit can detect any single bit errors and to convert a decoder circuit that is designed by reversible gates to an online testable reversible decoder circuit. Conventional digital circuits dissipate a significant amount of energy because bits of information are erased during the logic operations. Thus if logic gates are designed such that the information bits are not destroyed, the power consumption can be reduced. The information bits are not lost in case of a reversible computation. Reversible logic can be used to implement any Boolean logic function.
Informed Network Coding for Minimum Decoding Delay
Costa, Rui A; Widmer, Joerg; Barros, Joao
2008-01-01
Network coding is a highly efficient data dissemination mechanism for wireless networks. Since network coded information can only be recovered after delivering a sufficient number of coded packets, the resulting decoding delay can become problematic for delay-sensitive applications such as real-time media streaming. Motivated by this observation, we consider several algorithms that minimize the decoding delay and analyze their performance by means of simulation. The algorithms differ both in the required information about the state of the neighbors' buffers and in the way this knowledge is used to decide which packets to combine through coding operations. Our results show that a greedy algorithm, whose encodings maximize the number of nodes at which a coded packet is immediately decodable significantly outperforms existing network coding protocols.
Neuroprosthetic Decoder Training as Imitation Learning.
Directory of Open Access Journals (Sweden)
Josh Merel
2016-05-01
Full Text Available Neuroprosthetic brain-computer interfaces function via an algorithm which decodes neural activity of the user into movements of an end effector, such as a cursor or robotic arm. In practice, the decoder is often learned by updating its parameters while the user performs a task. When the user's intention is not directly observable, recent methods have demonstrated value in training the decoder against a surrogate for the user's intended movement. Here we show that training a decoder in this way is a novel variant of an imitation learning problem, where an oracle or expert is employed for supervised training in lieu of direct observations, which are not available. Specifically, we describe how a generic imitation learning meta-algorithm, dataset aggregation (DAgger, can be adapted to train a generic brain-computer interface. By deriving existing learning algorithms for brain-computer interfaces in this framework, we provide a novel analysis of regret (an important metric of learning efficacy for brain-computer interfaces. This analysis allows us to characterize the space of algorithmic variants and bounds on their regret rates. Existing approaches for decoder learning have been performed in the cursor control setting, but the available design principles for these decoders are such that it has been impossible to scale them to naturalistic settings. Leveraging our findings, we then offer an algorithm that combines imitation learning with optimal control, which should allow for training of arbitrary effectors for which optimal control can generate goal-oriented control. We demonstrate this novel and general BCI algorithm with simulated neuroprosthetic control of a 26 degree-of-freedom model of an arm, a sophisticated and realistic end effector.
LP Decoding meets LP Decoding: A Connection between Channel Coding and Compressed Sensing
Dimakis, Alexandros G
2009-01-01
This is a tale of two linear programming decoders, namely channel coding linear programming decoding (CC-LPD) and compressed sensing linear programming decoding (CS-LPD). So far, they have evolved quite independently. The aim of the present paper is to show that there is a tight connection between, on the one hand, CS-LPD based on a zero-one measurement matrix over the reals and, on the other hand, CC-LPD of the binary linear code that is obtained by viewing this measurement matrix as a binary parity-check matrix. This connection allows one to translate performance guarantees from one setup to the other.
Krumholz, Mark R; da Silva, Robert L; Rendahl, Theodore; Parra, Jonathan
2015-01-01
Stellar population synthesis techniques for predicting the observable light emitted by a stellar population have extensive applications in numerous areas of astronomy. However, accurate predictions for small populations of young stars, such as those found in individual star clusters, star-forming dwarf galaxies, and small segments of spiral galaxies, require that the population be treated stochastically. Conversely, accurate deductions of the properties of such objects also requires consideration of stochasticity. Here we describe a comprehensive suite of modular, open-source software tools for tackling these related problems. These include: a greatly-enhanced version of the slug code introduced by da Silva et al. (2012), which computes spectra and photometry for stochastically- or deterministically-sampled stellar populations with nearly-arbitrary star formation histories, clustering properties, and initial mass functions; cloudy_slug, a tool that automatically couples slug-computed spectra with the cloudy r...
Decoding of concatenated codes with interleaved outer codes
DEFF Research Database (Denmark)
Justesen, Jørn; Thommesen, Christian; Høholdt, Tom
2004-01-01
Recently Bleichenbacher et al. proposed a decoding algorithm for interleaved Reed/Solomon codes, which allows close to errors to be corrected in many cases. We discuss the application of this decoding algorithm to concatenated codes. (NK) N-K......Recently Bleichenbacher et al. proposed a decoding algorithm for interleaved Reed/Solomon codes, which allows close to errors to be corrected in many cases. We discuss the application of this decoding algorithm to concatenated codes. (NK) N-K...
Generalized Sudan's List Decoding for Order Domain Codes
DEFF Research Database (Denmark)
Geil, Hans Olav; Matsumoto, Ryutaroh
2007-01-01
We generalize Sudan's list decoding algorithm without multiplicity to evaluation codes coming from arbitrary order domains. The number of correctable errors by the proposed method is larger than the original list decoding without multiplicity.......We generalize Sudan's list decoding algorithm without multiplicity to evaluation codes coming from arbitrary order domains. The number of correctable errors by the proposed method is larger than the original list decoding without multiplicity....
Granade, Christopher; Cory, D G
2015-01-01
In recent years, Bayesian methods have been proposed as a solution to a wide range of issues in quantum state and process tomography. State-of- the-art Bayesian tomography solutions suffer from three problems: numerical intractability, a lack of informative prior distributions, and an inability to track time-dependent processes. Here, we solve all three problems. First, we use modern statistical methods, as pioneered by Husz\\'ar and Houlsby and by Ferrie, to make Bayesian tomography numerically tractable. Our approach allows for practical computation of Bayesian point and region estimators for quantum states and channels. Second, we propose the first informative priors on quantum states and channels. Finally, we develop a method that allows online tracking of time-dependent states and estimates the drift and diffusion processes affecting a state. We provide source code and animated visual examples for our methods.
Noncausal Bayesian Vector Autoregression
DEFF Research Database (Denmark)
Lanne, Markku; Luoto, Jani
We propose a Bayesian inferential procedure for the noncausal vector autoregressive (VAR) model that is capable of capturing nonlinearities and incorporating effects of missing variables. In particular, we devise a fast and reliable posterior simulator that yields the predictive distribution...
Malicious Bayesian Congestion Games
Gairing, Martin
2008-01-01
In this paper, we introduce malicious Bayesian congestion games as an extension to congestion games where players might act in a malicious way. In such a game each player has two types. Either the player is a rational player seeking to minimize her own delay, or - with a certain probability - the player is malicious in which case her only goal is to disturb the other players as much as possible. We show that such games do in general not possess a Bayesian Nash equilibrium in pure strategies (i.e. a pure Bayesian Nash equilibrium). Moreover, given a game, we show that it is NP-complete to decide whether it admits a pure Bayesian Nash equilibrium. This result even holds when resource latency functions are linear, each player is malicious with the same probability, and all strategy sets consist of singleton sets. For a slightly more restricted class of malicious Bayesian congestion games, we provide easy checkable properties that are necessary and sufficient for the existence of a pure Bayesian Nash equilibrium....
High Speed Frame Synchronization and Viterbi Decoding
DEFF Research Database (Denmark)
Paaske, Erik; Justesen, Jørn; Larsen, Knud J.;
1998-01-01
The study has been divided into two phases. The purpose of Phase 1 of the study was to describe the system structure and algorithms in sufficient detail to allow drawing the high level architecture of units containing frame synchronization and Viterbi decoding. After selection of which specific u...
High Speed Frame Synchronization and Viterbi Decoding
DEFF Research Database (Denmark)
Paaske, Erik; Justesen, Jørn; Larsen, Knud J.;
1996-01-01
The purpose of Phase 1 of the study is to describe the system structure and algorithms in sufficient detail to allow drawing the high level architecture of units containing frame synchronization and Viterbi decoding. The systems we consider are high data rate space communication systems. Also, th...
List Decoding Barnes-Wall Lattices
Grigorescu, Elena
2011-01-01
The question of list decoding error-correcting codes over finite fields (under the Hamming metric) has been widely studied in recent years. Motivated by the similar discrete structure of linear codes and point lattices in R^N, and their many shared applications across complexity theory, cryptography, and coding theory, we initiate the study of list decoding for lattices. Namely: for a lattice L in R^N, given a target vector r in R^N and a distance parameter d, output the set of all lattice points w in L that are within distance d of r. In this work we focus on combinatorial and algorithmic questions related to list decoding for the well-studied family of Barnes-Wall lattices. Our main contributions are twofold: 1) We give tight (up to polynomials) combinatorial bounds on the worst-case list size, showing it to be polynomial in the lattice dimension for any error radius bounded away from the lattice's minimum distance (in the Euclidean norm). 2) Building on the unique decoding algorithm of Micciancio and Nicol...
Decoding Hermitian Codes with Sudan's Algorithm
DEFF Research Database (Denmark)
Høholdt, Tom; Nielsen, Rasmus Refslund
1999-01-01
We present an efficient implementation of Sudan's algorithm for list decoding Hermitian codes beyond half the minimum distance. The main ingredients are an explicit method to calculate so-called increasing zero bases, an efficient interpolation algorithm for finding the Q-polynomial, and a reduct...
Perceptual Learning via Decoded-EEG Neurofeedback
Brandmeyer, A.; Sadakata, M.; Spyrou, L.; McQueen, J.M.; Desain, P.W.M.
2013-01-01
An experiment was conducted to determine whether decoding auditory evoked potentials during passive listening and providing the classifier output as a neurofeedback signal leads to the enhancement of auditory perceptual discrimination and/or brain responses related to auditory perception. Results in
A chemical system that mimics decoding operations.
Giansante, Carlo; Ceroni, Paola; Venturi, Margherita; Sakamoto, Junji; Schlüter, A Dieter
2009-02-23
The chemical information stored in equilibrium mixtures of molecular species is larger than the sum of information carried by the individual molecules. Protonation equilibria in dilute dichloromethane solution of a shape-persistent macrocycle bearing two 2,2'-bipyridine units and two Coumarin 2 moieties (see figure) can be exploited to mimic decoding operations.
Efficient Decoding of Turbo Codes with Nonbinary Belief Propagation
Directory of Open Access Journals (Sweden)
Thierry Lestable
2008-05-01
Full Text Available This paper presents a new approach to decode turbo codes using a nonbinary belief propagation decoder. The proposed approach can be decomposed into two main steps. First, a nonbinary Tanner graph representation of the turbo code is derived by clustering the binary parity-check matrix of the turbo code. Then, a group belief propagation decoder runs several iterations on the obtained nonbinary Tanner graph. We show in particular that it is necessary to add a preprocessing step on the parity-check matrix of the turbo code in order to ensure good topological properties of the Tanner graph and then good iterative decoding performance. Finally, by capitalizing on the diversity which comes from the existence of distinct efficient preprocessings, we propose a new decoding strategy, called decoder diversity, that intends to take benefits from the diversity through collaborative decoding schemes.
Decoding subjective mental states from fMRI activity patterns
International Nuclear Information System (INIS)
In recent years, functional magnetic resonance imaging (fMRI) decoding has emerged as a powerful tool to read out detailed stimulus features from multi-voxel brain activity patterns. Moreover, the method has been extended to perform a primitive form of 'mind-reading,' by applying a decoder 'objectively' trained using stimulus features to more 'subjective' conditions. In this paper, we first introduce basic procedures for fMRI decoding based on machine learning techniques. Second, we discuss the source of information used for decoding, in particular, the possibility of extracting information from subvoxel neural structures. We next introduce two experimental designs for decoding subjective mental states: the 'objective-to-subjective design' and the 'subjective-to-subjective design.' Then, we illustrate recent studies on the decoding of a variety of mental states, such as, attention, awareness, decision making, memory, and mental imagery. Finally, we discuss the challenges and new directions of fMRI decoding. (author)
Efficient Decoding of Turbo Codes with Nonbinary Belief Propagation
Directory of Open Access Journals (Sweden)
Lestable Thierry
2008-01-01
Full Text Available Abstract This paper presents a new approach to decode turbo codes using a nonbinary belief propagation decoder. The proposed approach can be decomposed into two main steps. First, a nonbinary Tanner graph representation of the turbo code is derived by clustering the binary parity-check matrix of the turbo code. Then, a group belief propagation decoder runs several iterations on the obtained nonbinary Tanner graph. We show in particular that it is necessary to add a preprocessing step on the parity-check matrix of the turbo code in order to ensure good topological properties of the Tanner graph and then good iterative decoding performance. Finally, by capitalizing on the diversity which comes from the existence of distinct efficient preprocessings, we propose a new decoding strategy, called decoder diversity, that intends to take benefits from the diversity through collaborative decoding schemes.
Lombaert, E.; Guillemaud, T.; Thomas, C.E.; Handley, L.J.L.; Li, J.; Wang, S.; Pang, H.; Goryacheva, I.; Zakharov, I.A.; Jousselin, E.; Poland, R.L.; Migeon, A.; Lenteren, van J.C.; Clercq, de P.; Berkvens, N.; Jones, W.; Estoup, A.
2011-01-01
Correct identification of the source population of an invasive species is a prerequisite for testing hypotheses concerning the factors responsible for biological invasions. The native area of invasive species may be large, poorly known and/or genetically structured. Because the actual source populat
Institute of Scientific and Technical Information of China (English)
PaolaSANCHEZ-PENA; JeanSEBASTIENHULOT[; SalkURIEN; MD; AnnickANKRI; GillesMONTALESCOT; PhilippeLECHAT
2004-01-01
AIM: Recent studies have suggested that intravenous enoxaparin can be used as an alternative therapy in patients percutaneous coronary intervention (PCI); yet the optimal regimen is to be defined. METHODS: Anti-Xa activities were measured in 556 patients who received a single 0.5 mg/kg dose of enoxaparin intravenously immediately before PCI. A population pharmacoki-
Computational statistics using the bBayesian Inference Engine
Weinberg, Martin D
2012-01-01
This paper introduces the Bayesian Inference Engine (BIE), a general parallel-optimised software package for parameter inference and model selection. This package is motivated by the analysis needs of modern astronomical surveys and the need to organise and reuse expensive derived data. I describe key concepts that illustrate the power of Bayesian inference to address these needs and outline the computational challenge. The techniques presented are based on experience gained in modelling star-counts and stellar populations, analysing the morphology of galaxy images, and performing Bayesian investigations of semi-analytic models of galaxy formation. These inference problems require advanced Markov chain Monte Carlo (MCMC) algorithms that expedite sampling, mixing, and the analysis of the Bayesian posterior distribution. The BIE was designed to be a collaborative platform for applying Bayesian methodology to astronomy. By providing a variety of statistical algorithms for all phases of the inference problem, a u...
Bayesian phylogeography finds its roots.
Directory of Open Access Journals (Sweden)
Philippe Lemey
2009-09-01
Full Text Available As a key factor in endemic and epidemic dynamics, the geographical distribution of viruses has been frequently interpreted in the light of their genetic histories. Unfortunately, inference of historical dispersal or migration patterns of viruses has mainly been restricted to model-free heuristic approaches that provide little insight into the temporal setting of the spatial dynamics. The introduction of probabilistic models of evolution, however, offers unique opportunities to engage in this statistical endeavor. Here we introduce a Bayesian framework for inference, visualization and hypothesis testing of phylogeographic history. By implementing character mapping in a Bayesian software that samples time-scaled phylogenies, we enable the reconstruction of timed viral dispersal patterns while accommodating phylogenetic uncertainty. Standard Markov model inference is extended with a stochastic search variable selection procedure that identifies the parsimonious descriptions of the diffusion process. In addition, we propose priors that can incorporate geographical sampling distributions or characterize alternative hypotheses about the spatial dynamics. To visualize the spatial and temporal information, we summarize inferences using virtual globe software. We describe how Bayesian phylogeography compares with previous parsimony analysis in the investigation of the influenza A H5N1 origin and H5N1 epidemiological linkage among sampling localities. Analysis of rabies in West African dog populations reveals how virus diffusion may enable endemic maintenance through continuous epidemic cycles. From these analyses, we conclude that our phylogeographic framework will make an important asset in molecular epidemiology that can be easily generalized to infer biogeogeography from genetic data for many organisms.
Bayesian least squares deconvolution
Ramos, A Asensio
2015-01-01
Aims. To develop a fully Bayesian least squares deconvolution (LSD) that can be applied to the reliable detection of magnetic signals in noise-limited stellar spectropolarimetric observations using multiline techniques. Methods. We consider LSD under the Bayesian framework and we introduce a flexible Gaussian Process (GP) prior for the LSD profile. This prior allows the result to automatically adapt to the presence of signal. We exploit several linear algebra identities to accelerate the calculations. The final algorithm can deal with thousands of spectral lines in a few seconds. Results. We demonstrate the reliability of the method with synthetic experiments and we apply it to real spectropolarimetric observations of magnetic stars. We are able to recover the magnetic signals using a small number of spectral lines, together with the uncertainty at each velocity bin. This allows the user to consider if the detected signal is reliable. The code to compute the Bayesian LSD profile is freely available.
Bayesian least squares deconvolution
Asensio Ramos, A.; Petit, P.
2015-11-01
Aims: We develop a fully Bayesian least squares deconvolution (LSD) that can be applied to the reliable detection of magnetic signals in noise-limited stellar spectropolarimetric observations using multiline techniques. Methods: We consider LSD under the Bayesian framework and we introduce a flexible Gaussian process (GP) prior for the LSD profile. This prior allows the result to automatically adapt to the presence of signal. We exploit several linear algebra identities to accelerate the calculations. The final algorithm can deal with thousands of spectral lines in a few seconds. Results: We demonstrate the reliability of the method with synthetic experiments and we apply it to real spectropolarimetric observations of magnetic stars. We are able to recover the magnetic signals using a small number of spectral lines, together with the uncertainty at each velocity bin. This allows the user to consider if the detected signal is reliable. The code to compute the Bayesian LSD profile is freely available.
Hybrid Batch Bayesian Optimization
Azimi, Javad; Fern, Xiaoli
2012-01-01
Bayesian Optimization aims at optimizing an unknown non-convex/concave function that is costly to evaluate. We are interested in application scenarios where concurrent function evaluations are possible. Under such a setting, BO could choose to either sequentially evaluate the function, one input at a time and wait for the output of the function before making the next selection, or evaluate the function at a batch of multiple inputs at once. These two different settings are commonly referred to as the sequential and batch settings of Bayesian Optimization. In general, the sequential setting leads to better optimization performance as each function evaluation is selected with more information, whereas the batch setting has an advantage in terms of the total experimental time (the number of iterations). In this work, our goal is to combine the strength of both settings. Specifically, we systematically analyze Bayesian optimization using Gaussian process as the posterior estimator and provide a hybrid algorithm t...
Loredo, T J
2004-01-01
I describe a framework for adaptive scientific exploration based on iterating an Observation--Inference--Design cycle that allows adjustment of hypotheses and observing protocols in response to the results of observation on-the-fly, as data are gathered. The framework uses a unified Bayesian methodology for the inference and design stages: Bayesian inference to quantify what we have learned from the available data and predict future data, and Bayesian decision theory to identify which new observations would teach us the most. When the goal of the experiment is simply to make inferences, the framework identifies a computationally efficient iterative ``maximum entropy sampling'' strategy as the optimal strategy in settings where the noise statistics are independent of signal properties. Results of applying the method to two ``toy'' problems with simulated data--measuring the orbit of an extrasolar planet, and locating a hidden one-dimensional object--show the approach can significantly improve observational eff...
Bayesian Exploratory Factor Analysis
DEFF Research Database (Denmark)
Conti, Gabriella; Frühwirth-Schnatter, Sylvia; Heckman, James J.;
2014-01-01
This paper develops and applies a Bayesian approach to Exploratory Factor Analysis that improves on ad hoc classical approaches. Our framework relies on dedicated factor models and simultaneously determines the number of factors, the allocation of each measurement to a unique factor, and the corr......This paper develops and applies a Bayesian approach to Exploratory Factor Analysis that improves on ad hoc classical approaches. Our framework relies on dedicated factor models and simultaneously determines the number of factors, the allocation of each measurement to a unique factor......, and the corresponding factor loadings. Classical identification criteria are applied and integrated into our Bayesian procedure to generate models that are stable and clearly interpretable. A Monte Carlo study confirms the validity of the approach. The method is used to produce interpretable low dimensional aggregates...
On Lattice Sequential Decoding for The Unconstrained AWGN Channel
Abediseid, Walid
2013-04-04
In this paper, the performance limits and the computational complexity of the lattice sequential decoder are analyzed for the unconstrained additive white Gaussian noise channel. The performance analysis available in the literature for such a channel has been studied only under the use of the minimum Euclidean distance decoder that is commonly referred to as the \\\\textit{lattice decoder}. Lattice decoders based on solutions to the NP-hard closest vector problem are very complex to implement, and the search for low complexity receivers for the detection of lattice codes is considered a challenging problem. However, the low computational complexity advantage that sequential decoding promises, makes it an alternative solution to the lattice decoder. In this work, we characterize the performance and complexity tradeoff via the error exponent and the decoding complexity, respectively, of such a decoder as a function of the decoding parameter --- the bias term. For the above channel, we derive the cut-off volume-to-noise ratio that is required to achieve a good error performance with low decoding complexity.
On Lattice Sequential Decoding for The Unconstrained AWGN Channel
Abediseid, Walid
2012-10-01
In this paper, the performance limits and the computational complexity of the lattice sequential decoder are analyzed for the unconstrained additive white Gaussian noise channel. The performance analysis available in the literature for such a channel has been studied only under the use of the minimum Euclidean distance decoder that is commonly referred to as the lattice decoder. Lattice decoders based on solutions to the NP-hard closest vector problem are very complex to implement, and the search for low complexity receivers for the detection of lattice codes is considered a challenging problem. However, the low computational complexity advantage that sequential decoding promises, makes it an alternative solution to the lattice decoder. In this work, we characterize the performance and complexity tradeoff via the error exponent and the decoding complexity, respectively, of such a decoder as a function of the decoding parameter --- the bias term. For the above channel, we derive the cut-off volume-to-noise ratio that is required to achieve a good error performance with low decoding complexity.
Encoding and decoding time in neural development.
Toma, Kenichi; Wang, Tien-Cheng; Hanashima, Carina
2016-01-01
The development of a multicellular organism involves time-dependent changes in molecular and cellular states; therefore 'time' is an indispensable mathematical parameter of ontogenesis. Regardless of their inextricable relationship, there is a limited number of events for which the output of developmental phenomena primarily uses temporal cues that are generated through multilevel interactions between molecules, cells, and tissues. In this review, we focus on neural stem cells, which serve as a faithful decoder of temporal cues to transmit biological information and generate specific output in the developing nervous system. We further explore the identity of the temporal information that is encoded in neural development, and how this information is decoded into various cellular fate decisions. PMID:26748623
Hardware Implementation of Serially Concatenated PPM Decoder
Moision, Bruce; Hamkins, Jon; Barsoum, Maged; Cheng, Michael; Nakashima, Michael
2009-01-01
A prototype decoder for a serially concatenated pulse position modulation (SCPPM) code has been implemented in a field-programmable gate array (FPGA). At the time of this reporting, this is the first known hardware SCPPM decoder. The SCPPM coding scheme, conceived for free-space optical communications with both deep-space and terrestrial applications in mind, is an improvement of several dB over the conventional Reed-Solomon PPM scheme. The design of the FPGA SCPPM decoder is based on a turbo decoding algorithm that requires relatively low computational complexity while delivering error-rate performance within approximately 1 dB of channel capacity. The SCPPM encoder consists of an outer convolutional encoder, an interleaver, an accumulator, and an inner modulation encoder (more precisely, a mapping of bits to PPM symbols). Each code is describable by a trellis (a finite directed graph). The SCPPM decoder consists of an inner soft-in-soft-out (SISO) module, a de-interleaver, an outer SISO module, and an interleaver connected in a loop (see figure). Each SISO module applies the Bahl-Cocke-Jelinek-Raviv (BCJR) algorithm to compute a-posteriori bit log-likelihood ratios (LLRs) from apriori LLRs by traversing the code trellis in forward and backward directions. The SISO modules iteratively refine the LLRs by passing the estimates between one another much like the working of a turbine engine. Extrinsic information (the difference between the a-posteriori and a-priori LLRs) is exchanged rather than the a-posteriori LLRs to minimize undesired feedback. All computations are performed in the logarithmic domain, wherein multiplications are translated into additions, thereby reducing complexity and sensitivity to fixed-point implementation roundoff errors. To lower the required memory for storing channel likelihood data and the amounts of data transfer between the decoder and the receiver, one can discard the majority of channel likelihoods, using only the remainder in
Neural decoding of visual imagery during sleep.
Horikawa, T; Tamaki, M; Miyawaki, Y; Kamitani, Y
2013-05-01
Visual imagery during sleep has long been a topic of persistent speculation, but its private nature has hampered objective analysis. Here we present a neural decoding approach in which machine-learning models predict the contents of visual imagery during the sleep-onset period, given measured brain activity, by discovering links between human functional magnetic resonance imaging patterns and verbal reports with the assistance of lexical and image databases. Decoding models trained on stimulus-induced brain activity in visual cortical areas showed accurate classification, detection, and identification of contents. Our findings demonstrate that specific visual experience during sleep is represented by brain activity patterns shared by stimulus perception, providing a means to uncover subjective contents of dreaming using objective neural measurement. PMID:23558170
Olfactory Decoding Method Using Neural Spike Signals
Institute of Scientific and Technical Information of China (English)
Kyung-jin YOU; Hyun-chool SHIN
2010-01-01
This paper presents a travel method for inferring the odor based on naval activities observed from rats'main olfactory bulbs.Mufti-channel extmcellular single unit recordings are done by microwire electrodes(Tungsten,50μm,32 channels)innplanted in the mitral/tufted cell layers of the main olfactory bulb of the anesthetized rats to obtain neural responses to various odors.Neural responses as a key feature are measured by subtraction firing rates before stimulus from after.For odor irderenoe,a decoding method is developed based on the ML estimation.The results show that the average decoding acauacy is about 100.0%,96.0%,and 80.0% with three rats,respectively.This wait has profound implications for a novel brain-madune interface system far odor inference.
Bayesian multiple target tracking
Streit, Roy L
2013-01-01
This second edition has undergone substantial revision from the 1999 first edition, recognizing that a lot has changed in the multiple target tracking field. One of the most dramatic changes is in the widespread use of particle filters to implement nonlinear, non-Gaussian Bayesian trackers. This book views multiple target tracking as a Bayesian inference problem. Within this framework it develops the theory of single target tracking, multiple target tracking, and likelihood ratio detection and tracking. In addition to providing a detailed description of a basic particle filter that implements
Bayesian and frequentist inequality tests
David M. Kaplan; Zhuo, Longhao
2016-01-01
Bayesian and frequentist criteria are fundamentally different, but often posterior and sampling distributions are asymptotically equivalent (and normal). We compare Bayesian and frequentist hypothesis tests of inequality restrictions in such cases. For finite-dimensional parameters, if the null hypothesis is that the parameter vector lies in a certain half-space, then the Bayesian test has (frequentist) size $\\alpha$; if the null hypothesis is any other convex subspace, then the Bayesian test...
Unsupervised learning of facial emotion decoding skills
Jan Oliver Huelle; Benjamin eSack; Katja eBroer; Irina eKomlewa; Silke eAnders
2014-01-01
Research on the mechanisms underlying human facial emotion recognition has long focussed on genetically determined neural algorithms and often neglected the question of how these algorithms might be tuned by social learning. Here we show that facial emotion decoding skills can be significantly and sustainably improved by practice without an external teaching signal. Participants saw video clips of dynamic facial expressions of five different women and were asked to decide which of four possib...
Decoding continuous behavioral variables from neuroimaging data
Directory of Open Access Journals (Sweden)
Jessica R Cohen
2011-06-01
Full Text Available The application of statistical machine learning techniques to neuroimaging data has allowed researchers to decode the cognitive and disease states of participants. The majority of studies using these techniques have focused on pattern classification to decode the type of object a participant is viewing, the type of cognitive task a participant is completing, or the disease state of a participant's brain. However, an emerging body of literature is extending these classification studies to the decoding of values of continuous variables (such as age, cognitive characteristics, or neuropsychological state using high-dimensional regression methods. This review details the methods used in such analyses and describes recent results. We provide specific examples of studies which have used this approach to answer novel questions about age and cognitive and disease states. We conclude that while there is still much to learn about these methods, they provide useful information about the relationship between neural activity and age, cognitive state, and disease state, which could not have been obtained using traditional univariate analytical methods.
Kernel Temporal Differences for Neural Decoding
Directory of Open Access Journals (Sweden)
Jihye Bae
2015-01-01
Full Text Available We study the feasibility and capability of the kernel temporal difference (KTD(λ algorithm for neural decoding. KTD(λ is an online, kernel-based learning algorithm, which has been introduced to estimate value functions in reinforcement learning. This algorithm combines kernel-based representations with the temporal difference approach to learning. One of our key observations is that by using strictly positive definite kernels, algorithm’s convergence can be guaranteed for policy evaluation. The algorithm’s nonlinear functional approximation capabilities are shown in both simulations of policy evaluation and neural decoding problems (policy improvement. KTD can handle high-dimensional neural states containing spatial-temporal information at a reasonable computational complexity allowing real-time applications. When the algorithm seeks a proper mapping between a monkey’s neural states and desired positions of a computer cursor or a robot arm, in both open-loop and closed-loop experiments, it can effectively learn the neural state to action mapping. Finally, a visualization of the coadaptation process between the decoder and the subject shows the algorithm’s capabilities in reinforcement learning brain machine interfaces.
Sequential decoders for large MIMO systems
Ali, Konpal S.
2014-05-01
Due to their ability to provide high data rates, multiple-input multiple-output (MIMO) systems have become increasingly popular. Decoding of these systems with acceptable error performance is computationally very demanding. In this paper, we employ the Sequential Decoder using the Fano Algorithm for large MIMO systems. A parameter called the bias is varied to attain different performance-complexity trade-offs. Low values of the bias result in excellent performance but at the expense of high complexity and vice versa for higher bias values. Numerical results are done that show moderate bias values result in a decent performance-complexity trade-off. We also attempt to bound the error by bounding the bias, using the minimum distance of a lattice. The variations in complexity with SNR have an interesting trend that shows room for considerable improvement. Our work is compared against linear decoders (LDs) aided with Element-based Lattice Reduction (ELR) and Complex Lenstra-Lenstra-Lovasz (CLLL) reduction. © 2014 IFIP.
iBOA: The Incremental Bayesian Optimization Algorithm
Pelikan, Martin; Goldberg, David E
2008-01-01
This paper proposes the incremental Bayesian optimization algorithm (iBOA), which modifies standard BOA by removing the population of solutions and using incremental updates of the Bayesian network. iBOA is shown to be able to learn and exploit unrestricted Bayesian networks using incremental techniques for updating both the structure as well as the parameters of the probabilistic model. This represents an important step toward the design of competent incremental estimation of distribution algorithms that can solve difficult nearly decomposable problems scalably and reliably.
A Modified max-log-MAP Decoding Algorithm for Turbo Decoding
Institute of Scientific and Technical Information of China (English)
无
2002-01-01
Turbo decoding is iterative decoding, and the MAP algorithm is optimal in terms of performance in Turbo decoding. The log-MAP algorithm is the MAP executed in the logarithmic domain, so it is also optimal. Both the MAP and the log-MAP algorithm are complicated for implementation. The max-log-MAP algorithm is derived from the log-MAP with approximation, which is simply compared with the log-MAP algorithm but is suboptimal in terms of performance. A modified max-log-MAP algorithm is presented in this paper, based on the Taylor series of logarithm and exponent. Analysis and simulation results show that the modified max-log-MAP algorithm outperforms the max-log-MAP algorithm with almost the same complexity.
Hardware Implementation of Successive Cancellation Decoders for Polar Codes
Leroux, Camille; Sarkis, Gabi; Tal, Ido; Vardy, Alexander; Gross, Warren J
2011-01-01
The recently-discovered polar codes are seen as a major breakthrough in coding theory; they provably achieve the theoretical capacity of discrete memoryless channels using the low complexity successive cancellation (SC) decoding algorithm. Motivated by recent developments in polar coding theory, we propose a family of efficient hardware implementations for SC polar decoders. We show that such decoders can be implemented with O(n) processing elements, O(n) memory elements, and can provide a constant throughput for a given target clock frequency. Furthermore, we show that SC decoding can be implemented in the logarithm domain, thereby eliminating costly multiplication and division operations and reducing the complexity of each processing element greatly. We also present a detailed architecture for an SC decoder and provide logic synthesis results confirming the linear growth in complexity of the decoder as the code length increases.
Successive Refinement with Decoder Cooperation and its Channel Coding Duals
Asnani, Himanshu; Weissman, Tsachy
2012-01-01
We study cooperation in multi terminal source coding models involving successive refinement. Specifically, we study the case of a single encoder and two decoders, where the encoder provides a common description to both the decoders and a private description to only one of the decoders. The decoders cooperate via cribbing, i.e., the decoder with access only to the common description is allowed to observe, in addition, a deterministic function of the reconstruction symbols produced by the other. We characterize the fundamental performance limits in the respective settings of non-causal, strictly-causal and causal cribbing. We use a new coding scheme, referred to as Forward Encoding and Block Markov Decoding, which is a variant of one recently used by Cuff and Zhao for coordination via implicit communication. Finally, we use the insight gained to introduce and solve some dual channel coding scenarios involving Multiple Access Channels with cribbing.
Performance Analysis of Viterbi Decoder for Wireless Applications
Directory of Open Access Journals (Sweden)
G.Sivasankar
2014-07-01
Full Text Available Viterbi decoder is employed in wireless communication to decode the convolutional codes; those codes are used in every robust digital communication systems. Convolutional encoding and viterbi decoding is a powerful method for forward error correction. This paper deals with synthesis and implementation of viterbi decoder with a constraint length of three as well as seven and the code rate of ½ in FPGA (Field Programmable Gate Array. The performance of viterbi decoder is analyzed in terms of resource utilization. The design of viterbi decoder is simulated using Verilog HDL. It is synthesized and implemented using Xilinx 9.1ise and Spartan 3E Kit. It is compatible with many common standards such as 3GPP, IEEE 802.16 and LTE.
Decoding Delay Controlled Completion Time Reduction in Instantly Decodable Network Coding
Douik, Ahmed
2016-06-27
For several years, the completion time and the decoding delay problems in Instantly Decodable Network Coding (IDNC) were considered separately and were thought to act completely against each other. Recently, some works aimed to balance the effects of these two important IDNC metrics but none of them studied a further optimization of one by controlling the other. This paper investigates the effect of controlling the decoding delay to reduce the completion time below its currently best-known solution in both perfect and imperfect feedback with persistent erasure channels. To solve the problem, the decodingdelay- dependent expressions of the users’ and overall completion times are derived in the complete feedback scenario. Although using such expressions to find the optimal overall completion time is NP-hard, the paper proposes two novel heuristics that minimizes the probability of increasing the maximum of these decoding-delay-dependent completion time expressions after each transmission through a layered control of their decoding delays. Afterward, the paper extends the study to the imperfect feedback scenario in which uncertainties at the sender affects its ability to anticipate accurately the decoding delay increase at each user. The paper formulates the problem in such environment and derives the expression of the minimum increase in the completion time. Simulation results show the performance of the proposed solutions and suggest that both heuristics achieves a lower mean completion time as compared to the best-known heuristics for the completion time reduction in perfect and imperfect feedback. The gap in performance becomes more significant as the erasure of the channel increases.
A. Korattikara; V. Rathod; K. Murphy; M. Welling
2015-01-01
We consider the problem of Bayesian parameter estimation for deep neural networks, which is important in problem settings where we may have little data, and/ or where we need accurate posterior predictive densities p(y|x, D), e.g., for applications involving bandits or active learning. One simple ap
Bayesian logistic regression analysis
Van Erp, H.R.N.; Van Gelder, P.H.A.J.M.
2012-01-01
In this paper we present a Bayesian logistic regression analysis. It is found that if one wishes to derive the posterior distribution of the probability of some event, then, together with the traditional Bayes Theorem and the integrating out of nuissance parameters, the Jacobian transformation is an
Loredo, Thomas J.
2004-04-01
I describe a framework for adaptive scientific exploration based on iterating an Observation-Inference-Design cycle that allows adjustment of hypotheses and observing protocols in response to the results of observation on-the-fly, as data are gathered. The framework uses a unified Bayesian methodology for the inference and design stages: Bayesian inference to quantify what we have learned from the available data and predict future data, and Bayesian decision theory to identify which new observations would teach us the most. When the goal of the experiment is simply to make inferences, the framework identifies a computationally efficient iterative ``maximum entropy sampling'' strategy as the optimal strategy in settings where the noise statistics are independent of signal properties. Results of applying the method to two ``toy'' problems with simulated data-measuring the orbit of an extrasolar planet, and locating a hidden one-dimensional object-show the approach can significantly improve observational efficiency in settings that have well-defined nonlinear models. I conclude with a list of open issues that must be addressed to make Bayesian adaptive exploration a practical and reliable tool for optimizing scientific exploration.
DEFF Research Database (Denmark)
Antoniou, Constantinos; Harrison, Glenn W.; Lau, Morten I.;
2015-01-01
A large literature suggests that many individuals do not apply Bayes’ Rule when making decisions that depend on them correctly pooling prior information and sample data. We replicate and extend a classic experimental study of Bayesian updating from psychology, employing the methods of experimental...
Bayesian Independent Component Analysis
DEFF Research Database (Denmark)
Winther, Ole; Petersen, Kaare Brandt
2007-01-01
In this paper we present an empirical Bayesian framework for independent component analysis. The framework provides estimates of the sources, the mixing matrix and the noise parameters, and is flexible with respect to choice of source prior and the number of sources and sensors. Inside the engine...
DEFF Research Database (Denmark)
Hartelius, Karsten; Carstensen, Jens Michael
2003-01-01
A method for locating distorted grid structures in images is presented. The method is based on the theories of template matching and Bayesian image restoration. The grid is modeled as a deformable template. Prior knowledge of the grid is described through a Markov random field (MRF) model which...
A Bayesian Approach to Identifying New Risk Factors for Dementia
Wen, Yen-Hsia; Wu, Shihn-Sheng; Lin, Chun-Hung Richard; Tsai, Jui-Hsiu; Yang, Pinchen; Chang, Yang-Pei; Tseng, Kuan-Hua
2016-01-01
Abstract Dementia is one of the most disabling and burdensome health conditions worldwide. In this study, we identified new potential risk factors for dementia from nationwide longitudinal population-based data by using Bayesian statistics. We first tested the consistency of the results obtained using Bayesian statistics with those obtained using classical frequentist probability for 4 recognized risk factors for dementia, namely severe head injury, depression, diabetes mellitus, and vascular...
Mitigating Hardware Cyber-Security Risks in Error Correcting Decoders
Hemati, Saied
2015-01-01
This paper investigates hardware cyber-security risks associated with channel decoders, which are commonly acquired as a black box in semiconductor industry. It is shown that channel decoders are potentially attractive targets for hardware cyber-security attacks and can be easily embedded with malicious blocks. Several attack scenarios are considered in this work and suitable methods for mitigating the risks are proposed. These methods are based on randomizing the inputs of the channel decode...
Completion Delay Minimization for Instantly Decodable Network Codes
Sorour, Sameh; Valaee, Shahrokh
2012-01-01
In this paper, we consider the problem of minimizing the completion delay for instantly decodable network coding (IDNC), in wireless multicast and broadcast scenarios. We are interested in this class of network coding due to its numerous benefits, such as low decoding delay, low coding and decoding complexities and simple receiver requirements. We first extend the IDNC graph, which represents all feasible IDNC coding opportunities, to efficiently operate in both multicast and broadcast scenar...
On a turbo decoder design for low power dissipation
Fei, Jia
2000-01-01
(Abstract) A new coding scheme called "turbo coding" has generated tremendous interest in channel coding of digital communication systems due to its high error correcting capability. Two key innovations in turbo coding are parallel concatenated encoding and iterative decoding. A soft-in soft-out component decoder can be implemented using the maximum a posteriori (MAP) or the maximum likelihood (ML) decoding algorithm. While the MAP algorithm offers better performance than the ML algori...
Hardware architectures for Successive Cancellation Decoding of Polar Codes
Leroux, Camille; Tal, Ido; Vardy, Alexander; Gross, Warren J.
2010-01-01
The recently-discovered polar codes are widely seen as a major breakthrough in coding theory. These codes achieve the capacity of many important channels under successive cancellation decoding. Motivated by the rapid progress in the theory of polar codes, we pro pose a family of architectures for efficient hardware implementation of successive cancellation decoders. We show that such decoders can be implemented with O(n) processing elements and O(n) memory elements, while providing constant t...
A Bayesian nonparametric meta-analysis model.
Karabatsos, George; Talbott, Elizabeth; Walker, Stephen G
2015-03-01
In a meta-analysis, it is important to specify a model that adequately describes the effect-size distribution of the underlying population of studies. The conventional normal fixed-effect and normal random-effects models assume a normal effect-size population distribution, conditionally on parameters and covariates. For estimating the mean overall effect size, such models may be adequate, but for prediction, they surely are not if the effect-size distribution exhibits non-normal behavior. To address this issue, we propose a Bayesian nonparametric meta-analysis model, which can describe a wider range of effect-size distributions, including unimodal symmetric distributions, as well as skewed and more multimodal distributions. We demonstrate our model through the analysis of real meta-analytic data arising from behavioral-genetic research. We compare the predictive performance of the Bayesian nonparametric model against various conventional and more modern normal fixed-effects and random-effects models.
Sub-quadratic decoding of one-point hermitian codes
DEFF Research Database (Denmark)
Nielsen, Johan Sebastian Rosenkilde; Beelen, Peter
2015-01-01
We present the first two sub-quadratic complexity decoding algorithms for one-point Hermitian codes. The first is based on a fast realization of the Guruswami-Sudan algorithm using state-of-the-art algorithms from computer algebra for polynomial-ring matrix minimization. The second is a power...... decoding algorithm: an extension of classical key equation decoding which gives a probabilistic decoding algorithm up to the Sudan radius. We show how the resulting key equations can be solved by the matrix minimization algorithms from computer algebra, yielding similar asymptotic complexities....
Approximate Bayesian inference for complex ecosystems
Michael P H Stumpf
2014-01-01
Mathematical models have been central to ecology for nearly a century. Simple models of population dynamics have allowed us to understand fundamental aspects underlying the dynamics and stability of ecological systems. What has remained a challenge, however, is to meaningfully interpret experimental or observational data in light of mathematical models. Here, we review recent developments, notably in the growing field of approximate Bayesian computation (ABC), that allow us to calibrate mathe...
Abts, Koenraad; van Kessel, Stijn
2015-01-01
Populism is a concept applied to a wide range of political movements and actors across the globe. There is, at the same time, considerable confusion about the attributes and manifestation of populism, as well as its impact on democracy. This contribution identifies the defining elements of the populist ideology and discusses the varieties in which populism manifests itself, for instance as a component of certain party families. We finally discuss various normative interpretations of populism,...
Decoding the direction of auditory motion in blind humans.
Wolbers, Thomas; Zahorik, Pavel; Giudice, Nicholas A
2011-05-15
Accurate processing of nonvisual stimuli is fundamental to humans with visual impairments. In this population, moving sounds activate an occipito-temporal region thought to encompass the equivalent of monkey area MT+, but it remains unclear whether the signal carries information beyond the mere presence of motion. To address this important question, we tested whether the processing in this region retains functional properties that are critical for accurate motion processing and that are well established in the visual modality. Specifically, we focussed on the property of 'directional selectivity', because MT+ neurons in non-human primates fire preferentially to specific directions of visual motion. Recent neuroimaging studies have revealed similar properties in sighted humans by successfully decoding different directions of visual motion from fMRI activation patterns. Here we used fMRI and multivariate pattern classification to demonstrate that the direction in which a sound is moving can be reliably decoded from dorsal occipito-temporal activation in the blind. We also show that classification performance is at chance (i) in a control region in posterior parietal cortex and (ii) when motion information is removed and subjects only hear a sequence of static sounds presented at the same start and end positions. These findings reveal that information about the direction of auditory motion is present in dorsal occipito-temporal responses of blind humans. As such, this area, which appears consistent with the hMT+ complex in the sighted, provides crucial information for the generation of a veridical percept of moving non-visual stimuli. PMID:20451630
A primer on equalization, decoding and non-iterative joint equalization and decoding
Myburgh, Hermanus C.; Olivier, Jan C.
2013-12-01
In this article, a general model for non-iterative joint equalization and decoding is systematically derived for use in systems transmitting convolutionally encoded BPSK-modulated information through a multipath channel, with and without interleaving. Optimal equalization and decoding are discussed first, by presenting the maximum likelihood sequence estimation and maximum a posteriori probability algorithms and relating them to equalization in single-carrier channels with memory, and to the decoding of convolutional codes. The non-iterative joint equalizer/decoder (NI-JED) is then derived for the case where no interleaver is used, as well as for the case when block interleavers of varying depths are used, and complexity analyses are performed in each case. Simulation results are performed to compare the performance of the NI-JED to that of a conventional turbo equalizer (CTE), and it is shown that the NI-JED outperforms the CTE, although at much higher computational cost. This article serves to explain the state-of-the-art to students and professionals in the field of wireless communication systems, presenting these fundamental topics clearly and concisely.
Sudan-decoding generalized geometric Goppa codes
DEFF Research Database (Denmark)
Heydtmann, Agnes Eileen
2003-01-01
for these codes based on Sudan's improved algorithm is presented and its error-correcting capacity is analyzed. For the implementation of the algorithm it is necessary that the so-called increasing zero bases of certain spaces of functions are available. A method to obtain such bases is developed.......Generalized geometric Goppa codes are vector spaces of n-tuples with entries from different extension fields of a ground field. They are derived from evaluating functions similar to conventional geometric Goppa codes, but allowing evaluation in places of arbitrary degree. A decoding scheme...
Maximum a posteriori decoder for digital communications
Altes, Richard A. (Inventor)
1997-01-01
A system and method for decoding by identification of the most likely phase coded signal corresponding to received data. The present invention has particular application to communication with signals that experience spurious random phase perturbations. The generalized estimator-correlator uses a maximum a posteriori (MAP) estimator to generate phase estimates for correlation with incoming data samples and for correlation with mean phases indicative of unique hypothesized signals. The result is a MAP likelihood statistic for each hypothesized transmission, wherein the highest value statistic identifies the transmitted signal.
Probability and Bayesian statistics
1987-01-01
This book contains selected and refereed contributions to the "Inter national Symposium on Probability and Bayesian Statistics" which was orga nized to celebrate the 80th birthday of Professor Bruno de Finetti at his birthplace Innsbruck in Austria. Since Professor de Finetti died in 1985 the symposium was dedicated to the memory of Bruno de Finetti and took place at Igls near Innsbruck from 23 to 26 September 1986. Some of the pa pers are published especially by the relationship to Bruno de Finetti's scientific work. The evolution of stochastics shows growing importance of probability as coherent assessment of numerical values as degrees of believe in certain events. This is the basis for Bayesian inference in the sense of modern statistics. The contributions in this volume cover a broad spectrum ranging from foundations of probability across psychological aspects of formulating sub jective probability statements, abstract measure theoretical considerations, contributions to theoretical statistics an...
DEFF Research Database (Denmark)
Mørup, Morten; Schmidt, Mikkel N
2012-01-01
Many networks of scientific interest naturally decompose into clusters or communities with comparatively fewer external than internal links; however, current Bayesian models of network communities do not exert this intuitive notion of communities. We formulate a nonparametric Bayesian model...... for community detection consistent with an intuitive definition of communities and present a Markov chain Monte Carlo procedure for inferring the community structure. A Matlab toolbox with the proposed inference procedure is available for download. On synthetic and real networks, our model detects communities...... consistent with ground truth, and on real networks, it outperforms existing approaches in predicting missing links. This suggests that community structure is an important structural property of networks that should be explicitly modeled....
Brody, Samuel; Lapata, Mirella
2009-01-01
Sense induction seeks to automatically identify word senses directly from a corpus. A key assumption underlying previous work is that the context surrounding an ambiguous word is indicative of its meaning. Sense induction is thus typically viewed as an unsupervised clustering problem where the aim is to partition a word’s contexts into different classes, each representing a word sense. Our work places sense induction in a Bayesian context by modeling the contexts of the ambiguous word as samp...
Bayesian Generalized Rating Curves
Helgi Sigurðarson 1985
2014-01-01
A rating curve is a curve or a model that describes the relationship between water elevation, or stage, and discharge in an observation site in a river. The rating curve is fit from paired observations of stage and discharge. The rating curve then predicts discharge given observations of stage and this methodology is applied as stage is substantially easier to directly observe than discharge. In this thesis a statistical rating curve model is proposed working within the framework of Bayesian...
Efficient Bayesian Phase Estimation
Wiebe, Nathan; Granade, Chris
2016-07-01
We introduce a new method called rejection filtering that we use to perform adaptive Bayesian phase estimation. Our approach has several advantages: it is classically efficient, easy to implement, achieves Heisenberg limited scaling, resists depolarizing noise, tracks time-dependent eigenstates, recovers from failures, and can be run on a field programmable gate array. It also outperforms existing iterative phase estimation algorithms such as Kitaev's method.
Directory of Open Access Journals (Sweden)
Jiri eHammer
2013-11-01
Full Text Available In neuronal population signals, including the electroencephalogram (EEG and electrocorticogram (ECoG, the low-frequency component (LFC is particularly informative about motor behavior and can be used for decoding movement parameters for brain-machine interface (BMI applications. An idea previously expressed, but as of yet not quantitatively tested, is that it is the LFC phase that is the main source of decodable information. To test this issue, we analyzed human ECoG recorded during a game-like, one-dimensional, continuous motor task with a novel decoding method suitable for unfolding magnitude and phase explicitly into a complex-valued, time-frequency signal representation, enabling quantification of the decodable information within the temporal, spatial and frequency domains and allowing disambiguation of the phase contribution from that of the spectral magnitude. The decoding accuracy based only on phase information was substantially (at least 2 fold and significantly higher than that based only on magnitudes for position, velocity and acceleration. The frequency profile of movement-related information in the ECoG data matched well with the frequency profile expected when assuming a close time-domain correlate of movement velocity in the ECoG, e.g., a (noisy copy of hand velocity. No such match was observed with the frequency profiles expected when assuming a copy of either hand position or acceleration. There was also no indication of additional magnitude-based mechanisms encoding movement information in the LFC range. Thus, our study contributes to elucidating the nature of the informative low-frequency component of motor cortical population activity and may hence contribute to improve decoding strategies and BMI performance.
Klok, T.C.; Holmstrup, M.; Damgaard, C.
2007-01-01
Matrix models can be used to extrapolate effects of environmental toxicants on life history parameters to the population level. In applications of these models, life history parameters are usually treated as independent factors; however, they are actually strongly linked to each other. To interpret
Bayesian theory and applications
Dellaportas, Petros; Polson, Nicholas G; Stephens, David A
2013-01-01
The development of hierarchical models and Markov chain Monte Carlo (MCMC) techniques forms one of the most profound advances in Bayesian analysis since the 1970s and provides the basis for advances in virtually all areas of applied and theoretical Bayesian statistics. This volume guides the reader along a statistical journey that begins with the basic structure of Bayesian theory, and then provides details on most of the past and present advances in this field. The book has a unique format. There is an explanatory chapter devoted to each conceptual advance followed by journal-style chapters that provide applications or further advances on the concept. Thus, the volume is both a textbook and a compendium of papers covering a vast range of topics. It is appropriate for a well-informed novice interested in understanding the basic approach, methods and recent applications. Because of its advanced chapters and recent work, it is also appropriate for a more mature reader interested in recent applications and devel...
Wiegerinck, Wim; Schoenaker, Christiaan; Duane, Gregory
2016-04-01
Recently, methods for model fusion by dynamically combining model components in an interactive ensemble have been proposed. In these proposals, fusion parameters have to be learned from data. One can view these systems as parametrized dynamical systems. We address the question of learnability of dynamical systems with respect to both short term (vector field) and long term (attractor) behavior. In particular we are interested in learning in the imperfect model class setting, in which the ground truth has a higher complexity than the models, e.g. due to unresolved scales. We take a Bayesian point of view and we define a joint log-likelihood that consists of two terms, one is the vector field error and the other is the attractor error, for which we take the L1 distance between the stationary distributions of the model and the assumed ground truth. In the context of linear models (like so-called weighted supermodels), and assuming a Gaussian error model in the vector fields, vector field learning leads to a tractable Gaussian solution. This solution can then be used as a prior for the next step, Bayesian attractor learning, in which the attractor error is used as a log-likelihood term. Bayesian attractor learning is implemented by elliptical slice sampling, a sampling method for systems with a Gaussian prior and a non Gaussian likelihood. Simulations with a partially observed driven Lorenz 63 system illustrate the approach.
Bayesian analysis of the Hector’s Dolphin data
King, R; Brooks, S.P.
2004-01-01
In recent years there have been increasing concerns for many wildlife populations, due to decreasing population trends. This has led to the introduction of management schemes to increase the survival rates and hence the population size of many species of animals. We concentrate on a particular dolphin population situated off the coast of New Zealand, and investigate whether the introduction of a fishing gill net ban was effective in decreasing dolphin mortality. We undertake a Bayesian analys...
Decoding of concatenated codes with interleaved outer codes
DEFF Research Database (Denmark)
Justesen, Jørn; Høholdt, Tom; Thommesen, Christian
2004-01-01
Recently Bleichenbacher et al. proposed a decoding algorithm for interleaved (N, K) Reed-Solomon codes, which allows close to N-K errors to be corrected in many cases. We discuss the application of this decoding algorithm to concatenated codes....
Decoding Information in the Human Hippocampus: A User's Guide
Chadwick, Martin J.; Bonnici, Heidi M.; Maguire, Eleanor A.
2012-01-01
Multi-voxel pattern analysis (MVPA), or "decoding", of fMRI activity has gained popularity in the neuroimaging community in recent years. MVPA differs from standard fMRI analyses by focusing on whether information relating to specific stimuli is encoded in patterns of activity across multiple voxels. If a stimulus can be predicted, or decoded,…
Decoding Technique of Concatenated Hadamard Codes and Its Performance
Institute of Scientific and Technical Information of China (English)
无
1999-01-01
The decoding technique of concatenated Hadamard codes and its performance are studied. Efficient softin-soft-out decoding algorithms based on the fast Hadamard transform are developed. Performance required by CDMA mobile or PCS speech services, e.g. , BER= 10-3, can be achieved at Eb/No = 0.9 dB using short interleaving length of 192 bits.
Decoding the Ternary (23, 11, 9 Quadratic Residue Code
Directory of Open Access Journals (Sweden)
J. Carmelo Interlando
2009-01-01
Full Text Available The algebraic decoding of binary quadratic residue codes can be performed using the Peterson or the Berlekamp-Massey algorithm once certain unknown syndromes are determined or eliminated. The technique of determining unknown syndromes is applied to the nonbinary case to decode the expurgated ternary quadratic residue code of length 23.
A Method of Coding and Decoding in Underwater Image Transmission
Institute of Scientific and Technical Information of China (English)
程恩
2001-01-01
A new method of coding and decoding in the system of underwater image transmission is introduced, including the rapid digital frequency synthesizer in multiple frequency shift keying,image data generator, image grayscale decoder with intelligent fuzzy algorithm, image restoration and display on microcomputer.
Bayer image parallel decoding based on GPU
Hu, Rihui; Xu, Zhiyong; Wei, Yuxing; Sun, Shaohua
2012-11-01
In the photoelectrical tracking system, Bayer image is decompressed in traditional method, which is CPU-based. However, it is too slow when the images become large, for example, 2K×2K×16bit. In order to accelerate the Bayer image decoding, this paper introduces a parallel speedup method for NVIDA's Graphics Processor Unit (GPU) which supports CUDA architecture. The decoding procedure can be divided into three parts: the first is serial part, the second is task-parallelism part, and the last is data-parallelism part including inverse quantization, inverse discrete wavelet transform (IDWT) as well as image post-processing part. For reducing the execution time, the task-parallelism part is optimized by OpenMP techniques. The data-parallelism part could advance its efficiency through executing on the GPU as CUDA parallel program. The optimization techniques include instruction optimization, shared memory access optimization, the access memory coalesced optimization and texture memory optimization. In particular, it can significantly speed up the IDWT by rewriting the 2D (Tow-dimensional) serial IDWT into 1D parallel IDWT. Through experimenting with 1K×1K×16bit Bayer image, data-parallelism part is 10 more times faster than CPU-based implementation. Finally, a CPU+GPU heterogeneous decompression system was designed. The experimental result shows that it could achieve 3 to 5 times speed increase compared to the CPU serial method.
Decoding suprathreshold stochastic resonance with optimal weights
Energy Technology Data Exchange (ETDEWEB)
Xu, Liyan, E-mail: xuliyan@qdu.edu.cn [Institute of Complexity Science, Qingdao University, Qingdao 266071 (China); Vladusich, Tony [Computational and Theoretical Neuroscience Laboratory, Institute for Telecommunications Research, School of Information Technology and Mathematical Sciences, University of South Australia, SA 5095 (Australia); Duan, Fabing [Institute of Complexity Science, Qingdao University, Qingdao 266071 (China); Gunn, Lachlan J.; Abbott, Derek [Centre for Biomedical Engineering (CBME) and School of Electrical & Electronic Engineering, The University of Adelaide, Adelaide, SA 5005 (Australia); McDonnell, Mark D. [Computational and Theoretical Neuroscience Laboratory, Institute for Telecommunications Research, School of Information Technology and Mathematical Sciences, University of South Australia, SA 5095 (Australia); Centre for Biomedical Engineering (CBME) and School of Electrical & Electronic Engineering, The University of Adelaide, Adelaide, SA 5005 (Australia)
2015-10-09
We investigate an array of stochastic quantizers for converting an analog input signal into a discrete output in the context of suprathreshold stochastic resonance. A new optimal weighted decoding is considered for different threshold level distributions. We show that for particular noise levels and choices of the threshold levels optimally weighting the quantizer responses provides a reduced mean square error in comparison with the original unweighted array. However, there are also many parameter regions where the original array provides near optimal performance, and when this occurs, it offers a much simpler approach than optimally weighting each quantizer's response. - Highlights: • A weighted summing array of independently noisy binary comparators is investigated. • We present an optimal linearly weighted decoding scheme for combining the comparator responses. • We solve for the optimal weights by applying least squares regression to simulated data. • We find that the MSE distortion of weighting before summation is superior to unweighted summation of comparator responses. • For some parameter regions, the decrease in MSE distortion due to weighting is negligible.
FPGA Prototyping of RNN Decoder for Convolutional Codes
Directory of Open Access Journals (Sweden)
Salcic Zoran
2006-01-01
Full Text Available This paper presents prototyping of a recurrent type neural network (RNN convolutional decoder using system-level design specification and design flow that enables easy mapping to the target FPGA architecture. Implementation and the performance measurement results have shown that an RNN decoder for hard-decision decoding coupled with a simple hard-limiting neuron activation function results in a very low complexity, which easily fits into standard Altera FPGA. Moreover, the design methodology allowed modeling of complete testbed for prototyping RNN decoders in simulation and real-time environment (same FPGA, thus enabling evaluation of BER performance characteristics of the decoder for various conditions of communication channel in real time.
Hardware architectures for Successive Cancellation Decoding of Polar Codes
Leroux, Camille; Vardy, Alexander; Gross, Warren J
2010-01-01
The recently-discovered polar codes are widely seen as a major breakthrough in coding theory. These codes achieve the capacity of many important channels under successive cancellation decoding. Motivated by the rapid progress in the theory of polar codes, we propose a family of architectures for efficient hardware implementation of successive cancellation decoders. We show that such decoders can be implemented with O(n) processing elements and O(n) memory elements, while providing constant throughput. We also propose a technique for overlapping the decoding of several consecutive codewords, thereby achieving a significant speed-up factor. We furthermore show that successive cancellation decoding can be implemented in the logarithmic domain, thereby eliminating the multiplication and division operations and greatly reducing the complexity of each processing element.
Iterative Soft Decision Based Complex K-best MIMO Decoder
Directory of Open Access Journals (Sweden)
Mehnaz Rahman
2015-11-01
Full Text Available This paper presents an iterative soft decision based complex multiple input multiple output (MIMO decoding algorithm, which reduces the complexity of Maximum Likelihood (ML detector. We develop a novel iterative complex K-best decoder exploiting the techniques of lattice reduction for 8×8 MIMO. Besides list size, a new adjustable variable has been introduced in order to control the on-demand child expansion. Following this method, we obtain 6.9 to 8.0 dB improvement over real domain K-best decoder and 1.4 to 2.5 dB better performance compared to iterative conventional complex decoder for 4th iteration and 64-QAM modulation scheme. We also demonstrate the significance of new parameter on bit error rate. The proposed decoder not only increases the performance, but also reduces the computational complexity to a certain level.
Multilevel Decoders Surpassing Belief Propagation on the Binary Symmetric Channel
Planjery, Shiva Kumar; Chilappagari, Shashi Kiran; Vasić, Bane
2010-01-01
In this paper, we propose a new class of quantized message-passing decoders for LDPC codes over the BSC. The messages take values (or levels) from a finite set. The update rules do not mimic belief propagation but instead are derived using the knowledge of trapping sets. We show that the update rules can be derived to correct certain error patterns that are uncorrectable by algorithms such as BP and min-sum. In some cases even with a small message set, these decoders can guarantee correction of a higher number of errors than BP and min-sum. We provide particularly good 3-bit decoders for 3-left-regular LDPC codes. They significantly outperform the BP and min-sum decoders, but more importantly, they achieve this at only a fraction of the complexity of the BP and min-sum decoders.
Iterative List Decoding of Concatenated Source-Channel Codes
Directory of Open Access Journals (Sweden)
Hedayat Ahmadreza
2005-01-01
Full Text Available Whenever variable-length entropy codes are used in the presence of a noisy channel, any channel errors will propagate and cause significant harm. Despite using channel codes, some residual errors always remain, whose effect will get magnified by error propagation. Mitigating this undesirable effect is of great practical interest. One approach is to use the residual redundancy of variable length codes for joint source-channel decoding. In this paper, we improve the performance of residual redundancy source-channel decoding via an iterative list decoder made possible by a nonbinary outer CRC code. We show that the list decoding of VLC's is beneficial for entropy codes that contain redundancy. Such codes are used in state-of-the-art video coders, for example. The proposed list decoder improves the overall performance significantly in AWGN and fully interleaved Rayleigh fading channels.
Bayesian optimization for materials design
Frazier, Peter I.; Wang, Jialei
2015-01-01
We introduce Bayesian optimization, a technique developed for optimizing time-consuming engineering simulations and for fitting machine learning models on large datasets. Bayesian optimization guides the choice of experiments during materials design and discovery to find good material designs in as few experiments as possible. We focus on the case when materials designs are parameterized by a low-dimensional vector. Bayesian optimization is built on a statistical technique called Gaussian pro...
Reduced complexity turbo equalization using a dynamic Bayesian network
Myburgh, Hermanus C.; Olivier, Jan C.; van Zyl, Augustinus J.
2012-12-01
It is proposed that a dynamic Bayesian network (DBN) is used to perform turbo equalization in a system transmitting information over a Rayleigh fading multipath channel. The DBN turbo equalizer (DBN-TE) is modeled on a single directed acyclic graph by relaxing the Markov assumption and allowing weak connections to past and future states. Its complexity is exponential in encoder constraint length and approximately linear in the channel memory length. Results show that the performance of the DBN-TE closely matches that of a traditional turbo equalizer that uses a maximum a posteriori equalizer and decoder pair. The DBN-TE achieves full convergence and near-optimal performance after small number of iterations.
Lattice Sequential Decoder for Coded MIMO Channel: Performance and Complexity Analysis
Abediseid, Walid
2011-01-01
In this paper, the performance limit of lattice sequential decoder for lattice space-time coded MIMO channel is analysed. We determine the rates achievable by lattice coding and sequential decoding applied to such channel. The diversity-multiplexing tradeoff (DMT) under lattice sequential decoding is derived as a function of its parameter---the bias term. The bias parameter is critical for controlling the amount of computations required at the decoding stage. Achieving low decoding complexity requires increasing the value of the bias term. However, this is done at the expense of losing the optimal tradeoff of the channel. We show how such a decoder can bridge the gap between lattice decoder and low complexity decoders. Moreover, the computational complexity of lattice sequential decoder is analysed. Specifically, we derive the tail distribution of the decoder's computational complexity in the high signal-to-noise ratio regime. Similar to the conventional sequential decoder used in discrete memoryless channel,...
Bayesian Posteriors Without Bayes' Theorem
Hill, Theodore P
2012-01-01
The classical Bayesian posterior arises naturally as the unique solution of several different optimization problems, without the necessity of interpreting data as conditional probabilities and then using Bayes' Theorem. For example, the classical Bayesian posterior is the unique posterior that minimizes the loss of Shannon information in combining the prior and the likelihood distributions. These results, direct corollaries of recent results about conflations of probability distributions, reinforce the use of Bayesian posteriors, and may help partially reconcile some of the differences between classical and Bayesian statistics.
Congdon, Peter
2014-01-01
This book provides an accessible approach to Bayesian computing and data analysis, with an emphasis on the interpretation of real data sets. Following in the tradition of the successful first edition, this book aims to make a wide range of statistical modeling applications accessible using tested code that can be readily adapted to the reader's own applications. The second edition has been thoroughly reworked and updated to take account of advances in the field. A new set of worked examples is included. The novel aspect of the first edition was the coverage of statistical modeling using WinBU
Computationally efficient Bayesian tracking
Aughenbaugh, Jason; La Cour, Brian
2012-06-01
In this paper, we describe the progress we have achieved in developing a computationally efficient, grid-based Bayesian fusion tracking system. In our approach, the probability surface is represented by a collection of multidimensional polynomials, each computed adaptively on a grid of cells representing state space. Time evolution is performed using a hybrid particle/grid approach and knowledge of the grid structure, while sensor updates use a measurement-based sampling method with a Delaunay triangulation. We present an application of this system to the problem of tracking a submarine target using a field of active and passive sonar buoys.
Bayesian nonparametric data analysis
Müller, Peter; Jara, Alejandro; Hanson, Tim
2015-01-01
This book reviews nonparametric Bayesian methods and models that have proven useful in the context of data analysis. Rather than providing an encyclopedic review of probability models, the book’s structure follows a data analysis perspective. As such, the chapters are organized by traditional data analysis problems. In selecting specific nonparametric models, simpler and more traditional models are favored over specialized ones. The discussed methods are illustrated with a wealth of examples, including applications ranging from stylized examples to case studies from recent literature. The book also includes an extensive discussion of computational methods and details on their implementation. R code for many examples is included in on-line software pages.
Bayesian Geostatistical Design
DEFF Research Database (Denmark)
Diggle, Peter; Lophaven, Søren Nymand
2006-01-01
locations to, or deletion of locations from, an existing design, and prospective design, which consists of choosing positions for a new set of sampling locations. We propose a Bayesian design criterion which focuses on the goal of efficient spatial prediction whilst allowing for the fact that model......This paper describes the use of model-based geostatistics for choosing the set of sampling locations, collectively called the design, to be used in a geostatistical analysis. Two types of design situation are considered. These are retrospective design, which concerns the addition of sampling...
Encoder-decoder optimization for brain-computer interfaces.
Directory of Open Access Journals (Sweden)
Josh Merel
2015-06-01
Full Text Available Neuroprosthetic brain-computer interfaces are systems that decode neural activity into useful control signals for effectors, such as a cursor on a computer screen. It has long been recognized that both the user and decoding system can adapt to increase the accuracy of the end effector. Co-adaptation is the process whereby a user learns to control the system in conjunction with the decoder adapting to learn the user's neural patterns. We provide a mathematical framework for co-adaptation and relate co-adaptation to the joint optimization of the user's control scheme ("encoding model" and the decoding algorithm's parameters. When the assumptions of that framework are respected, co-adaptation cannot yield better performance than that obtainable by an optimal initial choice of fixed decoder, coupled with optimal user learning. For a specific case, we provide numerical methods to obtain such an optimized decoder. We demonstrate our approach in a model brain-computer interface system using an online prosthesis simulator, a simple human-in-the-loop pyschophysics setup which provides a non-invasive simulation of the BCI setting. These experiments support two claims: that users can learn encoders matched to fixed, optimal decoders and that, once learned, our approach yields expected performance advantages.
Decoding Generalized Concatenated Codes Using Interleaved Reed-Solomon Codes
Senger, Christian; Bossert, Martin; Zyablov, Victor
2008-01-01
Generalized Concatenated codes are a code construction consisting of a number of outer codes whose code symbols are protected by an inner code. As outer codes, we assume the most frequently used Reed-Solomon codes; as inner code, we assume some linear block code which can be decoded up to half its minimum distance. Decoding up to half the minimum distance of Generalized Concatenated codes is classically achieved by the Blokh-Zyablov-Dumer algorithm, which iteratively decodes by first using the inner decoder to get an estimate of the outer code words and then using an outer error/erasure decoder with a varying number of erasures determined by a set of pre-calculated thresholds. In this paper, a modified version of the Blokh-Zyablov-Dumer algorithm is proposed, which exploits the fact that a number of outer Reed-Solomon codes with average minimum distance d can be grouped into one single Interleaved Reed-Solomon code which can be decoded beyond d/2. This allows to skip a number of decoding iterations on the one...
On Multiple Decoding Attempts for Reed-Solomon Codes
Nguyen, Phong S; Narayanan, Krishna R
2010-01-01
One popular approach to soft-decision decoding of Reed-Solomon (RS) codes is based on the idea of using multiple trials of a simple RS decoding algorithm in combination with successively erasing or flipping a set of symbols or bits in each trial. In this paper, we present an framework based on rate-distortion (RD) theory to analyze such multiple-decoding algorithms for RS codes. By defining an appropriate distortion measure between an error pattern and an erasure pattern, it is shown that, for a single errors-and-erasures decoding trial, the condition for successful decoding is equivalent to the condition that the distortion is smaller than a fixed threshold. Finding the best set of erasure patterns for multiple decoding trials then turns out to be a covering problem which can be solved asymptotically by rate-distortion theory. Thus, the proposed approach can be used to understand the asymptotic performance-versus-complexity trade-off of multiple errors-and-erasures decoding of RS codes. We also consider an a...
O2-GIDNC: Beyond instantly decodable network coding
Aboutorab, Neda
2013-06-01
In this paper, we are concerned with extending the graph representation of generalized instantly decodable network coding (GIDNC) to a more general opportunistic network coding (ONC) scenario, referred to as order-2 GIDNC (O2-GIDNC). In the O2-GIDNC scheme, receivers can store non-instantly decodable packets (NIDPs) comprising two of their missing packets, and use them in a systematic way for later decodings. Once this graph representation is found, it can be used to extend the GIDNC graph-based analyses to the proposed O2-GIDNC scheme with a limited increase in complexity. In the proposed O2-GIDNC scheme, the information of the stored NIDPs at the receivers and the decoding opportunities they create can be exploited to improve the broadcast completion time and decoding delay compared to traditional GIDNC scheme. The completion time and decoding delay minimizing algorithms that can operate on the new O2-GIDNC graph are further described. The simulation results show that our proposed O2-GIDNC improves the completion time and decoding delay performance of the traditional GIDNC. © 2013 IEEE.
Coding and Decoding for the Dynamic Decode and Forward Relay Protocol
Kumar, K Raj
2008-01-01
We study the Dynamic Decode and Forward (DDF) protocol for a single half-duplex relay, single-antenna channel with quasi-static fading. The DDF protocol is well-known and has been analyzed in terms of the Diversity-Multiplexing Tradeoff (DMT) in the infinite block length limit. We characterize the finite block length DMT and give new explicit code constructions. The finite block length analysis illuminates a few key aspects that have been neglected in the previous literature: 1) we show that one dominating cause of degradation with respect to the infinite block length regime is the event of decoding error at the relay; 2) we explicitly take into account the fact that the destination does not generally know a priori the relay decision time at which the relay switches from listening to transmit mode. Both the above problems can be tackled by a careful design of the decoding algorithm. In particular, we introduce a decision rejection criterion at the relay based on Forney's decision rule (a variant of the Neyman...
Academic Training - Bioinformatics: Decoding the Genome
Chris Jones
2006-01-01
ACADEMIC TRAINING LECTURE SERIES 27, 28 February 1, 2, 3 March 2006 from 11:00 to 12:00 - Auditorium, bldg. 500 Decoding the Genome A special series of 5 lectures on: Recent extraordinary advances in the life sciences arising through new detection technologies and bioinformatics The past five years have seen an extraordinary change in the information and tools available in the life sciences. The sequencing of the human genome, the discovery that we possess far fewer genes than foreseen, the measurement of the tiny changes in the genomes that differentiate us, the sequencing of the genomes of many pathogens that lead to diseases such as malaria are all examples of completely new information that is now available in the quest for improved healthcare. New tools have allowed similar strides in the discovery of the associated protein structures, providing invaluable information for those searching for new drugs. New DNA microarray chips permit simultaneous measurement of the state of expression of tens...
Decoding the mechanisms of Antikythera astronomical device
Lin, Jian-Liang
2016-01-01
This book presents a systematic design methodology for decoding the interior structure of the Antikythera mechanism, an astronomical device from ancient Greece. The historical background, surviving evidence and reconstructions of the mechanism are introduced, and the historical development of astronomical achievements and various astronomical instruments are investigated. Pursuing an approach based on the conceptual design of modern mechanisms and bearing in mind the standards of science and technology at the time, all feasible designs of the six lost/incomplete/unclear subsystems are synthesized as illustrated examples, and 48 feasible designs of the complete interior structure are presented. This approach provides not only a logical tool for applying modern mechanical engineering knowledge to the reconstruction of the Antikythera mechanism, but also an innovative research direction for identifying the original structures of the mechanism in the future. In short, the book offers valuable new insights for all...
Interference Alignment for Clustered Multicell Joint Decoding
Chatzinotas, Symeon
2010-01-01
Multicell joint processing has been proven to be very efficient in overcoming the interference-limited nature of the cellular paradigm. However, for reasons of practical implementation global multicell joint decoding is not feasible and thus clusters of cooperating Base Stations have to be considered. In this context, intercluster interference has to be mitigated in order to harvest the full potential of multicell joint processing. In this paper, four scenarios of intercluster interference are investigated, namely a) global multicell joint processing, b) interference alignment, c) resource division multiple access and d) cochannel interference allowance. Each scenario is modelled and analyzed using the per-cell ergodic sum-rate capacity as a figure of merit. In this process, a number of theorems are derived for analytically expressing the asymptotic eigenvalue distributions of the channel covariance matrices. The analysis is based on principles from Free Probability theory and especially properties in the R a...
Inference in hybrid Bayesian networks
DEFF Research Database (Denmark)
Lanseth, Helge; Nielsen, Thomas Dyhre; Rumí, Rafael;
2009-01-01
Since the 1980s, Bayesian Networks (BNs) have become increasingly popular for building statistical models of complex systems. This is particularly true for boolean systems, where BNs often prove to be a more efficient modelling framework than traditional reliability-techniques (like fault trees...... decade's research on inference in hybrid Bayesian networks. The discussions are linked to an example model for estimating human reliability....
Directory of Open Access Journals (Sweden)
Archana Venkataraman
2015-01-01
Full Text Available Resting-state functional magnetic resonance imaging (rsfMRI studies reveal a complex pattern of hyper- and hypo-connectivity in children with autism spectrum disorder (ASD. Whereas rsfMRI findings tend to implicate the default mode network and subcortical areas in ASD, task fMRI and behavioral experiments point to social dysfunction as a unifying impairment of the disorder. Here, we leverage a novel Bayesian framework for whole-brain functional connectomics that aggregates population differences in connectivity to localize a subset of foci that are most affected by ASD. Our approach is entirely data-driven and does not impose spatial constraints on the region foci or dictate the trajectory of altered functional pathways. We apply our method to data from the openly shared Autism Brain Imaging Data Exchange (ABIDE and pinpoint two intrinsic functional networks that distinguish ASD patients from typically developing controls. One network involves foci in the right temporal pole, left posterior cingulate cortex, left supramarginal gyrus, and left middle temporal gyrus. Automated decoding of this network by the Neurosynth meta-analytic database suggests high-level concepts of “language” and “comprehension” as the likely functional correlates. The second network consists of the left banks of the superior temporal sulcus, right posterior superior temporal sulcus extending into temporo-parietal junction, and right middle temporal gyrus. Associated functionality of these regions includes “social” and “person”. The abnormal pathways emanating from the above foci indicate that ASD patients simultaneously exhibit reduced long-range or inter-hemispheric connectivity and increased short-range or intra-hemispheric connectivity. Our findings reveal new insights into ASD and highlight possible neural mechanisms of the disorder.
Venkataraman, Archana; Duncan, James S; Yang, Daniel Y-J; Pelphrey, Kevin A
2015-01-01
Resting-state functional magnetic resonance imaging (rsfMRI) studies reveal a complex pattern of hyper- and hypo-connectivity in children with autism spectrum disorder (ASD). Whereas rsfMRI findings tend to implicate the default mode network and subcortical areas in ASD, task fMRI and behavioral experiments point to social dysfunction as a unifying impairment of the disorder. Here, we leverage a novel Bayesian framework for whole-brain functional connectomics that aggregates population differences in connectivity to localize a subset of foci that are most affected by ASD. Our approach is entirely data-driven and does not impose spatial constraints on the region foci or dictate the trajectory of altered functional pathways. We apply our method to data from the openly shared Autism Brain Imaging Data Exchange (ABIDE) and pinpoint two intrinsic functional networks that distinguish ASD patients from typically developing controls. One network involves foci in the right temporal pole, left posterior cingulate cortex, left supramarginal gyrus, and left middle temporal gyrus. Automated decoding of this network by the Neurosynth meta-analytic database suggests high-level concepts of "language" and "comprehension" as the likely functional correlates. The second network consists of the left banks of the superior temporal sulcus, right posterior superior temporal sulcus extending into temporo-parietal junction, and right middle temporal gyrus. Associated functionality of these regions includes "social" and "person". The abnormal pathways emanating from the above foci indicate that ASD patients simultaneously exhibit reduced long-range or inter-hemispheric connectivity and increased short-range or intra-hemispheric connectivity. Our findings reveal new insights into ASD and highlight possible neural mechanisms of the disorder.
Bayesian Inference on Gravitational Waves
Directory of Open Access Journals (Sweden)
Asad Ali
2015-12-01
Full Text Available The Bayesian approach is increasingly becoming popular among the astrophysics data analysis communities. However, the Pakistan statistics communities are unaware of this fertile interaction between the two disciplines. Bayesian methods have been in use to address astronomical problems since the very birth of the Bayes probability in eighteenth century. Today the Bayesian methods for the detection and parameter estimation of gravitational waves have solid theoretical grounds with a strong promise for the realistic applications. This article aims to introduce the Pakistan statistics communities to the applications of Bayesian Monte Carlo methods in the analysis of gravitational wave data with an overview of the Bayesian signal detection and estimation methods and demonstration by a couple of simplified examples.
Turbo decoder architecture for beyond-4G applications
Wong, Cheng-Chi
2013-01-01
This book describes the most recent techniques for turbo decoder implementation, especially for 4G and beyond 4G applications. The authors reveal techniques for the design of high-throughput decoders for future telecommunication systems, enabling designers to reduce hardware cost and shorten processing time. Coverage includes an explanation of VLSI implementation of the turbo decoder, from basic functional units to advanced parallel architecture. The authors discuss both hardware architecture techniques and experimental results, showing the variations in area/throughput/performance with respec
Simplified Syndrome Decoding of (n, 1) Convolutional Codes
Reed, I. S.; Truong, T. K.
1983-01-01
A new syndrome decoding algorithm for the (n, 1) convolutional codes (CC) that is different and simpler than the previous syndrome decoding algorithm of Schalkwijk and Vinck is presented. The new algorithm uses the general solution of the polynomial linear Diophantine equation for the error polynomial vector E(D). This set of Diophantine solutions is a coset of the CC space. A recursive or Viterbi-like algorithm is developed to find the minimum weight error vector cirumflex E(D) in this error coset. An example illustrating the new decoding algorithm is given for the binary nonsymmetric (2,1)CC.
New syndrome decoding techniques for the (n, k) convolutional codes
Reed, I. S.; Truong, T. K.
1984-01-01
This paper presents a new syndrome decoding algorithm for the (n, k) convolutional codes (CC) which differs completely from an earlier syndrome decoding algorithm of Schalkwijk and Vinck. The new algorithm is based on the general solution of the syndrome equation, a linear Diophantine equation for the error polynomial vector E(D). The set of Diophantine solutions is a coset of the CC. In this error coset a recursive, Viterbi-like algorithm is developed to find the minimum weight error vector (circumflex)E(D). An example, illustrating the new decoding algorithm, is given for the binary nonsystemmatic (3, 1)CC. Previously announced in STAR as N83-34964
Locally decodable codes and private information retrieval schemes
Yekhanin, Sergey
2010-01-01
Locally decodable codes (LDCs) are codes that simultaneously provide efficient random access retrieval and high noise resilience by allowing reliable reconstruction of an arbitrary bit of a message by looking at only a small number of randomly chosen codeword bits. Local decodability comes with a certain loss in terms of efficiency - specifically, locally decodable codes require longer codeword lengths than their classical counterparts. Private information retrieval (PIR) schemes are cryptographic protocols designed to safeguard the privacy of database users. They allow clients to retrieve rec
Multiple LDPC decoding for distributed source coding and video coding
DEFF Research Database (Denmark)
Forchhammer, Søren; Luong, Huynh Van; Huang, Xin
2011-01-01
Distributed source coding (DSC) is a coding paradigm for systems which fully or partly exploit the source statistics at the decoder to reduce the computational burden at the encoder. Distributed video coding (DVC) is one example. This paper considers the use of Low Density Parity Check Accumulate...... (LDPCA) codes in a DSC scheme with feed-back. To improve the LDPC coding performance in the context of DSC and DVC, while retaining short encoder blocks, this paper proposes multiple parallel LDPC decoding. The proposed scheme passes soft information between decoders to enhance performance. Experimental...
Bayesian coestimation of phylogeny and sequence alignment
Directory of Open Access Journals (Sweden)
Jensen Jens
2005-04-01
Full Text Available Abstract Background Two central problems in computational biology are the determination of the alignment and phylogeny of a set of biological sequences. The traditional approach to this problem is to first build a multiple alignment of these sequences, followed by a phylogenetic reconstruction step based on this multiple alignment. However, alignment and phylogenetic inference are fundamentally interdependent, and ignoring this fact leads to biased and overconfident estimations. Whether the main interest be in sequence alignment or phylogeny, a major goal of computational biology is the co-estimation of both. Results We developed a fully Bayesian Markov chain Monte Carlo method for coestimating phylogeny and sequence alignment, under the Thorne-Kishino-Felsenstein model of substitution and single nucleotide insertion-deletion (indel events. In our earlier work, we introduced a novel and efficient algorithm, termed the "indel peeling algorithm", which includes indels as phylogenetically informative evolutionary events, and resembles Felsenstein's peeling algorithm for substitutions on a phylogenetic tree. For a fixed alignment, our extension analytically integrates out both substitution and indel events within a proper statistical model, without the need for data augmentation at internal tree nodes, allowing for efficient sampling of tree topologies and edge lengths. To additionally sample multiple alignments, we here introduce an efficient partial Metropolized independence sampler for alignments, and combine these two algorithms into a fully Bayesian co-estimation procedure for the alignment and phylogeny problem. Our approach results in estimates for the posterior distribution of evolutionary rate parameters, for the maximum a-posteriori (MAP phylogenetic tree, and for the posterior decoding alignment. Estimates for the evolutionary tree and multiple alignment are augmented with confidence estimates for each node height and alignment column
Implementing Bayesian Vector Autoregressions Implementing Bayesian Vector Autoregressions
Directory of Open Access Journals (Sweden)
Richard M. Todd
1988-03-01
Full Text Available Implementing Bayesian Vector Autoregressions This paper discusses how the Bayesian approach can be used to construct a type of multivariate forecasting model known as a Bayesian vector autoregression (BVAR. In doing so, we mainly explain Doan, Littermann, and Sims (1984 propositions on how to estimate a BVAR based on a certain family of prior probability distributions. indexed by a fairly small set of hyperparameters. There is also a discussion on how to specify a BVAR and set up a BVAR database. A 4-variable model is used to iliustrate the BVAR approach.
Construction and decoding of a class of algebraic geometry codes
DEFF Research Database (Denmark)
Justesen, Jørn; Larsen, Knud J.; Jensen, Helge Elbrønd;
1989-01-01
A class of codes derived from algebraic plane curves is constructed. The concepts and results from algebraic geometry that were used are explained in detail; no further knowledge of algebraic geometry is needed. Parameters, generator and parity-check matrices are given. The main result is a decod...... is a decoding algorithm which turns out to be a generalization of the Peterson algorithm for decoding BCH decoder codes......A class of codes derived from algebraic plane curves is constructed. The concepts and results from algebraic geometry that were used are explained in detail; no further knowledge of algebraic geometry is needed. Parameters, generator and parity-check matrices are given. The main result...
VHDL Implementation of Fast and Efficient Viterbi decoder
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Rajesh. C
2013-09-01
Full Text Available Viterbi decoders are used in wide variety of communication applications. In this paper, we focus on different types of VHDL implementations of Viterbi decoder. The two approaches of Implementation of Viterbi decoder are register-exchange approach and trace back approach. There are two methods in trace back approach i.e. shift update and selective update. The behaviour of a Viterbi decoder is described in VHDL. A gate level circuit was obtained from the behavioural description through logic synthesis. We compared the performance characteristics of all approaches in terms of speed, area consumption, power and specific hardware components used by that particular design. Our experimental results show that the performance characteristics of selective update method are better compared to register-exchange and shift update method in terms of area and power consumption. In contrast, the performance characteristics of register-exchange method are better compared to selective update and shift update method in terms of speed.
Decoding Reed-Solomon Codes beyond half the minimum distance
DEFF Research Database (Denmark)
Høholdt, Tom; Nielsen, Rasmus Refslund
1999-01-01
We describe an efficient implementation of M.Sudan"s algorithm for decoding Reed-Solomon codes beyond half the minimum distance. Furthermore we calculate an upper bound of the probabilty of getting more than one codeword as output...
On Complexity, Energy- and Implementation-Efficiency of Channel Decoders
Kienle, Frank; Meyr, Heinrich
2010-01-01
Future wireless communication systems require efficient and flexible baseband receivers. Meaningful efficiency metrics are key for design space exploration to quantify the algorithmic and the implementation complexity of a receiver. Most of the current established efficiency metrics are based on counting operations, thus neglecting important issues like data and storage complexity. In this paper we introduce suitable energy and area efficiency metrics which resolve the afore-mentioned disadvantages. These are decoded information bit per energy and throughput per area unit. Efficiency metrics are assessed by various implementations of turbo decoders, LDPC decoders and convolutional decoders. New exploration methodologies are presented, which permit an appropriate benchmarking of implementation efficiency, communications performance, and flexibility trade-offs. These exploration methodologies are based on efficiency trajectories rather than a single snapshot metric as done in state-of-the-art approaches.
Improved List Sphere Decoder for Multiple Antenna Systems
Institute of Scientific and Technical Information of China (English)
无
2008-01-01
An improved list sphere decoder (ILSD) is proposed based on the conventional list sphere decoder (LSD) and the reduced-complexity maximum likelihood sphere-decoding algorithm. Unlike the conventional LSD with fixed initial radius, the ILSD adopts an adaptive radius to accelerate the list construction. Characterized by low-complexity and radius-insensitivity, the proposed algorithm makes iterative joint detection and decoding more realizable in multiple-antenna systems. Simulation results show that computational savings of ILSD over LSD are more apparent with more transmit antennas or larger constellations, and with no performance degradation. Because the complexity of the ILSD algorithm almost keeps invariant with the increasing of initial radius, the BER performance can be improved by selecting a sufficiently large radius.
A Correlational Encoder Decoder Architecture for Pivot Based Sequence Generation
SAHA, AMRITA; Khapra, Mitesh M.; Chandar, Sarath; Rajendran, Janarthanan; Cho, Kyunghyun
2016-01-01
Interlingua based Machine Translation (MT) aims to encode multiple languages into a common linguistic representation and then decode sentences in multiple target languages from this representation. In this work we explore this idea in the context of neural encoder decoder architectures, albeit on a smaller scale and without MT as the end goal. Specifically, we consider the case of three languages or modalities X, Z and Y wherein we are interested in generating sequences in Y starting from inf...
Performance Analysis of a Decoding Algorithm for Algebraic Geometry Codes
DEFF Research Database (Denmark)
Jensen, Helge Elbrønd; Nielsen, Rasmus Refslund; Høholdt, Tom
1998-01-01
We analyse the known decoding algorithms for algebraic geometry codes in the case where the number of errors is greater than or equal to [(dFR-1)/2]+1, where dFR is the Feng-Rao distance......We analyse the known decoding algorithms for algebraic geometry codes in the case where the number of errors is greater than or equal to [(dFR-1)/2]+1, where dFR is the Feng-Rao distance...
Recent results in the decoding of Algebraic geometry codes
DEFF Research Database (Denmark)
Høholdt, Tom; Jensen, Helge Elbrønd; Nielsen, Rasmus Refslund
1998-01-01
We analyse the known decoding algorithms for algebraic geometry codes in the case where the number of errors is [(dFR-1)/2]+1, where dFR is the Feng-Rao distance......We analyse the known decoding algorithms for algebraic geometry codes in the case where the number of errors is [(dFR-1)/2]+1, where dFR is the Feng-Rao distance...
Molecular decoding using luminescence from an entangled porous framework
Takashima, Yohei; Martínez, Virginia Martínez; Furukawa, Shuhei; Kondo, Mio; Shimomura, Satoru; Uehara, Hiromitsu; Nakahama, Masashi; Sugimoto, Kunihisa; Kitagawa, Susumu
2011-01-01
Chemosensors detect a single target molecule from among several molecules, but cannot differentiate targets from one another. In this study, we report a molecular decoding strategy in which a single host domain accommodates a class of molecules and distinguishes between them with a corresponding readout. We synthesized the decoding host by embedding naphthalenediimide into the scaffold of an entangled porous framework that exhibited structural dynamics due to the dislocation of two chemically...
New syndrome decoder for (n, 1) convolutional codes
Reed, I. S.; Truong, T. K.
1983-01-01
The letter presents a new syndrome decoding algorithm for the (n, 1) convolutional codes (CC) that is different and simpler than the previous syndrome decoding algorithm of Schalkwijk and Vinck. The new technique uses the general solution of the polynomial linear Diophantine equation for the error polynomial vector E(D). A recursive, Viterbi-like, algorithm is developed to find the minimum weight error vector E(D). An example is given for the binary nonsystematic (2, 1) CC.
Emotional facial expressions decoding in siblings of children with autism
Dethier, Marie; Sojic, Barbara; Blairy, Sylvie
2010-01-01
The ability to identify other people’s emotions, including their emotional facial expression (EFE), is fundamental to many social processes. Individuals with autism spectrum disorders (ASD) show deficits in several empathy-related processes, including EFE decoding (e.g. Ashwin, Chapman, Colle, & Baron-Cohen, 2007). The object of this study was to investigate the capacity to decode accurately EFE in siblings of children with ASD. Indeed, autism is considered to be substantially influenced by g...
Rethinking the Role of Decodable Texts in Early Literacy Instruction
Frey, Rick Chan
2012-01-01
Decodable books based on previous classroom instruction are the most frequently used texts for 1st grade reading instruction in public schools, yet no empirical studies exist demonstrating their efficacy or their benefits for beginning readers. This study attempts to address this gap in the research literature by analyzing the reading behaviors of a group of 1st grade students reading the decodable texts included as part of the 1st grade reading curriculum in a large public, urban school dist...
Dynamic Bayesian diffusion estimation
Dedecius, K
2012-01-01
The rapidly increasing complexity of (mainly wireless) ad-hoc networks stresses the need of reliable distributed estimation of several variables of interest. The widely used centralized approach, in which the network nodes communicate their data with a single specialized point, suffers from high communication overheads and represents a potentially dangerous concept with a single point of failure needing special treatment. This paper's aim is to contribute to another quite recent method called diffusion estimation. By decentralizing the operating environment, the network nodes communicate just within a close neighbourhood. We adopt the Bayesian framework to modelling and estimation, which, unlike the traditional approaches, abstracts from a particular model case. This leads to a very scalable and universal method, applicable to a wide class of different models. A particularly interesting case - the Gaussian regressive model - is derived as an example.
Decoding Schemes for FBMC with Single-Delay STTC
Directory of Open Access Journals (Sweden)
Chrislin Lélé
2010-01-01
Full Text Available Orthogonally multiplexed Quadrature Amplitude Modulation (OQAM with Filter-Bank-based MultiCarrier modulation (FBMC is a multicarrier modulation scheme that can be considered an alternative to the conventional orthogonal frequency division multiplexing (OFDM with cyclic prefix (CP for transmission over multipath fading channels. However, as OQAM-based FBMC is based on real orthogonality, transmission over a complex-valued channel makes the decoding process more challenging compared to CP-OFDM case. Moreover, if we apply Multiple Input Multiple Output (MIMO techniques to OQAM-based FBMC, the decoding schemes are different from the ones used in CP-OFDM. In this paper, we consider the combination of OQAM-based FBMC with single-delay Space-Time Trellis Coding (STTC. We extend the decoding process presented earlier in the case of Nt=2 transmit antennas to greater values of Nt. Then, for Nt≥2, we make an analysis of the theoretical and simulation performance of ML and Viterbi decoding. Finally, to improve the performance of this method, we suggest an iterative decoding method. We show that the OQAM-based FBMC iterative decoding scheme can slightly outperform CP-OFDM.
Decoding Schemes for FBMC with Single-Delay STTC
Directory of Open Access Journals (Sweden)
Lélé Chrislin
2010-01-01
Full Text Available Abstract Orthogonally multiplexed Quadrature Amplitude Modulation (OQAM with Filter-Bank-based MultiCarrier modulation (FBMC is a multicarrier modulation scheme that can be considered an alternative to the conventional orthogonal frequency division multiplexing (OFDM with cyclic prefix (CP for transmission over multipath fading channels. However, as OQAM-based FBMC is based on real orthogonality, transmission over a complex-valued channel makes the decoding process more challenging compared to CP-OFDM case. Moreover, if we apply Multiple Input Multiple Output (MIMO techniques to OQAM-based FBMC, the decoding schemes are different from the ones used in CP-OFDM. In this paper, we consider the combination of OQAM-based FBMC with single-delay Space-Time Trellis Coding (STTC. We extend the decoding process presented earlier in the case of transmit antennas to greater values of . Then, for , we make an analysis of the theoretical and simulation performance of ML and Viterbi decoding. Finally, to improve the performance of this method, we suggest an iterative decoding method. We show that the OQAM-based FBMC iterative decoding scheme can slightly outperform CP-OFDM.
Evaluation framework for K-best sphere decoders
Shen, Chungan
2010-08-01
While Maximum-Likelihood (ML) is the optimum decoding scheme for most communication scenarios, practical implementation difficulties limit its use, especially for Multiple Input Multiple Output (MIMO) systems with a large number of transmit or receive antennas. Tree-searching type decoder structures such as Sphere decoder and K-best decoder present an interesting trade-off between complexity and performance. Many algorithmic developments and VLSI implementations have been reported in literature with widely varying performance to area and power metrics. In this semi-tutorial paper we present a holistic view of different Sphere decoding techniques and K-best decoding techniques, identifying the key algorithmic and implementation trade-offs. We establish a consistent benchmark framework to investigate and compare the delay cost, power cost, and power-delay-product cost incurred by each method. Finally, using the framework, we propose and analyze a novel architecture and compare that to other published approaches. Our goal is to explicitly elucidate the overall advantages and disadvantages of each proposed algorithms in one coherent framework. © 2010 World Scientific Publishing Company.
Partially blind instantly decodable network codes for lossy feedback environment
Sorour, Sameh
2014-09-01
In this paper, we study the multicast completion and decoding delay minimization problems for instantly decodable network coding (IDNC) in the case of lossy feedback. When feedback loss events occur, the sender falls into uncertainties about packet reception at the different receivers, which forces it to perform partially blind selections of packet combinations in subsequent transmissions. To determine efficient selection policies that reduce the completion and decoding delays of IDNC in such an environment, we first extend the perfect feedback formulation in our previous works to the lossy feedback environment, by incorporating the uncertainties resulting from unheard feedback events in these formulations. For the completion delay problem, we use this formulation to identify the maximum likelihood state of the network in events of unheard feedback and employ it to design a partially blind graph update extension to the multicast IDNC algorithm in our earlier work. For the decoding delay problem, we derive an expression for the expected decoding delay increment for any arbitrary transmission. This expression is then used to find the optimal policy that reduces the decoding delay in such lossy feedback environment. Results show that our proposed solutions both outperform previously proposed approaches and achieve tolerable degradation even at relatively high feedback loss rates.
Bayesian isochrone fitting and stellar ages
Valls-Gabaud, D
2016-01-01
Stellar evolution theory has been extraordinarily successful at explaining the different phases under which stars form, evolve and die. While the strongest constraints have traditionally come from binary stars, the advent of asteroseismology is bringing unique measures in well-characterised stars. For stellar populations in general, however, only photometric measures are usually available, and the comparison with the predictions of stellar evolution theory have mostly been qualitative. For instance, the geometrical shapes of isochrones have been used to infer ages of coeval populations, but without any proper statistical basis. In this chapter we provide a pedagogical review on a Bayesian formalism to make quantitative inferences on the properties of single, binary and small ensembles of stars, including unresolved populations. As an example, we show how stellar evolution theory can be used in a rigorous way as a prior information to measure the ages of stars between the ZAMS and the Helium flash, and their u...
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
A multi-dimensional concatenation scheme for block codes is introduced, in which information symbols are interleaved and re-encoded for more than once. It provides a convenient platform to design high performance codes with flexible interleaver size.Coset based MAP soft-in/soft-out decoding algorithms are presented for the F24 code. Simulation results show that the proposed coding scheme can achieve high coding gain with flexible interleaver length and very low decoding complexity.
Squires, Katie E.
2013-01-01
This study investigated the differential contribution of auditory-verbal and visuospatial working memory (WM) on decoding skills in second- and fifth-grade children identified with poor decoding. Thirty-two second-grade students and 22 fifth-grade students completed measures that assessed simple and complex auditory-verbal and visuospatial memory, phonological awareness, orthographic knowledge, listening comprehension and verbal and nonverbal intelligence. Bivariate correlations revealed th...
Current trends in Bayesian methodology with applications
Upadhyay, Satyanshu K; Dey, Dipak K; Loganathan, Appaia
2015-01-01
Collecting Bayesian material scattered throughout the literature, Current Trends in Bayesian Methodology with Applications examines the latest methodological and applied aspects of Bayesian statistics. The book covers biostatistics, econometrics, reliability and risk analysis, spatial statistics, image analysis, shape analysis, Bayesian computation, clustering, uncertainty assessment, high-energy astrophysics, neural networking, fuzzy information, objective Bayesian methodologies, empirical Bayes methods, small area estimation, and many more topics.Each chapter is self-contained and focuses on
Fast mental states decoding in mixed reality.
De Massari, Daniele; Pacheco, Daniel; Malekshahi, Rahim; Betella, Alberto; Verschure, Paul F M J; Birbaumer, Niels; Caria, Andrea
2014-01-01
The combination of Brain-Computer Interface (BCI) technology, allowing online monitoring and decoding of brain activity, with virtual and mixed reality (MR) systems may help to shape and guide implicit and explicit learning using ecological scenarios. Real-time information of ongoing brain states acquired through BCI might be exploited for controlling data presentation in virtual environments. Brain states discrimination during mixed reality experience is thus critical for adapting specific data features to contingent brain activity. In this study we recorded electroencephalographic (EEG) data while participants experienced MR scenarios implemented through the eXperience Induction Machine (XIM). The XIM is a novel framework modeling the integration of a sensing system that evaluates and measures physiological and psychological states with a number of actuators and effectors that coherently reacts to the user's actions. We then assessed continuous EEG-based discrimination of spatial navigation, reading and calculation performed in MR, using linear discriminant analysis (LDA) and support vector machine (SVM) classifiers. Dynamic single trial classification showed high accuracy of LDA and SVM classifiers in detecting multiple brain states as well as in differentiating between high and low mental workload, using a 5 s time-window shifting every 200 ms. Our results indicate overall better performance of LDA with respect to SVM and suggest applicability of our approach in a BCI-controlled MR scenario. Ultimately, successful prediction of brain states might be used to drive adaptation of data representation in order to boost information processing in MR. PMID:25505878
Fast mental states decoding in mixed reality.
Directory of Open Access Journals (Sweden)
Daniele eDe Massari
2014-11-01
Full Text Available The combination of Brain-Computer Interface technology, allowing online monitoring and decoding of brain activity, with virtual and mixed reality systems may help to shape and guide implicit and explicit learning using ecological scenarios. Real-time information of ongoing brain states acquired through BCI might be exploited for controlling data presentation in virtual environments. In this context, assessing to what extent brain states can be discriminated during mixed reality experience is critical for adapting specific data features to contingent brain activity. In this study we recorded EEG data while participants experienced a mixed reality scenario implemented through the eXperience Induction Machine (XIM. The XIM is a novel framework modeling the integration of a sensing system that evaluates and measures physiological and psychological states with a number of actuators and effectors that coherently reacts to the user's actions. We then assessed continuous EEG-based discrimination of spatial navigation, reading and calculation performed in mixed reality, using LDA and SVM classifiers. Dynamic single trial classification showed high accuracy of LDA and SVM classifiers in detecting multiple brain states as well as in differentiating between high and low mental workload, using a 5 s time-window shifting every 200 ms. Our results indicate overall better performance of LDA with respect to SVM and suggest applicability of our approach in a BCI-controlled mixed reality scenario. Ultimately, successful prediction of brain states might be used to drive adaptation of data representation in order to boost information processing in mixed reality.
Rate Aware Instantly Decodable Network Codes
Douik, Ahmed
2016-02-26
This paper addresses the problem of reducing the delivery time of data messages to cellular users using instantly decodable network coding (IDNC) with physical-layer rate awareness. While most of the existing literature on IDNC does not consider any physical layer complications, this paper proposes a cross-layer scheme that incorporates the different channel rates of the various users in the decision process of both the transmitted message combinations and the rates with which they are transmitted. The completion time minimization problem in such scenario is first shown to be intractable. The problem is, thus, approximated by reducing, at each transmission, the increase of an anticipated version of the completion time. The paper solves the problem by formulating it as a maximum weight clique problem over a newly designed rate aware IDNC (RA-IDNC) graph. Further, the paper provides a multi-layer solution to improve the completion time approximation. Simulation results suggest that the cross-layer design largely outperforms the uncoded transmissions strategies and the classical IDNC scheme. © 2015 IEEE.
Decoding reality the universe as quantum information
Vedral, Vlatko
2010-01-01
In Decoding Reality, Vlatko Vedral offers a mind-stretching look at the deepest questions about the universe--where everything comes from, why things are as they are, what everything is. The most fundamental definition of reality is not matter or energy, he writes, but information--and it is the processing of information that lies at the root of all physical, biological, economic, and social phenomena. This view allows Vedral to address a host of seemingly unrelated questions: Why does DNA bind like it does? What is the ideal diet for longevity? How do you make your first million dollars? We can unify all through the understanding that everything consists of bits of information, he writes, though that raises the question of where these bits come from. To find the answer, he takes us on a guided tour through the bizarre realm of quantum physics. At this sub-sub-subatomic level, we find such things as the interaction of separated quantum particles--what Einstein called "spooky action at a distance." In fact, V...
DECODING OF STRUCTURALLY AND LOGICAL CODES
Directory of Open Access Journals (Sweden)
Yu. D. Ivanov
2016-01-01
Full Text Available The article deals with the description of the main points of the structural and logical coding and the features of SLC codes. There are shown the basic points of the generalized algorithm of decoding SLC, which is based on the method of perfect matrix arrangement (PMA of the n-dimensional cube vertices for adequate representation and transformation of boolean functions, which is based on the method of generating sequences of variables for building the maximum coverage of the cube vertices. The structural and logical codes (SLC use natural logic redundancy of the infimum disjunctive normal forms (IDNF of boolean functions, which make the basis for building the SLC codes and correcting the errors, that occur during data transfer in real discrete channels, on the channels with independent errors. The main task is to define the basic relations between the implemented SLC codes of the logical redundancy and boundary values of multiplicity of independent errors which are corrected. The principal difference between the SLC codes and the well-known correcting codes is that the redundancy, that is needed to correct the errors in converting the discrete information, is not introduced into an additional code sequence but is defined in a natural way, during the construction of codewords of SLC.
Why Hawking Radiation Cannot Be Decoded
Ong, Yen Chin; Chen, Pisin
2014-01-01
One of the great difficulties in the theory of black hole evaporation is that the most decisive phenomena tend to occur when the black hole is extremely hot: that is, when the physics is most poorly understood. Fortunately, a crucial step in the Harlow-Hayden approach to the firewall paradox, concerning the time available for decoding of Hawking radiation emanating from charged AdS black holes, can be made to work without relying on the unknown physics of black holes with extremely high temperatures; in fact, it relies on the properties of cold black holes. Here we clarify this surprising point. The approach is based on ideas borrowed from applications of the AdS/CFT correspondence to the quark-gluon plasma. Firewalls aside, our work presents a detailed analysis of the thermodynamics and evolution of evaporating charged AdS black holes with flat event horizons. We show that, in one way or another, these black holes are always eventually destroyed in a time which, while long by normal standards, is short relat...
Older adults have difficulty in decoding sarcasm.
Phillips, Louise H; Allen, Roy; Bull, Rebecca; Hering, Alexandra; Kliegel, Matthias; Channon, Shelley
2015-12-01
Younger and older adults differ in performance on a range of social-cognitive skills, with older adults having difficulties in decoding nonverbal cues to emotion and intentions. Such skills are likely to be important when deciding whether someone is being sarcastic. In the current study we investigated in a life span sample whether there are age-related differences in the interpretation of sarcastic statements. Using both video and verbal materials, 116 participants aged between 18 and 86 completed judgments about whether statements should be interpreted literally or sarcastically. For the verbal stories task, older adults were poorer at understanding sarcastic intent compared with younger and middle-aged participants, but there was no age difference in interpreting control stories. For the video task, older adults showed poorer understanding of sarcastic exchanges compared with younger and middle-aged counterparts, but there was no age difference in understanding the meaning of sincere interactions. For the videos task, the age differences were mediated by the ability to perceive facial expressions of emotion. Age effects could not be explained in terms of variance in working memory. These results indicate that increased age is associated with specific difficulties in using nonverbal and contextual cues to understand sarcastic intent. (PsycINFO Database Record PMID:26501728
Irregular-Time Bayesian Networks
Ramati, Michael
2012-01-01
In many fields observations are performed irregularly along time, due to either measurement limitations or lack of a constant immanent rate. While discrete-time Markov models (as Dynamic Bayesian Networks) introduce either inefficient computation or an information loss to reasoning about such processes, continuous-time Markov models assume either a discrete state space (as Continuous-Time Bayesian Networks), or a flat continuous state space (as stochastic dif- ferential equations). To address these problems, we present a new modeling class called Irregular-Time Bayesian Networks (ITBNs), generalizing Dynamic Bayesian Networks, allowing substantially more compact representations, and increasing the expressivity of the temporal dynamics. In addition, a globally optimal solution is guaranteed when learning temporal systems, provided that they are fully observed at the same irregularly spaced time-points, and a semiparametric subclass of ITBNs is introduced to allow further adaptation to the irregular nature of t...
Order Statistics Based List Decoding Techniques for Linear Binary Block Codes
Alnawayseh, Saif E A
2011-01-01
The order statistics based list decoding techniques for linear binary block codes of small to medium block length are investigated. The construction of the list of the test error patterns is considered. The original order statistics decoding is generalized by assuming segmentation of the most reliable independent positions of the received bits. The segmentation is shown to overcome several drawbacks of the original order statistics decoding. The complexity of the order statistics based decoding is further reduced by assuming a partial ordering of the received bits in order to avoid the complex Gauss elimination. The probability of the test error patterns in the decoding list is derived. The bit error rate performance and the decoding complexity trade-off of the proposed decoding algorithms is studied by computer simulations. Numerical examples show that, in some cases, the proposed decoding schemes are superior to the original order statistics decoding in terms of both the bit error rate performance as well a...
Neuronanatomy, neurology and Bayesian networks
Bielza Lozoya, Maria Concepcion
2014-01-01
Bayesian networks are data mining models with clear semantics and a sound theoretical foundation. In this keynote talk we will pinpoint a number of neuroscience problems that can be addressed using Bayesian networks. In neuroanatomy, we will show computer simulation models of dendritic trees and classification of neuron types, both based on morphological features. In neurology, we will present the search for genetic biomarkers in Alzheimer's disease and the prediction of health-related qualit...
Efficient universal computing architectures for decoding neural activity.
Directory of Open Access Journals (Sweden)
Benjamin I Rapoport
Full Text Available The ability to decode neural activity into meaningful control signals for prosthetic devices is critical to the development of clinically useful brain- machine interfaces (BMIs. Such systems require input from tens to hundreds of brain-implanted recording electrodes in order to deliver robust and accurate performance; in serving that primary function they should also minimize power dissipation in order to avoid damaging neural tissue; and they should transmit data wirelessly in order to minimize the risk of infection associated with chronic, transcutaneous implants. Electronic architectures for brain- machine interfaces must therefore minimize size and power consumption, while maximizing the ability to compress data to be transmitted over limited-bandwidth wireless channels. Here we present a system of extremely low computational complexity, designed for real-time decoding of neural signals, and suited for highly scalable implantable systems. Our programmable architecture is an explicit implementation of a universal computing machine emulating the dynamics of a network of integrate-and-fire neurons; it requires no arithmetic operations except for counting, and decodes neural signals using only computationally inexpensive logic operations. The simplicity of this architecture does not compromise its ability to compress raw neural data by factors greater than [Formula: see text]. We describe a set of decoding algorithms based on this computational architecture, one designed to operate within an implanted system, minimizing its power consumption and data transmission bandwidth; and a complementary set of algorithms for learning, programming the decoder, and postprocessing the decoded output, designed to operate in an external, nonimplanted unit. The implementation of the implantable portion is estimated to require fewer than 5000 operations per second. A proof-of-concept, 32-channel field-programmable gate array (FPGA implementation of this portion
A Bayesian approach to combining animal abundance and demographic data
Directory of Open Access Journals (Sweden)
Brooks, S. P.
2004-06-01
Full Text Available In studies of wild animals, one frequently encounters both count and mark-recapture-recovery data. Here, we consider an integrated Bayesian analysis of ring¿recovery and count data using a state-space model. We then impose a Leslie-matrix-based model on the true population counts describing the natural birth-death and age transition processes. We focus upon the analysis of both count and recovery data collected on British lapwings (Vanellus vanellus combined with records of the number of frost days each winter. We demonstrate how the combined analysis of these data provides a more robust inferential framework and discuss how the Bayesian approach using MCMC allows us to remove the potentially restrictive normality assumptions commonly assumed for analyses of this sort. It is shown how WinBUGS may be used to perform the Bayesian analysis. WinBUGS code is provided and its performance is critically discussed.
Some Bayesian statistical techniques useful in estimating frequency and density
Johnson, D.H.
1977-01-01
This paper presents some elementary applications of Bayesian statistics to problems faced by wildlife biologists. Bayesian confidence limits for frequency of occurrence are shown to be generally superior to classical confidence limits. Population density can be estimated from frequency data if the species is sparsely distributed relative to the size of the sample plot. For other situations, limits are developed based on the normal distribution and prior knowledge that the density is non-negative, which insures that the lower confidence limit is non-negative. Conditions are described under which Bayesian confidence limits are superior to those calculated with classical methods; examples are also given on how prior knowledge of the density can be used to sharpen inferences drawn from a new sample.
Comparing Nonparametric Bayesian Tree Priors for Clonal Reconstruction of Tumors
Deshwar, Amit G; Vembu, Shankar; Morris, Quaid
2014-01-01
Statistical machine learning methods, especially nonparametric Bayesian methods, have become increasingly popular to infer clonal population structure of tumors. Here we describe the treeCRP, an extension of the Chinese restaurant process (CRP), a popular construction used in nonparametric mixture models, to infer the phylogeny and genotype of major subclonal lineages represented in the population of cancer cells. We also propose new split-merge updates tailored to the subclonal reconstructio...
Dale Poirier
2008-01-01
This paper provides Bayesian rationalizations for White’s heteroskedastic consistent (HC) covariance estimator and various modifications of it. An informed Bayesian bootstrap provides the statistical framework.
PERFORMANCE OF A NEW DECODING METHOD USED IN OPEN-LOOP ALL-OPTICAL CHAOTIC COMMUNICATION SYSTEM
Institute of Scientific and Technical Information of China (English)
Liu Huijie; Feng Jiuchao
2011-01-01
A new decoding method with decoder is used in open-loop all-optical chaotic communication system under strong injection condition.The performance of the new decoding method is numerically investigated by comparing it with the common decoding method without decoder.For new decoding method,two cases are analyzed,including whether or not the output of the decoder is adjusted by its input to receiver.The results indicate the decoding quality can be improved by adjusting for the new decoding method.Meanwhile,the injection strength of decoder can be restricted in a certain range.The adjusted new decoding method with decoder can achieve better decoding quality than decoding method without decoder when the bit rate of message is under 5 Gb/s.However,a stronger injection for receiver is needed.Moreover,the new decoding method can broaden the range of injection strength acceptable for good decoding quality.Different message encryption techniques are tested,and the result is similar to that of the common decoding method,indicative of the fact that the message encoded by using Chaotic Modulation (CM) can be best recovered by the new decoding method owning to the essence of this encryption technique.
Dynamic Batch Bayesian Optimization
Azimi, Javad; Fern, Xiaoli
2011-01-01
Bayesian optimization (BO) algorithms try to optimize an unknown function that is expensive to evaluate using minimum number of evaluations/experiments. Most of the proposed algorithms in BO are sequential, where only one experiment is selected at each iteration. This method can be time inefficient when each experiment takes a long time and more than one experiment can be ran concurrently. On the other hand, requesting a fix-sized batch of experiments at each iteration causes performance inefficiency in BO compared to the sequential policies. In this paper, we present an algorithm that asks a batch of experiments at each time step t where the batch size p_t is dynamically determined in each step. Our algorithm is based on the observation that the sequence of experiments selected by the sequential policy can sometimes be almost independent from each other. Our algorithm identifies such scenarios and request those experiments at the same time without degrading the performance. We evaluate our proposed method us...
Nonparametric Bayesian Classification
Coram, M A
2002-01-01
A Bayesian approach to the classification problem is proposed in which random partitions play a central role. It is argued that the partitioning approach has the capacity to take advantage of a variety of large-scale spatial structures, if they are present in the unknown regression function $f_0$. An idealized one-dimensional problem is considered in detail. The proposed nonparametric prior uses random split points to partition the unit interval into a random number of pieces. This prior is found to provide a consistent estimate of the regression function in the $\\L^p$ topology, for any $1 \\leq p < \\infty$, and for arbitrary measurable $f_0:[0,1] \\rightarrow [0,1]$. A Markov chain Monte Carlo (MCMC) implementation is outlined and analyzed. Simulation experiments are conducted to show that the proposed estimate compares favorably with a variety of conventional estimators. A striking resemblance between the posterior mean estimate and the bagged CART estimate is noted and discussed. For higher dimensions, a ...
Measuring Integrated Information from the Decoding Perspective.
Oizumi, Masafumi; Amari, Shun-ichi; Yanagawa, Toru; Fujii, Naotaka; Tsuchiya, Naotsugu
2016-01-01
Accumulating evidence indicates that the capacity to integrate information in the brain is a prerequisite for consciousness. Integrated Information Theory (IIT) of consciousness provides a mathematical approach to quantifying the information integrated in a system, called integrated information, Φ. Integrated information is defined theoretically as the amount of information a system generates as a whole, above and beyond the amount of information its parts independently generate. IIT predicts that the amount of integrated information in the brain should reflect levels of consciousness. Empirical evaluation of this theory requires computing integrated information from neural data acquired from experiments, although difficulties with using the original measure Φ precludes such computations. Although some practical measures have been previously proposed, we found that these measures fail to satisfy the theoretical requirements as a measure of integrated information. Measures of integrated information should satisfy the lower and upper bounds as follows: The lower bound of integrated information should be 0 and is equal to 0 when the system does not generate information (no information) or when the system comprises independent parts (no integration). The upper bound of integrated information is the amount of information generated by the whole system. Here we derive the novel practical measure Φ* by introducing a concept of mismatched decoding developed from information theory. We show that Φ* is properly bounded from below and above, as required, as a measure of integrated information. We derive the analytical expression of Φ* under the Gaussian assumption, which makes it readily applicable to experimental data. Our novel measure Φ* can generally be used as a measure of integrated information in research on consciousness, and also as a tool for network analysis on diverse areas of biology.
Reconfigurable and Parallelized Network Coding Decoder for VANETs
Directory of Open Access Journals (Sweden)
Sunwoo Kim
2012-01-01
Full Text Available Network coding is a promising technique for data communications in wired and wireless networks. However, it places an additional computing overhead on the receiving node in exchange for the improved bandwidth. This paper proposes an FPGA-based reconfigurable and parallelized network coding decoder for embedded systems especially for vehicular ad hoc networks. In our design, rapid decoding process can be achieved by exploiting parallelism in the coefficient vector operations. The proposed decoder is implemented by using a modern Xilinx Virtex-5 device and its performance is evaluated considering the performance of the software decoding on various embedded processors. The performance on four different sizes of the coefficient matrix is measured and the decoding throughput of 18.3 Mbps for the size 16 × 16 and 6.5 Mbps for 128 × 128 has been achieved at the operating frequency of 64.5 MHz. Compared to the recent TEGRA 250 processor, the result obtained with128 × 128 coefficient matrix reaches up to 5.06 in terms of speedup.
Joint source-channel decoding for MPEG-2 video transmission
Yin, Liuguo; Chen, Weigang; Lu, Jianhua; Chen, Chang Wen
2004-10-01
Joint source-channel coding schemes have been proven to be effective ways for reliable multimedia communications. In this paper, a joint source-channel decoding (JSCD) scheme that combines the hidden Markov source (HMS) estimation and low-density parity-check (LDPC) coding is proposed for the standard MPEG-2 video transmission. The LDPC code of the proposed scheme has a near-Shannon-limit error-correcting capability, while the HMS estimator may accurately extract the residual redundancy within the MPEG-2 video stream without any prior information. Furthermore, with a joint iterative decoding algorithm, the estimated source redundancy may be well exploited by the LDPC decoder, and the channel decoding feedback may refine the subsequence HMS estimation, thereby effectively improving the system performance. On the other hand, we also show that the proposed JSCD scheme has approximately the same computation complexity as that of the standard decoding scheme. Moreover, it is worth noting that the proposed scheme is based on separation encoding schemes, which is very convenient to be applied to existing multimedia transmission systems.
Performance evaluation of H.264 decoder on different processors
Directory of Open Access Journals (Sweden)
H.S.Prasantha
2010-08-01
Full Text Available H.264/AVC (Advanced Video Coding is the newest video coding standard of the moving video coding experts group. The decoder is standardized by imposing restrictions on the bit stream and syntax, and defining the process of decoding syntax elements such that every decoder conforming to the standard will produce similar output when encoded bit stream is provided as input. It uses state of art coding tools and provides enhanced coding efficiency for a wide range of applications, including video telephony, real-time video conferencing, direct-broadcast TV (television, blue-ray disc, DVB (Digital video broadcast broadcast, streaming video and others. The paper proposes to port the H.264/AVC decoder on the various processors such as TI DSP (Digital signal processor, ARM (Advanced risk machines and P4 (Pentium processors. The paper also proposesto analyze and compare Video Quality Metrics for different encoded video sequences. The paper proposes to investigate the decoder performance on different processors with and without deblocking filter and compare the performance based on different video quality measures.
Bayesian individualization via sampling-based methods.
Wakefield, J
1996-02-01
We consider the situation where we wish to adjust the dosage regimen of a patient based on (in general) sparse concentration measurements taken on-line. A Bayesian decision theory approach is taken which requires the specification of an appropriate prior distribution and loss function. A simple method for obtaining samples from the posterior distribution of the pharmacokinetic parameters of the patient is described. In general, these samples are used to obtain a Monte Carlo estimate of the expected loss which is then minimized with respect to the dosage regimen. Some special cases which yield analytic solutions are described. When the prior distribution is based on a population analysis then a method of accounting for the uncertainty in the population parameters is described. Two simulation studies showing how the methods work in practice are presented. PMID:8827585
Bayesian seismic AVO inversion
Energy Technology Data Exchange (ETDEWEB)
Buland, Arild
2002-07-01
A new linearized AVO inversion technique is developed in a Bayesian framework. The objective is to obtain posterior distributions for P-wave velocity, S-wave velocity and density. Distributions for other elastic parameters can also be assessed, for example acoustic impedance, shear impedance and P-wave to S-wave velocity ratio. The inversion algorithm is based on the convolutional model and a linearized weak contrast approximation of the Zoeppritz equation. The solution is represented by a Gaussian posterior distribution with explicit expressions for the posterior expectation and covariance, hence exact prediction intervals for the inverted parameters can be computed under the specified model. The explicit analytical form of the posterior distribution provides a computationally fast inversion method. Tests on synthetic data show that all inverted parameters were almost perfectly retrieved when the noise approached zero. With realistic noise levels, acoustic impedance was the best determined parameter, while the inversion provided practically no information about the density. The inversion algorithm has also been tested on a real 3-D dataset from the Sleipner Field. The results show good agreement with well logs but the uncertainty is high. The stochastic model includes uncertainties of both the elastic parameters, the wavelet and the seismic and well log data. The posterior distribution is explored by Markov chain Monte Carlo simulation using the Gibbs sampler algorithm. The inversion algorithm has been tested on a seismic line from the Heidrun Field with two wells located on the line. The uncertainty of the estimated wavelet is low. In the Heidrun examples the effect of including uncertainty of the wavelet and the noise level was marginal with respect to the AVO inversion results. We have developed a 3-D linearized AVO inversion method with spatially coupled model parameters where the objective is to obtain posterior distributions for P-wave velocity, S
HIGH THROUGHPUT OF MAP PROCESSOR USING PIPELINE WINDOW DECODING
Directory of Open Access Journals (Sweden)
P. Nithya
2012-11-01
Full Text Available Turbo codes are one of the most efficient error correcting code which approaches the Shannon limit.The high throughput in turbo decoder can be achieved by parallelizing several soft Input Soft Output(SISOunits together.In this way,multiple SISO decoders work on the same data frame at the same values and delievers soft outputs can be split into three terms like the soft channel and a priori input and the extrinsic value.The extrinsic value is used for the next iteration.The high throughput of Max-Log-MAP processor tha supports both single Binary(SBand Double-binary(DB convolutional turbo codes.Decoding of these codes however an iterative processing is requires high computation rate and latency.Thus in order to achieve high throughput and to reduce latency by using serial processing techniques.The pipeline window(PWdecoding is introduced to support arbitrary frame sizes with high throughput.
Completion Delay Minimization for Instantly Decodable Network Codes
Sorour, Sameh
2012-01-01
In this paper, we consider the problem of minimizing the completion delay for instantly decodable network coding (IDNC), in wireless multicast and broadcast scenarios. We are interested in this class of network coding due to its numerous benefits, such as low decoding delay, low coding and decoding complexities and simple receiver requirements. We first extend the IDNC graph, which represents all feasible IDNC coding opportunities, to efficiently operate in both multicast and broadcast scenarios. We then formulate the minimum completion delay problem for IDNC as a stochastic shortest path (SSP) problem. Although finding the optimal policy using SSP is intractable, we use this formulation to draw the theoretical guidelines for the policies that can efficiently reduce the completion delay in IDNC. Based on these guidelines, we design a maximum weight clique selection algorithm, which can efficiently reduce the IDNC completion delay in polynomial time. We also design a quadratic time heuristic clique selection a...
Fault Secure Encoder and Decoder with Clock Gating
Directory of Open Access Journals (Sweden)
N.Kapileswar
2012-04-01
Full Text Available This paper presents circuit design for a low power fault secure encoder and decoder system. Memory cells in logic circuits have been protected from soft errors for more than a decade due to increase in soft error rates. In this paper the circuitry around the memory block have been susceptible to soft errors and must be protected from faults. The proposed design uses error correcting codes and ring counter addressing scheme. In the ring counter several new clock gating techniques are proposed to reduce power consumption. A fault secure Encoder and Decoder error free low power logic circuits can be achieved by the proposed design. Simulation results show great improvement in power consumption. Fault secure Encoder and Decoder with clock gated by CG-element consumes approximately half the power of that consumed by the fault free circuit which doesn’t employ clock gating technique
Ternary Tree and Memory-Efficient Huffman Decoding Algorithm
Directory of Open Access Journals (Sweden)
Pushpa R. Suri
2011-01-01
Full Text Available In this study, the focus was on the use of ternary tree over binary tree. Here, a new one pass Algorithm for Decoding adaptive Huffman ternary tree codes was implemented. To reduce the memory size and fasten the process of searching for a symbol in a Huffman tree, we exploited the property of the encoded symbols and proposed a memory efficient data structure to represent the codeword length of Huffman ternary tree. In this algorithm we tried to find out the staring and ending address of the code to know the length of the code. And then in second algorithm we tried to decode the ternary tree code using binary search method. In this algorithm we tried to find out the staring and ending address of the code to know the length of the code. And then in second algorithm we tried to decode the ternary tree code using binary search method.
On the Optimality of Successive Decoding in Compress-and-Forward Relay Schemes
Wu, Xiugang
2010-01-01
In the classical compress-and-forward relay scheme developed by (Cover and El Gamal, 1979), the decoding process operates in a successive way: the destination first decodes the compressed observation of the relay, and then decodes the original message of the source. Recently, two modified compress-and-forward relay schemes were proposed, and in both of them, the destination jointly decodes the compressed observation of the relay and the original message, instead of successively. Such a modification on the decoding process was motivated by realizing that it is generally easier to decode the compressed observation jointly with the original message, and more importantly, the original message can be decoded even without completely decoding the compressed observation. However, the question remains whether this freedom of choosing a higher compression rate at the relay improves the achievable rate of the original message. It has been shown in (El Gamal and Kim, 2010) that the answer is negative in the single relay ...
A General Rate K/N Convolutional Decoder Based on Neural Networks with Stopping Criterion
Directory of Open Access Journals (Sweden)
Johnny W. H. Kao
2009-01-01
Full Text Available A novel algorithm for decoding a general rate K/N convolutional code based on recurrent neural network (RNN is described and analysed. The algorithm is introduced by outlining the mathematical models of the encoder and decoder. A number of strategies for optimising the iterative decoding process are proposed, and a simulator was also designed in order to compare the Bit Error Rate (BER performance of the RNN decoder with the conventional decoder that is based on Viterbi Algorithm (VA. The simulation results show that this novel algorithm can achieve the same bit error rate and has a lower decoding complexity. Most importantly this algorithm allows parallel signal processing, which increases the decoding speed and accommodates higher data rate transmission. These characteristics are inherited from a neural network structure of the decoder and the iterative nature of the algorithm, that outperform the conventional VA algorithm.
Linear Universal Decoding for Compound Channels: a Local to Global Geometric Approach
Abbe, Emmanuel
2008-01-01
Over discrete memoryless channels (DMC), linear decoders (maximizing additive metrics) afford several nice properties. In particular, if suitable encoders are employed, the use of decoding algorithm with manageable complexities is permitted. Maximum likelihood is an example of linear decoder. For a compound DMC, decoders that perform well without the channel's knowledge are required in order to achieve capacity. Several such decoders have been studied in the literature. However, there is no such known decoder which is linear. Hence, the problem of finding linear decoders achieving capacity for compound DMC is addressed, and it is shown that under minor concessions, such decoders exist and can be constructed. This paper also develops a "local geometric analysis", which allows in particular, to solve the above problem. By considering very noisy channels, the original problem is reduced, in the limit, to an inner product space problem, for which insightful solutions can be found. The local setting can then provi...
A Discrete Time Markov Chain Model for High Throughput Bidirectional Fano Decoders
Xu, Ran; Morris, Kevin; Kocak, Taskin
2010-01-01
The bidirectional Fano algorithm (BFA) can achieve at least two times decoding throughput compared to the conventional unidirectional Fano algorithm (UFA). In this paper, bidirectional Fano decoding is examined from the queuing theory perspective. A Discrete Time Markov Chain (DTMC) is employed to model the BFA decoder with a finite input buffer. The relationship between the input data rate, the input buffer size and the clock speed of the BFA decoder is established. The DTMC based modelling can be used in designing a high throughput parallel BFA decoding system. It is shown that there is a tradeoff between the number of BFA decoders and the input buffer size, and an optimal input buffer size can be chosen to minimize the hardware complexity for a target decoding throughput in designing a high throughput parallel BFA decoding system.
Schürch, Roger; Couvillon, Margaret J; Burns, Dominic D R; Tasman, Kiah; Waxman, David; Ratnieks, Francis L W
2013-12-01
Honey bees communicate to nestmates locations of resources, including food, water, tree resin and nest sites, by making waggle dances. Dances are composed of repeated waggle runs, which encode the distance and direction vector from the hive or swarm to the resource. Distance is encoded in the duration of the waggle run, and direction is encoded in the angle of the dancer's body relative to vertical. Glass-walled observation hives enable researchers to observe or video, and decode waggle runs. However, variation in these signals makes it impossible to determine exact locations advertised. We present a Bayesian duration to distance calibration curve using Markov Chain Monte Carlo simulations that allows us to quantify how accurately distance to a food resource can be predicted from waggle run durations within a single dance. An angular calibration shows that angular precision does not change over distance, resulting in spatial scatter proportional to distance. We demonstrate how to combine distance and direction to produce a spatial probability distribution of the resource location advertised by the dance. Finally, we show how to map honey bee foraging and discuss how our approach can be integrated with Geographic Information Systems to better understand honey bee foraging ecology. PMID:24132490
A Bayesian Approach to Identifying New Risk Factors for Dementia
Wen, Yen-Hsia; Wu, Shihn-Sheng; Lin, Chun-Hung Richard; Tsai, Jui-Hsiu; Yang, Pinchen; Chang, Yang-Pei; Tseng, Kuan-Hua
2016-01-01
Abstract Dementia is one of the most disabling and burdensome health conditions worldwide. In this study, we identified new potential risk factors for dementia from nationwide longitudinal population-based data by using Bayesian statistics. We first tested the consistency of the results obtained using Bayesian statistics with those obtained using classical frequentist probability for 4 recognized risk factors for dementia, namely severe head injury, depression, diabetes mellitus, and vascular diseases. Then, we used Bayesian statistics to verify 2 new potential risk factors for dementia, namely hearing loss and senile cataract, determined from the Taiwan's National Health Insurance Research Database. We included a total of 6546 (6.0%) patients diagnosed with dementia. We observed older age, female sex, and lower income as independent risk factors for dementia. Moreover, we verified the 4 recognized risk factors for dementia in the older Taiwanese population; their odds ratios (ORs) ranged from 3.469 to 1.207. Furthermore, we observed that hearing loss (OR = 1.577) and senile cataract (OR = 1.549) were associated with an increased risk of dementia. We found that the results obtained using Bayesian statistics for assessing risk factors for dementia, such as head injury, depression, DM, and vascular diseases, were consistent with those obtained using classical frequentist probability. Moreover, hearing loss and senile cataract were found to be potential risk factors for dementia in the older Taiwanese population. Bayesian statistics could help clinicians explore other potential risk factors for dementia and for developing appropriate treatment strategies for these patients. PMID:27227925
New Iterated Decoding Algorithm Based on Differential Frequency Hopping System
Institute of Scientific and Technical Information of China (English)
LIANG Fu-lin; LUO Wei-xiong
2005-01-01
A new iterated decoding algorithm is proposed for differential frequency hopping (DFH) encoder concatenated with multi-frequency shift-key (MFSK) modulator. According to the character of the frequency hopping (FH) pattern trellis produced by DFH function, maximum a posteriori (MAP) probability theory is applied to realize the iterate decoding of it. Further, the initial conditions for the new iterate algorithm based on MAP algorithm are modified for better performance. Finally, the simulation result compared with that from traditional algorithms shows good anti-interference performance.
PERFORMANCE OF THREE STAGE TURBO-EQUALIZATION-DECODING
Institute of Scientific and Technical Information of China (English)
Kazi Takpaya
2003-01-01
An increasing demand for high data rate transmission and protection over bandlimited channels with severe inter-symbol interference has resulted in a flurry of activity to improve channel equalization, In conjunction with equalization, channel coding-decoding can be employed to improve system performance. In this letter, the performance of the three stage turbo-equalization-decoding employing log maximum a posteriori probability is experimentally evaluated by a fading simulator. The BER is evaluated using various information sequence and interleaver sizes taking into account that the communication medium is a noisy inter symbol interference channel.
Codes on the Klein quartic, ideals, and decoding
DEFF Research Database (Denmark)
Hansen, Johan P.
1987-01-01
distance3. The codes are constructed from algebraic geometry using the dictionary between coding theory and algebraic curves over finite fields established by Goppa. The curve used in the present work is the Klein quartic. This curve has the maximal number of rational points over GF(2^{3})allowed by Serre...... descriptions as left ideals in the group-algebra GF(2^{3})[G]. This description allows for easy decoding. For instance, in the case of the single error correcting code of length21and dimension16with minimal distance3. decoding is obtained by multiplication with an idempotent in the group algebra....
Collision Codes: Decoding Superimposed BPSK Modulated Wireless Transmissions
Foh, Chuan Heng; Qureshi, Jalaluddin; 10.1109/CCNC.2010.5421745
2010-01-01
The introduction of physical layer network coding gives rise to the concept of turning a collision of transmissions on a wireless channel useful. In the idea of physical layer network coding, two synchronized simultaneous packet transmissions are carefully encoded such that the superimposed transmission can be decoded to produce a packet which is identical to the bitwise binary sum of the two transmitted packets. This paper explores the decoding of superimposed transmission resulted by multiple synchronized simultaneous transmissions. We devise a coding scheme that achieves the identification of individual transmission from the synchronized superimposed transmission. A mathematical proof for the existence of such a coding scheme is given.
Conventional Tanner Graph for Recursive onvolutional Codes and Associated Decoding
Institute of Scientific and Technical Information of China (English)
SUN Hong
2001-01-01
A different representation of recur-sive systematic convolutional (RSC) codes is pro-posed. This representation can be realized by a con-ventional Tanner graph. The graph becomes a treeby introducing hidden edge. It is shown that thesum-product algorithm applied to this graph modelis equivalent to the BCJR algorithm for turbo de-coding with lower computational complexity. Themessage-passing chain of the BCJR algorithm is pre-sented more exactly in the graph. In addition, theproposed representation of RSC codes provides an ef-ficient method to set up the trellis and the conven-tional Tanner graph for RSC codes provides directlythe architecture for decoding.
Decoding Brain States Based on Magnetoencephalography From Prespecified Cortical Regions.
Zhang, Jinyin; Li, Xin; Foldes, Stephen T; Wang, Wei; Collinger, Jennifer L; Weber, Douglas J; Bagić, Anto
2016-01-01
Brain state decoding based on whole-head MEG has been extensively studied over the past decade. Recent MEG applications pose an emerging need of decoding brain states based on MEG signals originating from prespecified cortical regions. Toward this goal, we propose a novel region-of-interest-constrained discriminant analysis algorithm (RDA) in this paper. RDA integrates linear classification and beamspace transformation into a unified framework by formulating a constrained optimization problem. Our experimental results based on human subjects demonstrate that RDA can efficiently extract the discriminant pattern from prespecified cortical regions to accurately distinguish different brain states.
Adaptive neuron-to-EMG decoder training for FES neuroprostheses
Ethier, Christian; Acuna, Daniel; Solla, Sara A.; Miller, Lee E.
2016-08-01
Objective. We have previously demonstrated a brain-machine interface neuroprosthetic system that provided continuous control of functional electrical stimulation (FES) and restoration of grasp in a primate model of spinal cord injury (SCI). Predicting intended EMG directly from cortical recordings provides a flexible high-dimensional control signal for FES. However, no peripheral signal such as force or EMG is available for training EMG decoders in paralyzed individuals. Approach. Here we present a method for training an EMG decoder in the absence of muscle activity recordings; the decoder relies on mapping behaviorally relevant cortical activity to the inferred EMG activity underlying an intended action. Monkeys were trained at a 2D isometric wrist force task to control a computer cursor by applying force in the flexion, extension, ulnar, and radial directions and execute a center-out task. We used a generic muscle force-to-endpoint force model based on muscle pulling directions to relate each target force to an optimal EMG pattern that attained the target force while minimizing overall muscle activity. We trained EMG decoders during the target hold periods using a gradient descent algorithm that compared EMG predictions to optimal EMG patterns. Main results. We tested this method both offline and online. We quantified both the accuracy of offline force predictions and the ability of a monkey to use these real-time force predictions for closed-loop cursor control. We compared both offline and online results to those obtained with several other direct force decoders, including an optimal decoder computed from concurrently measured neural and force signals. Significance. This novel approach to training an adaptive EMG decoder could make a brain-control FES neuroprosthesis an effective tool to restore the hand function of paralyzed individuals. Clinical implementation would make use of individualized EMG-to-force models. Broad generalization could be achieved by
Joint Estimation and Decoding of Space-Time Trellis Codes
Directory of Open Access Journals (Sweden)
Zhang Jianqiu
2002-01-01
Full Text Available We explore the possibility of using an emerging tool in statistical signal processing, sequential importance sampling (SIS, for joint estimation and decoding of space-time trellis codes (STTC. First, we provide background on SIS, and then we discuss its application to space-time trellis code (STTC systems. It is shown through simulations that SIS is suitable for joint estimation and decoding of STTC with time-varying flat-fading channels when phase ambiguity is avoided. We used a design criterion for STTCs and temporally correlated channels that combats phase ambiguity without pilot signaling. We have shown by simulations that the design is valid.
Real Time Decoding of Color Symbol for Optical Positioning System
Abdul Waheed Malik; Benny Thörnberg; Qaisar Anwar; Tor Arne Johanson; Khurram Shahzad
2015-01-01
This paper presents the design and real-time decoding of a color symbol that can be used as a reference marker for optical navigation. The designed symbol has a circular shape and is printed on paper using two distinct colors. This pair of colors is selected based on the highest achievable signal to noise ratio. The symbol is designed to carry eight bit information. Real time decoding of this symbol is performed using a heterogeneous combination of Field Programmable Gate Array (FPGA) and a m...
Attention in a bayesian framework
DEFF Research Database (Denmark)
Whiteley, Louise Emma; Sahani, Maneesh
2012-01-01
The behavioral phenomena of sensory attention are thought to reflect the allocation of a limited processing resource, but there is little consensus on the nature of the resource or why it should be limited. Here we argue that a fundamental bottleneck emerges naturally within Bayesian models...... of perception, and use this observation to frame a new computational account of the need for, and action of, attention - unifying diverse attentional phenomena in a way that goes beyond previous inferential, probabilistic and Bayesian models. Attentional effects are most evident in cluttered environments......, and include both selective phenomena, where attention is invoked by cues that point to particular stimuli, and integrative phenomena, where attention is invoked dynamically by endogenous processing. However, most previous Bayesian accounts of attention have focused on describing relatively simple experimental...
Probability biases as Bayesian inference
Directory of Open Access Journals (Sweden)
Andre; C. R. Martins
2006-11-01
Full Text Available In this article, I will show how several observed biases in human probabilistic reasoning can be partially explained as good heuristics for making inferences in an environment where probabilities have uncertainties associated to them. Previous results show that the weight functions and the observed violations of coalescing and stochastic dominance can be understood from a Bayesian point of view. We will review those results and see that Bayesian methods should also be used as part of the explanation behind other known biases. That means that, although the observed errors are still errors under the be understood as adaptations to the solution of real life problems. Heuristics that allow fast evaluations and mimic a Bayesian inference would be an evolutionary advantage, since they would give us an efficient way of making decisions. %XX In that sense, it should be no surprise that humans reason with % probability as it has been observed.
Bayesian Methods and Universal Darwinism
Campbell, John
2010-01-01
Bayesian methods since the time of Laplace have been understood by their practitioners as closely aligned to the scientific method. Indeed a recent champion of Bayesian methods, E. T. Jaynes, titled his textbook on the subject Probability Theory: the Logic of Science. Many philosophers of science including Karl Popper and Donald Campbell have interpreted the evolution of Science as a Darwinian process consisting of a 'copy with selective retention' algorithm abstracted from Darwin's theory of Natural Selection. Arguments are presented for an isomorphism between Bayesian Methods and Darwinian processes. Universal Darwinism, as the term has been developed by Richard Dawkins, Daniel Dennett and Susan Blackmore, is the collection of scientific theories which explain the creation and evolution of their subject matter as due to the operation of Darwinian processes. These subject matters span the fields of atomic physics, chemistry, biology and the social sciences. The principle of Maximum Entropy states that system...
Assessing and tuning brain decoders: cross-validation, caveats, and guidelines
Varoquaux, Gaël; Raamana, Pradeep; Engemann, Denis; Hoyos-Idrobo, Andrés; Schwartz, Yannick; Thirion, Bertrand
2016-01-01
Decoding, ie prediction from brain images or signals, calls for empirical evaluation of its predictive power. Such evaluation is achieved via cross-validation, a method also used to tune decoders' hyper-parameters. This paper is a review on cross-validation procedures for decoding in neuroimaging. It includes a didactic overview of the relevant theoretical considerations. Practical aspects are highlighted with an extensive empirical study of the common decoders in within-and across-subject pr...
Bayesian modeling using WinBUGS
Ntzoufras, Ioannis
2009-01-01
A hands-on introduction to the principles of Bayesian modeling using WinBUGS Bayesian Modeling Using WinBUGS provides an easily accessible introduction to the use of WinBUGS programming techniques in a variety of Bayesian modeling settings. The author provides an accessible treatment of the topic, offering readers a smooth introduction to the principles of Bayesian modeling with detailed guidance on the practical implementation of key principles. The book begins with a basic introduction to Bayesian inference and the WinBUGS software and goes on to cover key topics, including: Markov Chain Monte Carlo algorithms in Bayesian inference Generalized linear models Bayesian hierarchical models Predictive distribution and model checking Bayesian model and variable evaluation Computational notes and screen captures illustrate the use of both WinBUGS as well as R software to apply the discussed techniques. Exercises at the end of each chapter allow readers to test their understanding of the presented concepts and all ...
Lazar, Aurel A; Slutskiy, Yevgeniy B; Zhou, Yiyin
2015-03-01
Past work demonstrated how monochromatic visual stimuli could be faithfully encoded and decoded under Nyquist-type rate conditions. Color visual stimuli were then traditionally encoded and decoded in multiple separate monochromatic channels. The brain, however, appears to mix information about color channels at the earliest stages of the visual system, including the retina itself. If information about color is mixed and encoded by a common pool of neurons, how can colors be demixed and perceived? We present Color Video Time Encoding Machines (Color Video TEMs) for encoding color visual stimuli that take into account a variety of color representations within a single neural circuit. We then derive a Color Video Time Decoding Machine (Color Video TDM) algorithm for color demixing and reconstruction of color visual scenes from spikes produced by a population of visual neurons. In addition, we formulate Color Video Channel Identification Machines (Color Video CIMs) for functionally identifying color visual processing performed by a spiking neural circuit. Furthermore, we derive a duality between TDMs and CIMs that unifies the two and leads to a general theory of neural information representation for stereoscopic color vision. We provide examples demonstrating that a massively parallel color visual neural circuit can be first identified with arbitrary precision and its spike trains can be subsequently used to reconstruct the encoded stimuli. We argue that evaluation of the functional identification methodology can be effectively and intuitively performed in the stimulus space. In this space, a signal reconstructed from spike trains generated by the identified neural circuit can be compared to the original stimulus.
Bayesian test and Kuhn's paradigm
Institute of Scientific and Technical Information of China (English)
Chen Xiaoping
2006-01-01
Kuhn's theory of paradigm reveals a pattern of scientific progress,in which normal science alternates with scientific revolution.But Kuhn underrated too much the function of scientific test in his pattern,because he focuses all his attention on the hypothetico-deductive schema instead of Bayesian schema.This paper employs Bayesian schema to re-examine Kuhn's theory of paradigm,to uncover its logical and rational components,and to illustrate the tensional structure of logic and belief,rationality and irrationality,in the process of scientific revolution.
Perception, illusions and Bayesian inference.
Nour, Matthew M; Nour, Joseph M
2015-01-01
Descriptive psychopathology makes a distinction between veridical perception and illusory perception. In both cases a perception is tied to a sensory stimulus, but in illusions the perception is of a false object. This article re-examines this distinction in light of new work in theoretical and computational neurobiology, which views all perception as a form of Bayesian statistical inference that combines sensory signals with prior expectations. Bayesian perceptual inference can solve the 'inverse optics' problem of veridical perception and provides a biologically plausible account of a number of illusory phenomena, suggesting that veridical and illusory perceptions are generated by precisely the same inferential mechanisms.
3D Bayesian contextual classifiers
DEFF Research Database (Denmark)
Larsen, Rasmus
2000-01-01
We extend a series of multivariate Bayesian 2-D contextual classifiers to 3-D by specifying a simultaneous Gaussian distribution for the feature vectors as well as a prior distribution of the class variables of a pixel and its 6 nearest 3-D neighbours.......We extend a series of multivariate Bayesian 2-D contextual classifiers to 3-D by specifying a simultaneous Gaussian distribution for the feature vectors as well as a prior distribution of the class variables of a pixel and its 6 nearest 3-D neighbours....
Bayesian methods for proteomic biomarker development
Directory of Open Access Journals (Sweden)
Belinda Hernández
2015-12-01
In this review we provide an introduction to Bayesian inference and demonstrate some of the advantages of using a Bayesian framework. We summarize how Bayesian methods have been used previously in proteomics and other areas of bioinformatics. Finally, we describe some popular and emerging Bayesian models from the statistical literature and provide a worked tutorial including code snippets to show how these methods may be applied for the evaluation of proteomic biomarkers.
Construction and decoding of matrix-product codes from nested codes
DEFF Research Database (Denmark)
Hernando, Fernando; Lally, Kristine; Ruano, Diego
2009-01-01
We consider matrix-product codes [C1 ... Cs] · A, where C1, ..., Cs are nested linear codes and matrix A has full rank. We compute their minimum distance and provide a decoding algorithm when A is a non-singular by columns matrix. The decoding algorithm decodes up to half of the minimum distance....
Bayesian variable order Markov models: Towards Bayesian predictive state representations
C. Dimitrakakis
2009-01-01
We present a Bayesian variable order Markov model that shares many similarities with predictive state representations. The resulting models are compact and much easier to specify and learn than classical predictive state representations. Moreover, we show that they significantly outperform a more st
Bayesian networks and food security - An introduction
Stein, A.
2004-01-01
This paper gives an introduction to Bayesian networks. Networks are defined and put into a Bayesian context. Directed acyclical graphs play a crucial role here. Two simple examples from food security are addressed. Possible uses of Bayesian networks for implementation and further use in decision sup
Bayesian Model Averaging for Propensity Score Analysis
Kaplan, David; Chen, Jianshen
2013-01-01
The purpose of this study is to explore Bayesian model averaging in the propensity score context. Previous research on Bayesian propensity score analysis does not take into account model uncertainty. In this regard, an internally consistent Bayesian framework for model building and estimation must also account for model uncertainty. The…
A Bayesian Nonparametric Approach to Test Equating
Karabatsos, George; Walker, Stephen G.
2009-01-01
A Bayesian nonparametric model is introduced for score equating. It is applicable to all major equating designs, and has advantages over previous equating models. Unlike the previous models, the Bayesian model accounts for positive dependence between distributions of scores from two tests. The Bayesian model and the previous equating models are…
TC81220F (HAWK) MPEG 2 system decoder LSI; MPEG2 system decoder LSI TC81220F (HAWK)
Energy Technology Data Exchange (ETDEWEB)
NONE
1999-03-01
Satellite broadcasting, cable broadcasting, and ground wave broadcasting have been digitized gradually. In the video and audio data communication, the MPEG2 (Moving Picture Experts Group 2) technology for compression and extension is important. A system LSI that includes an MPEG2 decoder in a receiving set (Set Top Box) is required to each broadcasting. To deduce the system cost, Toshiba developed a TX3904 MCU (microcontroller) that controls the system, a transport processor that selects the packet-multiplexed data, and TC81220F (HAWK) that puts MPEG2 audio video decoders in one chip. (translated by NEDO)
Error Locked Encoder and Decoder for Nanomemory Application
Directory of Open Access Journals (Sweden)
Y. Sharath
2014-03-01
Full Text Available Memory cells have been protected from soft errors for more than a decade; due to the increase in soft error rate in logic circuits, the encoder and decoder circuitry around the memory blocks have become susceptible to soft errors as well and must also be protected. We introduce a new approach to design fault-secure encoder and decoder circuitry for memory designs. The key novel contribution of this paper is identifying and defining a new class of error-correcting codes whose redundancy makes the design of fault-secure detectors (FSD particularly simple. We further quantify the importance of protecting encoder and decoder circuitry against transient errors, illustrating a scenario where the system failure rate (FIT is dominated by the failure rate of the encoder and decoder. We prove that Euclidean Geometry Low-Density Parity-Check (EG-LDPC codes have the fault-secure detector capability. Using some of the smaller EG-LDPC codes, we can tolerate bit or nanowire defect rates of 10% and fault rates of 10-18 upsets/device/cycle, achieving a FIT rate at or below one for the entire memory system and a memory density of 1011 bit/cm with nanowire pitch of 10 nm for memory blocks of 10 Mb or larger. Larger EG-LDPC codes can achieve even higher reliability and lower area overhead.
Decoding the Disciplines: An Approach to Scientific Thinking
Pinnow, Eleni
2016-01-01
The Decoding the Disciplines methodology aims to teach students to think like experts in discipline-specific tasks. The central aspect of the methodology is to identify a bottleneck in the course content: a particular topic that a substantial number of students struggle to master. The current study compared the efficacy of standard lecture and…
Complete ML Decoding orf the (73,45) PG Code
DEFF Research Database (Denmark)
Justesen, Jørn; Høholdt, Tom; Hjaltason, Johan
2005-01-01
Our recent proof of the completeness of decoding by list bit flipping is reviewed. The proof is based on an enumeration of all cosets of low weight in terms of their minimum weight and syndrome weight. By using a geometric description of the error patterns we characterize all remaining cosets....
Gradient Descent Bit Flipping Algorithms for Decoding LDPC Codes
Wadayama, Tadashi; Nakamura, Keisuke; Yagita, Masayuki; Funahashi, Yuuki; Usami, Shogo; Takumi, Ichi
2007-01-01
A novel class of bit-flipping (BF) algorithms for decoding low-density parity-check (LDPC) codes is presented. The proposed algorithms, which are called gradient descent bit flipping (GDBF) algorithms, can be regarded as simplified gradient descent algorithms. Based on gradient descent formulation, the proposed algorithms are naturally derived from a simple non-linear objective function.
Real Time Decoding of Color Symbol for Optical Positioning System
Directory of Open Access Journals (Sweden)
Abdul Waheed Malik
2015-01-01
Full Text Available This paper presents the design and real-time decoding of a color symbol that can be used as a reference marker for optical navigation. The designed symbol has a circular shape and is printed on paper using two distinct colors. This pair of colors is selected based on the highest achievable signal to noise ratio. The symbol is designed to carry eight bit information. Real time decoding of this symbol is performed using a heterogeneous combination of Field Programmable Gate Array (FPGA and a microcontroller. An image sensor having a resolution of 1600 by 1200 pixels is used to capture images of symbols in complex back‐ grounds. Dynamic image segmentation, component labeling and feature extraction was performed on the FPGA. The region of interest was further computed from the extracted features. Feature data belonging to the symbol was sent from the FPGA to the microcontroller. Image processing tasks are partitioned between the FPGA and microcontroller based on data intensity. Experiments were performed to verify the rotational independence of the symbols. The maximum distance between camera and symbol allowing for correct detection and decoding was analyzed. Experiments were also performed to analyze the number of generated image components and sub-pixel precision versus different light sources and intensities. The proposed hardware architecture can process up to 55 frames per second for accurate detection and decoding of symbols at two Megapixels resolution. The power consumption of the complete system is 342mw.
Relationships between grammatical encoding and decoding : an experimental psycholinguistic study
Olsthoorn, Nomi Maria
2007-01-01
Although usually considered distinct processes, grammatical encoding and decoding have many theoretical and empirical commonalities. In two series of experiments relationships between the two processes are explored. The first series uses a dual task (edited reading aloud (ERA)) paradigm to test the
A quantum algorithm for Viterbi decoding of classical convolutional codes
Grice, Jon R.; Meyer, David A.
2015-07-01
We present a quantum Viterbi algorithm (QVA) with better than classical performance under certain conditions. In this paper, the proposed algorithm is applied to decoding classical convolutional codes, for instance, large constraint length and short decode frames . Other applications of the classical Viterbi algorithm where is large (e.g., speech processing) could experience significant speedup with the QVA. The QVA exploits the fact that the decoding trellis is similar to the butterfly diagram of the fast Fourier transform, with its corresponding fast quantum algorithm. The tensor-product structure of the butterfly diagram corresponds to a quantum superposition that we show can be efficiently prepared. The quantum speedup is possible because the performance of the QVA depends on the fanout (number of possible transitions from any given state in the hidden Markov model) which is in general much less than . The QVA constructs a superposition of states which correspond to all legal paths through the decoding lattice, with phase as a function of the probability of the path being taken given received data. A specialized amplitude amplification procedure is applied one or more times to recover a superposition where the most probable path has a high probability of being measured.
Method for Veterbi decoding of large constraint length convolutional codes
Hsu, In-Shek; Truong, Trieu-Kie; Reed, Irving S.; Jing, Sun
1988-05-01
A new method of Viterbi decoding of convolutional codes lends itself to a pipline VLSI architecture using a single sequential processor to compute the path metrics in the Viterbi trellis. An array method is used to store the path information for NK intervals where N is a number, and K is constraint length. The selected path at the end of each NK interval is then selected from the last entry in the array. A trace-back method is used for returning to the beginning of the selected path back, i.e., to the first time unit of the interval NK to read out the stored branch metrics of the selected path which correspond to the message bits. The decoding decision made in this way is no longer maximum likelihood, but can be almost as good, provided that constraint length K in not too small. The advantage is that for a long message, it is not necessary to provide a large memory to store the trellis derived information until the end of the message to select the path that is to be decoded; the selection is made at the end of every NK time unit, thus decoding a long message in successive blocks.
Fast decoding of codes from algebraic plane curves
DEFF Research Database (Denmark)
Justesen, Jørn; Larsen, Knud J.; Jensen, Helge Elbrønd;
1992-01-01
Improvement to an earlier decoding algorithm for codes from algebraic geometry is presented. For codes from an arbitrary regular plane curve the authors correct up to d*/2-m2 /8+m/4-9/8 errors, where d* is the designed distance of the code and m is the degree of the curve. The complexity of finding...
Bayesian Classification of Image Structures
DEFF Research Database (Denmark)
Goswami, Dibyendu; Kalkan, Sinan; Krüger, Norbert
2009-01-01
In this paper, we describe work on Bayesian classi ers for distinguishing between homogeneous structures, textures, edges and junctions. We build semi-local classiers from hand-labeled images to distinguish between these four different kinds of structures based on the concept of intrinsic dimensi...
Bayesian Agglomerative Clustering with Coalescents
Teh, Yee Whye; Daumé III, Hal; Roy, Daniel
2009-01-01
We introduce a new Bayesian model for hierarchical clustering based on a prior over trees called Kingman's coalescent. We develop novel greedy and sequential Monte Carlo inferences which operate in a bottom-up agglomerative fashion. We show experimentally the superiority of our algorithms over others, and demonstrate our approach in document clustering and phylolinguistics.
Bayesian NL interpretation and learning
H. Zeevat
2011-01-01
Everyday natural language communication is normally successful, even though contemporary computational linguistics has shown that NL is characterised by very high degree of ambiguity and the results of stochastic methods are not good enough to explain the high success rate. Bayesian natural language
Differentiated Bayesian Conjoint Choice Designs
Z. Sándor (Zsolt); M. Wedel (Michel)
2003-01-01
textabstractPrevious conjoint choice design construction procedures have produced a single design that is administered to all subjects. This paper proposes to construct a limited set of different designs. The designs are constructed in a Bayesian fashion, taking into account prior uncertainty about
Bayesian inference for Hawkes processes
DEFF Research Database (Denmark)
Rasmussen, Jakob Gulddahl
The Hawkes process is a practically and theoretically important class of point processes, but parameter-estimation for such a process can pose various problems. In this paper we explore and compare two approaches to Bayesian inference. The first approach is based on the so-called conditional...
Bayesian stable isotope mixing models
In this paper we review recent advances in Stable Isotope Mixing Models (SIMMs) and place them into an over-arching Bayesian statistical framework which allows for several useful extensions. SIMMs are used to quantify the proportional contributions of various sources to a mixtur...
Bayesian inference for Hawkes processes
DEFF Research Database (Denmark)
Rasmussen, Jakob Gulddahl
2013-01-01
The Hawkes process is a practically and theoretically important class of point processes, but parameter-estimation for such a process can pose various problems. In this paper we explore and compare two approaches to Bayesian inference. The first approach is based on the so-called conditional...
3-D contextual Bayesian classifiers
DEFF Research Database (Denmark)
Larsen, Rasmus
In this paper we will consider extensions of a series of Bayesian 2-D contextual classification pocedures proposed by Owen (1984) Hjort & Mohn (1984) and Welch & Salter (1971) and Haslett (1985) to 3 spatial dimensions. It is evident that compared to classical pixelwise classification further...
Bayesian image restoration, using configurations
DEFF Research Database (Denmark)
Thorarinsdottir, Thordis
configurations are expressed in terms of the mean normal measure of the random set. These probabilities are used as prior probabilities in a Bayesian image restoration approach. Estimation of the remaining parameters in the model is outlined for salt and pepper noise. The inference in the model is discussed...
Bayesian image restoration, using configurations
DEFF Research Database (Denmark)
Thorarinsdottir, Thordis Linda
2006-01-01
configurations are expressed in terms of the mean normal measure of the random set. These probabilities are used as prior probabilities in a Bayesian image restoration approach. Estimation of the remaining parameters in the model is outlined for the salt and pepper noise. The inference in the model is discussed...
Bayesian Analysis of Experimental Data
Directory of Open Access Journals (Sweden)
Lalmohan Bhar
2013-10-01
Full Text Available Analysis of experimental data from Bayesian point of view has been considered. Appropriate methodology has been developed for application into designed experiments. Normal-Gamma distribution has been considered for prior distribution. Developed methodology has been applied to real experimental data taken from long term fertilizer experiments.
An efficient decoding for low density parity check codes
Zhao, Ling; Zhang, Xiaolin; Zhu, Manjie
2009-12-01
Low density parity check (LDPC) codes are a class of forward-error-correction codes. They are among the best-known codes capable of achieving low bit error rates (BER) approaching Shannon's capacity limit. Recently, LDPC codes have been adopted by the European Digital Video Broadcasting (DVB-S2) standard, and have also been proposed for the emerging IEEE 802.16 fixed and mobile broadband wireless-access standard. The consultative committee for space data system (CCSDS) has also recommended using LDPC codes in the deep space communications and near-earth communications. It is obvious that LDPC codes will be widely used in wired and wireless communication, magnetic recording, optical networking, DVB, and other fields in the near future. Efficient hardware implementation of LDPC codes is of great interest since LDPC codes are being considered for a wide range of applications. This paper presents an efficient partially parallel decoder architecture suited for quasi-cyclic (QC) LDPC codes using Belief propagation algorithm for decoding. Algorithmic transformation and architectural level optimization are incorporated to reduce the critical path. First, analyze the check matrix of LDPC code, to find out the relationship between the row weight and the column weight. And then, the sharing level of the check node updating units (CNU) and the variable node updating units (VNU) are determined according to the relationship. After that, rearrange the CNU and the VNU, and divide them into several smaller parts, with the help of some assistant logic circuit, these smaller parts can be grouped into CNU during the check node update processing and grouped into VNU during the variable node update processing. These smaller parts are called node update kernel units (NKU) and the assistant logic circuit are called node update auxiliary unit (NAU). With NAUs' help, the two steps of iteration operation are completed by NKUs, which brings in great hardware resource reduction. Meanwhile
On Lattice Sequential Decoding for Large MIMO Systems
Ali, Konpal S.
2014-04-01
Due to their ability to provide high data rates, Multiple-Input Multiple-Output (MIMO) wireless communication systems have become increasingly popular. Decoding of these systems with acceptable error performance is computationally very demanding. In the case of large overdetermined MIMO systems, we employ the Sequential Decoder using the Fano Algorithm. A parameter called the bias is varied to attain different performance-complexity trade-offs. Low values of the bias result in excellent performance but at the expense of high complexity and vice versa for higher bias values. We attempt to bound the error by bounding the bias, using the minimum distance of a lattice. Also, a particular trend is observed with increasing SNR: a region of low complexity and high error, followed by a region of high complexity and error falling, and finally a region of low complexity and low error. For lower bias values, the stages of the trend are incurred at lower SNR than for higher bias values. This has the important implication that a low enough bias value, at low to moderate SNR, can result in low error and low complexity even for large MIMO systems. Our work is compared against Lattice Reduction (LR) aided Linear Decoders (LDs). Another impressive observation for low bias values that satisfy the error bound is that the Sequential Decoder\\'s error is seen to fall with increasing system size, while it grows for the LR-aided LDs. For the case of large underdetermined MIMO systems, Sequential Decoding with two preprocessing schemes is proposed – 1) Minimum Mean Square Error Generalized Decision Feedback Equalization (MMSE-GDFE) preprocessing 2) MMSE-GDFE preprocessing, followed by Lattice Reduction and Greedy Ordering. Our work is compared against previous work which employs Sphere Decoding preprocessed using MMSE-GDFE, Lattice Reduction and Greedy Ordering. For the case of large systems, this results in high complexity and difficulty in choosing the sphere radius. Our schemes
Topics in Bayesian statistics and maximum entropy
International Nuclear Information System (INIS)
Notions of Bayesian decision theory and maximum entropy methods are reviewed with particular emphasis on probabilistic inference and Bayesian modeling. The axiomatic approach is considered as the best justification of Bayesian analysis and maximum entropy principle applied in natural sciences. Particular emphasis is put on solving the inverse problem in digital image restoration and Bayesian modeling of neural networks. Further topics addressed briefly include language modeling, neutron scattering, multiuser detection and channel equalization in digital communications, genetic information, and Bayesian court decision-making. (author)
Windowed Decoding of Protograph-based LDPC Convolutional Codes over Erasure Channels
Iyengar, Aravind; Siegel, Paul; Wolf, Jack; Vanelli-Coralli, Alessandro; Corazza, Giovanni
2010-01-01
We consider a windowed decoding scheme for LDPC convolutional codes that is based on the belief-propagation (BP) algorithm. We discuss the advantages of this decoding scheme and identify certain characteristics of LDPC convolutional code ensembles that exhibit good performance with the windowed decoder. We will consider the performance of these ensembles and codes over erasure channels with and without memory. We show that the structure of LDPC convolutional code ensembles is suitable to obtain performance close to the theoretical limits over the memoryless erasure channel, both for the BP decoder and windowed decoding. However, the same structure imposes limitations on the performance over erasure channels with memory.
New concatenated soft decoding of Reed-Solomon codes with lower complexities
Institute of Scientific and Technical Information of China (English)
BIAN Yin-bing; FENG Guang-zeng
2009-01-01
To improve error-correcting performance, an iterative concatenated soft decoding algorithm for Reed-Solomon (RS) codes is presented in this article. This algorithm brings both complexity as well as advantages in performance over presently popular sott decoding algorithms. The proposed algorithm consists of two powerful soft decoding techniques, adaptive belief propagation (ABP) and box and match algorithm (BMA), which are serially concatenated by the accumulated log-likelihood ratio (ALLR).Simulation results show that, compared with ABP and ABP-BMA algorithms, the proposed algorithm can bring more decoding gains and a better tradeoffbetween the decoding performance and complexity.
bgc: Software for Bayesian estimation of genomic clines.
Gompert, Z; Buerkle, C A
2012-11-01
Introgression in admixed populations can be used to identify candidate loci that might underlie adaptation or reproductive isolation. The Bayesian genomic cline model provides a framework for quantifying variable introgression in admixed populations and identifying regions of the genome with extreme introgression that are potentially associated with variation in fitness. Here we describe the bgc software, which uses Markov chain Monte Carlo to estimate the joint posterior probability distribution of the parameters in the Bayesian genomic cline model and designate outlier loci. This software can be used with next-generation sequence data, accounts for uncertainty in genotypic state, and can incorporate information from linked loci on a genetic map. Output from the analysis is written to an HDF5 file for efficient storage and manipulation. This software is written in C++. The source code, software manual, compilation instructions and example data sets are available under the GNU Public License at http://sites.google.com/site/bgcsoftware/. PMID:22978657
For whom will the Bayesian agents vote?
Caticha, Nestor; Vicente, Renato
2015-01-01
Within an agent-based model where moral classifications are socially learned, we ask if a population of agents behaves in a way that may be compared with conservative or liberal positions in the real political spectrum. We assume that agents first experience a formative period, in which they adjust their learning style acting as supervised Bayesian adaptive learners. The formative phase is followed by a period of social influence by reinforcement learning. By comparing data generated by the agents with data from a sample of 15000 Moral Foundation questionnaires we found the following. 1. The number of information exchanges in the formative phase correlates positively with statistics identifying liberals in the social influence phase. This is consistent with recent evidence that connects the dopamine receptor D4-7R gene, political orientation and early age social clique size. 2. The learning algorithms that result from the formative phase vary in the way they treat novelty and corroborative information with mo...
Are visual impairments responsible for emotion decoding deficits in alcohol-dependence?
Directory of Open Access Journals (Sweden)
Fabien eD'Hondt
2014-03-01
Full Text Available Emotional visual perception deficits constitute a major problem in alcohol-dependence. Indeed, the ability to assess the affective content of external cues is a key adaptive function, as it allows on the one hand the processing of potentially threatening or advantageous stimuli, and on the other hand the establishment of appropriate social interactions (by enabling rapid decoding of the affective state of others from their facial expressions. While such deficits have been classically considered as reflecting a genuine emotion decoding impairment in alcohol-dependence, converging evidence suggests that underlying visual deficits might play a role in emotional alterations. This hypothesis appears to be relevant especially as data from healthy populations indicate that a coarse but fast analysis of visual inputs would allow emotional processing to arise from early stages of perception. After reviewing those findings and the associated models, the present paper underlines data showing that rapid interactions between emotion and vision could be impaired in alcohol-dependence and provides new research avenues that may ultimately offer a better understanding of the roots of emotional deficits in this pathological state.
Bayesian analysis of rare events
Straub, Daniel; Papaioannou, Iason; Betz, Wolfgang
2016-06-01
In many areas of engineering and science there is an interest in predicting the probability of rare events, in particular in applications related to safety and security. Increasingly, such predictions are made through computer models of physical systems in an uncertainty quantification framework. Additionally, with advances in IT, monitoring and sensor technology, an increasing amount of data on the performance of the systems is collected. This data can be used to reduce uncertainty, improve the probability estimates and consequently enhance the management of rare events and associated risks. Bayesian analysis is the ideal method to include the data into the probabilistic model. It ensures a consistent probabilistic treatment of uncertainty, which is central in the prediction of rare events, where extrapolation from the domain of observation is common. We present a framework for performing Bayesian updating of rare event probabilities, termed BUS. It is based on a reinterpretation of the classical rejection-sampling approach to Bayesian analysis, which enables the use of established methods for estimating probabilities of rare events. By drawing upon these methods, the framework makes use of their computational efficiency. These methods include the First-Order Reliability Method (FORM), tailored importance sampling (IS) methods and Subset Simulation (SuS). In this contribution, we briefly review these methods in the context of the BUS framework and investigate their applicability to Bayesian analysis of rare events in different settings. We find that, for some applications, FORM can be highly efficient and is surprisingly accurate, enabling Bayesian analysis of rare events with just a few model evaluations. In a general setting, BUS implemented through IS and SuS is more robust and flexible.
Decoding bipedal locomotion from the rat sensorimotor cortex
Rigosa, J.; Panarese, A.; Dominici, N.; Friedli, L.; van den Brand, R.; Carpaneto, J.; DiGiovanna, J.; Courtine, G.; Micera, S.
2015-10-01
Objective. Decoding forelimb movements from the firing activity of cortical neurons has been interfaced with robotic and prosthetic systems to replace lost upper limb functions in humans. Despite the potential of this approach to improve locomotion and facilitate gait rehabilitation, decoding lower limb movement from the motor cortex has received comparatively little attention. Here, we performed experiments to identify the type and amount of information that can be decoded from neuronal ensemble activity in the hindlimb area of the rat motor cortex during bipedal locomotor tasks. Approach. Rats were trained to stand, step on a treadmill, walk overground and climb staircases in a bipedal posture. To impose this gait, the rats were secured in a robotic interface that provided support against the direction of gravity and in the mediolateral direction, but behaved transparently in the forward direction. After completion of training, rats were chronically implanted with a micro-wire array spanning the left hindlimb motor cortex to record single and multi-unit activity, and bipolar electrodes into 10 muscles of the right hindlimb to monitor electromyographic signals. Whole-body kinematics, muscle activity, and neural signals were simultaneously recorded during execution of the trained tasks over multiple days of testing. Hindlimb kinematics, muscle activity, gait phases, and locomotor tasks were decoded using offline classification algorithms. Main results. We found that the stance and swing phases of gait and the locomotor tasks were detected with accuracies as robust as 90% in all rats. Decoded hindlimb kinematics and muscle activity exhibited a larger variability across rats and tasks. Significance. Our study shows that the rodent motor cortex contains useful information for lower limb neuroprosthetic development. However, brain-machine interfaces estimating gait phases or locomotor behaviors, instead of continuous variables such as limb joint positions or speeds
Ki, Dae Wook; Kim, Jae Ho
2013-07-01
We propose a fast new multiple run_before decoding method in context-adaptive variable length coding (CAVLC). The transform coefficients are coded using CAVLC, in which the run_before symbols are generated for a 4×4 block input. To speed up the CAVLC decoding, the run_before symbols need to be decoded in parallel. We implemented a new CAVLC table for simultaneous decoding of up to three run_befores. The simulation results show a Total Speed-up Factor of 205%˜144% over various resolutions and quantization steps.
Bayesian methods for measures of agreement
Broemeling, Lyle D
2009-01-01
Using WinBUGS to implement Bayesian inferences of estimation and testing hypotheses, Bayesian Methods for Measures of Agreement presents useful methods for the design and analysis of agreement studies. It focuses on agreement among the various players in the diagnostic process.The author employs a Bayesian approach to provide statistical inferences based on various models of intra- and interrater agreement. He presents many examples that illustrate the Bayesian mode of reasoning and explains elements of a Bayesian application, including prior information, experimental information, the likelihood function, posterior distribution, and predictive distribution. The appendices provide the necessary theoretical foundation to understand Bayesian methods as well as introduce the fundamentals of programming and executing the WinBUGS software.Taking a Bayesian approach to inference, this hands-on book explores numerous measures of agreement, including the Kappa coefficient, the G coefficient, and intraclass correlation...
Plug & Play object oriented Bayesian networks
DEFF Research Database (Denmark)
Bangsø, Olav; Flores, J.; Jensen, Finn Verner
2003-01-01
Object oriented Bayesian networks have proven themselves useful in recent years. The idea of applying an object oriented approach to Bayesian networks has extended their scope to larger domains that can be divided into autonomous but interrelated entities. Object oriented Bayesian networks have...... been shown to be quite suitable for dynamic domains as well. However, processing object oriented Bayesian networks in practice does not take advantage of their modular structure. Normally the object oriented Bayesian network is transformed into a Bayesian network and, inference is performed...... by constructing a junction tree from this network. In this paper we propose a method for translating directly from object oriented Bayesian networks to junction trees, avoiding the intermediate translation. We pursue two main purposes: firstly, to maintain the original structure organized in an instance tree...
Bayesian Approach to the Best Estimate of the Hubble Constant
Institute of Scientific and Technical Information of China (English)
王晓峰; 陈黎; 李宗伟
2001-01-01
A Bayesian approach is used to derive the probability distribution (PD) of the Hubble constant H0 from recent measurements including supernovae Ia, the Tully-Fisher relation, population Ⅱ and physical methods. The discrepancies among these PDs are briefly discussed. The combined value of all the measurements is obtained,with a 95% confidence interval of 58.7 ＜ Ho ＜ 67.3 (km·s-1.Mpc-1).
Weijs, Liesbeth; Yang, Raymond S H; Das, Krishna; Covaci, Adrian; Blust, Ronny
2013-05-01
Physiologically based pharmacokinetic (PBPK) modeling in marine mammals is a challenge because of the lack of parameter information and the ban on exposure experiments. To minimize uncertainty and variability, parameter estimation methods are required for the development of reliable PBPK models. The present study is the first to develop PBPK models for the lifetime bioaccumulation of p,p'-DDT, p,p'-DDE, and p,p'-DDD in harbor porpoises. In addition, this study is also the first to apply the Bayesian approach executed with Markov chain Monte Carlo simulations using two data sets of harbor porpoises from the Black and North Seas. Parameters from the literature were used as priors for the first "model update" using the Black Sea data set, the resulting posterior parameters were then used as priors for the second "model update" using the North Sea data set. As such, PBPK models with parameters specific for harbor porpoises could be strengthened with more robust probability distributions. As the science and biomonitoring effort progress in this area, more data sets will become available to further strengthen and update the parameters in the PBPK models for harbor porpoises as a species anywhere in the world. Further, such an approach could very well be extended to other protected marine mammals.
da Rocha, Edroaldo Lummertz; Ung, Choong Yong; McGehee, Cordelia D; Correia, Cristina; Li, Hu
2016-06-01
The sequential chain of interactions altering the binary state of a biomolecule represents the 'information flow' within a cellular network that determines phenotypic properties. Given the lack of computational tools to dissect context-dependent networks and gene activities, we developed NetDecoder, a network biology platform that models context-dependent information flows using pairwise phenotypic comparative analyses of protein-protein interactions. Using breast cancer, dyslipidemia and Alzheimer's disease as case studies, we demonstrate NetDecoder dissects subnetworks to identify key players significantly impacting cell behaviour specific to a given disease context. We further show genes residing in disease-specific subnetworks are enriched in disease-related signalling pathways and information flow profiles, which drive the resulting disease phenotypes. We also devise a novel scoring scheme to quantify key genes-network routers, which influence many genes, key targets, which are influenced by many genes, and high impact genes, which experience a significant change in regulation. We show the robustness of our results against parameter changes. Our network biology platform includes freely available source code (http://www.NetDecoder.org) for researchers to explore genome-wide context-dependent information flow profiles and key genes, given a set of genes of particular interest and transcriptome data. More importantly, NetDecoder will enable researchers to uncover context-dependent drug targets. PMID:26975659
Bayesian Inference and Online Learning in Poisson Neuronal Networks.
Huang, Yanping; Rao, Rajesh P N
2016-08-01
Motivated by the growing evidence for Bayesian computation in the brain, we show how a two-layer recurrent network of Poisson neurons can perform both approximate Bayesian inference and learning for any hidden Markov model. The lower-layer sensory neurons receive noisy measurements of hidden world states. The higher-layer neurons infer a posterior distribution over world states via Bayesian inference from inputs generated by sensory neurons. We demonstrate how such a neuronal network with synaptic plasticity can implement a form of Bayesian inference similar to Monte Carlo methods such as particle filtering. Each spike in a higher-layer neuron represents a sample of a particular hidden world state. The spiking activity across the neural population approximates the posterior distribution over hidden states. In this model, variability in spiking is regarded not as a nuisance but as an integral feature that provides the variability necessary for sampling during inference. We demonstrate how the network can learn the likelihood model, as well as the transition probabilities underlying the dynamics, using a Hebbian learning rule. We present results illustrating the ability of the network to perform inference and learning for arbitrary hidden Markov models.
Iterative decoding of Generalized Parallel Concatenated Block codes using cyclic permutations
Directory of Open Access Journals (Sweden)
Hamid Allouch
2012-09-01
Full Text Available Iterative decoding techniques have gain popularity due to their performance and their application in most communications systems. In this paper, we present a new application of our iterative decoder on the GPCB (Generalized Parallel Concatenated Block codes which uses cyclic permutations. We introduce a new variant of the component decoder. After extensive simulation; the obtained result is very promising compared with several existing methods. We evaluate the effects of various parameters component codes, interleaver size, block size, and the number of iterations. Three interesting results are obtained; the first one is that the performances in terms of BER (Bit Error Rate of the new constituent decoder are relatively similar to that of original one. Secondly our turbo decoding outperforms another turbo decoder for some linear block codes. Thirdly the proposed iterative decoding of GPCB-BCH (75, 51 is about 2.1dB from its Shannon limit.
Trellis-Based Check Node Processing for Low-Complexity Nonbinary LP Decoding
Punekar, Mayur
2011-01-01
Linear Programming (LP) decoding is emerging as an attractive alternative to decode Low-Density Parity-Check (LDPC) codes. However, the earliest LP decoders proposed for binary and nonbinary LDPC codes are not suitable for use at moderate and large code lengths. To overcome this problem, Vontobel et al. developed an iterative Low-Complexity LP (LCLP) decoding algorithm for binary LDPC codes. The variable and check node calculations of binary LCLP decoding algorithm are related to those of binary Belief Propagation (BP). The present authors generalized this work to derive an iterative LCLP decoding algorithm for nonbinary linear codes. Contrary to binary LCLP, the variable and check node calculations of this algorithm are in general different from that of nonbinary BP. The overall complexity of nonbinary LCLP decoding is linear in block length; however the complexity of its check node calculations is exponential in the check node degree. In this paper, we propose a modified BCJR algorithm for efficient check n...
Optimal Threshold-Based Multi-Trial Error/Erasure Decoding with the Guruswami-Sudan Algorithm
Senger, Christian; Bossert, Martin; Zyablov, Victor V
2011-01-01
Traditionally, multi-trial error/erasure decoding of Reed-Solomon (RS) codes is based on Bounded Minimum Distance (BMD) decoders with an erasure option. Such decoders have error/erasure tradeoff factor L=2, which means that an error is twice as expensive as an erasure in terms of the code's minimum distance. The Guruswami-Sudan (GS) list decoder can be considered as state of the art in algebraic decoding of RS codes. Besides an erasure option, it allows to adjust L to values in the range 1=1 times. We show that BMD decoders with z_BMD decoding trials can result in lower residual codeword error probability than GS decoders with z_GS trials, if z_BMD is only slightly larger than z_GS. This is of practical interest since BMD decoders generally have lower computational complexity than GS decoders.
Flexible Bayesian Nonparametric Priors and Bayesian Computational Methods
Zhu, Weixuan
2016-01-01
The definition of vectors of dependent random probability measures is a topic of interest in Bayesian nonparametrics. They represent dependent nonparametric prior distributions that are useful for modelling observables for which specific covariate values are known. Our first contribution is the introduction of novel multivariate vectors of two-parameter Poisson-Dirichlet process. The dependence is induced by applying a L´evy copula to the marginal L´evy intensities. Our attenti...
Bayesian versus 'plain-vanilla Bayesian' multitarget statistics
Mahler, Ronald P. S.
2004-08-01
Finite-set statistics (FISST) is a direct generalization of single-sensor, single-target Bayes statistics to the multisensor-multitarget realm, based on random set theory. Various aspects of FISST are being investigated by several research teams around the world. In recent years, however, a few partisans have claimed that a "plain-vanilla Bayesian approach" suffices as down-to-earth, "straightforward," and general "first principles" for multitarget problems. Therefore, FISST is mere mathematical "obfuscation." In this and a companion paper I demonstrate the speciousness of these claims. In this paper I summarize general Bayes statistics, what is required to use it in multisensor-multitarget problems, and why FISST is necessary to make it practical. Then I demonstrate that the "plain-vanilla Bayesian approach" is so heedlessly formulated that it is erroneous, not even Bayesian denigrates FISST concepts while unwittingly assuming them, and has resulted in a succession of algorithms afflicted by inherent -- but less than candidly acknowledged -- computational "logjams."
Bayesian inference on proportional elections.
Brunello, Gabriel Hideki Vatanabe; Nakano, Eduardo Yoshio
2015-01-01
Polls for majoritarian voting systems usually show estimates of the percentage of votes for each candidate. However, proportional vote systems do not necessarily guarantee the candidate with the most percentage of votes will be elected. Thus, traditional methods used in majoritarian elections cannot be applied on proportional elections. In this context, the purpose of this paper was to perform a Bayesian inference on proportional elections considering the Brazilian system of seats distribution. More specifically, a methodology to answer the probability that a given party will have representation on the chamber of deputies was developed. Inferences were made on a Bayesian scenario using the Monte Carlo simulation technique, and the developed methodology was applied on data from the Brazilian elections for Members of the Legislative Assembly and Federal Chamber of Deputies in 2010. A performance rate was also presented to evaluate the efficiency of the methodology. Calculations and simulations were carried out using the free R statistical software. PMID:25786259
Bayesian approach to rough set
Marwala, Tshilidzi
2007-01-01
This paper proposes an approach to training rough set models using Bayesian framework trained using Markov Chain Monte Carlo (MCMC) method. The prior probabilities are constructed from the prior knowledge that good rough set models have fewer rules. Markov Chain Monte Carlo sampling is conducted through sampling in the rough set granule space and Metropolis algorithm is used as an acceptance criteria. The proposed method is tested to estimate the risk of HIV given demographic data. The results obtained shows that the proposed approach is able to achieve an average accuracy of 58% with the accuracy varying up to 66%. In addition the Bayesian rough set give the probabilities of the estimated HIV status as well as the linguistic rules describing how the demographic parameters drive the risk of HIV.
Bayesian priors for transiting planets
Kipping, David M
2016-01-01
As astronomers push towards discovering ever-smaller transiting planets, it is increasingly common to deal with low signal-to-noise ratio (SNR) events, where the choice of priors plays an influential role in Bayesian inference. In the analysis of exoplanet data, the selection of priors is often treated as a nuisance, with observers typically defaulting to uninformative distributions. Such treatments miss a key strength of the Bayesian framework, especially in the low SNR regime, where even weak a priori information is valuable. When estimating the parameters of a low-SNR transit, two key pieces of information are known: (i) the planet has the correct geometric alignment to transit and (ii) the transit event exhibits sufficient signal-to-noise to have been detected. These represent two forms of observational bias. Accordingly, when fitting transits, the model parameter priors should not follow the intrinsic distributions of said terms, but rather those of both the intrinsic distributions and the observational ...
Bayesian Source Separation and Localization
Knuth, K H
1998-01-01
The problem of mixed signals occurs in many different contexts; one of the most familiar being acoustics. The forward problem in acoustics consists of finding the sound pressure levels at various detectors resulting from sound signals emanating from the active acoustic sources. The inverse problem consists of using the sound recorded by the detectors to separate the signals and recover the original source waveforms. In general, the inverse problem is unsolvable without additional information. This general problem is called source separation, and several techniques have been developed that utilize maximum entropy, minimum mutual information, and maximum likelihood. In previous work, it has been demonstrated that these techniques can be recast in a Bayesian framework. This paper demonstrates the power of the Bayesian approach, which provides a natural means for incorporating prior information into a source model. An algorithm is developed that utilizes information regarding both the statistics of the amplitudes...
Bayesian Inference for Radio Observations
Lochner, Michelle; Zwart, Jonathan T L; Smirnov, Oleg; Bassett, Bruce A; Oozeer, Nadeem; Kunz, Martin
2015-01-01
(Abridged) New telescopes like the Square Kilometre Array (SKA) will push into a new sensitivity regime and expose systematics, such as direction-dependent effects, that could previously be ignored. Current methods for handling such systematics rely on alternating best estimates of instrumental calibration and models of the underlying sky, which can lead to inaccurate uncertainty estimates and biased results because such methods ignore any correlations between parameters. These deconvolution algorithms produce a single image that is assumed to be a true representation of the sky, when in fact it is just one realisation of an infinite ensemble of images compatible with the noise in the data. In contrast, here we report a Bayesian formalism that simultaneously infers both systematics and science. Our technique, Bayesian Inference for Radio Observations (BIRO), determines all parameters directly from the raw data, bypassing image-making entirely, by sampling from the joint posterior probability distribution. Thi...
Directory of Open Access Journals (Sweden)
L.S. Vismara
2007-12-01
Full Text Available A dinâmica da população de plantas daninhas pode ser representada por um sistema de equações que relaciona as densidades de sementes produzidas e de plântulas em áreas de cultivo. Os valores dos parâmetros dos modelos podem ser inferidos diretamente de experimentação e análise estatística ou extraídos da literatura. O presente trabalho teve por objetivo estimar os parâmetros do modelo de densidade populacional de plantas daninhas, a partir de um experimento conduzido na área experimental da Embrapa Milho e Sorgo, Sete Lagoas, MG, via os procedimentos de inferências clássica e Bayesiana.Dynamics of weed populations can be described as a system of equations relating the produced seed and seedling densities in crop areas. The model parameter values can be either directly inferred from experimentation and statistical analysis or obtained from the literature. The objective of this work was to estimate the weed population density model parameters based on experimental field data at Embrapa Milho e Sorgo, Sete Lagoas, MG, using classic and Bayesian inferences.
A Bayesian Nonparametric IRT Model
Karabatsos, George
2015-01-01
This paper introduces a flexible Bayesian nonparametric Item Response Theory (IRT) model, which applies to dichotomous or polytomous item responses, and which can apply to either unidimensional or multidimensional scaling. This is an infinite-mixture IRT model, with person ability and item difficulty parameters, and with a random intercept parameter that is assigned a mixing distribution, with mixing weights a probit function of other person and item parameters. As a result of its flexibility...
Elements of Bayesian experimental design
Energy Technology Data Exchange (ETDEWEB)
Sivia, D.S. [Rutherford Appleton Lab., Oxon (United Kingdom)
1997-09-01
We consider some elements of the Bayesian approach that are important for optimal experimental design. While the underlying principles used are very general, and are explained in detail in a recent tutorial text, they are applied here to the specific case of characterising the inferential value of different resolution peakshapes. This particular issue was considered earlier by Silver, Sivia and Pynn (1989, 1990a, 1990b), and the following presentation confirms and extends the conclusions of their analysis.
Bayesian kinematic earthquake source models
Minson, S. E.; Simons, M.; Beck, J. L.; Genrich, J. F.; Galetzka, J. E.; Chowdhury, F.; Owen, S. E.; Webb, F.; Comte, D.; Glass, B.; Leiva, C.; Ortega, F. H.
2009-12-01
Most coseismic, postseismic, and interseismic slip models are based on highly regularized optimizations which yield one solution which satisfies the data given a particular set of regularizing constraints. This regularization hampers our ability to answer basic questions such as whether seismic and aseismic slip overlap or instead rupture separate portions of the fault zone. We present a Bayesian methodology for generating kinematic earthquake source models with a focus on large subduction zone earthquakes. Unlike classical optimization approaches, Bayesian techniques sample the ensemble of all acceptable models presented as an a posteriori probability density function (PDF), and thus we can explore the entire solution space to determine, for example, which model parameters are well determined and which are not, or what is the likelihood that two slip distributions overlap in space. Bayesian sampling also has the advantage that all a priori knowledge of the source process can be used to mold the a posteriori ensemble of models. Although very powerful, Bayesian methods have up to now been of limited use in geophysical modeling because they are only computationally feasible for problems with a small number of free parameters due to what is called the "curse of dimensionality." However, our methodology can successfully sample solution spaces of many hundreds of parameters, which is sufficient to produce finite fault kinematic earthquake models. Our algorithm is a modification of the tempered Markov chain Monte Carlo (tempered MCMC or TMCMC) method. In our algorithm, we sample a "tempered" a posteriori PDF using many MCMC simulations running in parallel and evolutionary computation in which models which fit the data poorly are preferentially eliminated in favor of models which better predict the data. We present results for both synthetic test problems as well as for the 2007 Mw 7.8 Tocopilla, Chile earthquake, the latter of which is constrained by InSAR, local high
Bayesian Stable Isotope Mixing Models
Parnell, Andrew C.; Phillips, Donald L.; Bearhop, Stuart; Semmens, Brice X.; Ward, Eric J.; Moore, Jonathan W.; Andrew L Jackson; Inger, Richard
2012-01-01
In this paper we review recent advances in Stable Isotope Mixing Models (SIMMs) and place them into an over-arching Bayesian statistical framework which allows for several useful extensions. SIMMs are used to quantify the proportional contributions of various sources to a mixture. The most widely used application is quantifying the diet of organisms based on the food sources they have been observed to consume. At the centre of the multivariate statistical model we propose is a compositional m...
Bayesian Network--Response Regression
WANG, LU; Durante, Daniele; Dunson, David B.
2016-01-01
There is an increasing interest in learning how human brain networks vary with continuous traits (e.g., personality, cognitive abilities, neurological disorders), but flexible procedures to accomplish this goal are limited. We develop a Bayesian semiparametric model, which combines low-rank factorizations and Gaussian process priors to allow flexible shifts of the conditional expectation for a network-valued random variable across the feature space, while including subject-specific random eff...
Bayesian segmentation of hyperspectral images
Mohammadpour, Adel; Mohammad-Djafari, Ali
2007-01-01
In this paper we consider the problem of joint segmentation of hyperspectral images in the Bayesian framework. The proposed approach is based on a Hidden Markov Modeling (HMM) of the images with common segmentation, or equivalently with common hidden classification label variables which is modeled by a Potts Markov Random Field. We introduce an appropriate Markov Chain Monte Carlo (MCMC) algorithm to implement the method and show some simulation results.
Bayesian segmentation of hyperspectral images
Mohammadpour, Adel; Féron, Olivier; Mohammad-Djafari, Ali
2004-11-01
In this paper we consider the problem of joint segmentation of hyperspectral images in the Bayesian framework. The proposed approach is based on a Hidden Markov Modeling (HMM) of the images with common segmentation, or equivalently with common hidden classification label variables which is modeled by a Potts Markov Random Field. We introduce an appropriate Markov Chain Monte Carlo (MCMC) algorithm to implement the method and show some simulation results.
Bayesian analysis of contingency tables
Gómez Villegas, Miguel A.; González Pérez, Beatriz
2005-01-01
The display of the data by means of contingency tables is used in different approaches to statistical inference, for example, to broach the test of homogeneity of independent multinomial distributions. We develop a Bayesian procedure to test simple null hypotheses versus bilateral alternatives in contingency tables. Given independent samples of two binomial distributions and taking a mixed prior distribution, we calculate the posterior probability that the proportion of successes in the first...
Bayesian estimation of turbulent motion
Héas, P.; Herzet, C.; Mémin, E.; Heitz, D.; P. D. Mininni
2013-01-01
International audience Based on physical laws describing the multi-scale structure of turbulent flows, this article proposes a regularizer for fluid motion estimation from an image sequence. Regularization is achieved by imposing some scale invariance property between histograms of motion increments computed at different scales. By reformulating this problem from a Bayesian perspective, an algorithm is proposed to jointly estimate motion, regularization hyper-parameters, and to select the ...
Bayesian Kernel Mixtures for Counts
Canale, Antonio; David B Dunson
2011-01-01
Although Bayesian nonparametric mixture models for continuous data are well developed, there is a limited literature on related approaches for count data. A common strategy is to use a mixture of Poissons, which unfortunately is quite restrictive in not accounting for distributions having variance less than the mean. Other approaches include mixing multinomials, which requires finite support, and using a Dirichlet process prior with a Poisson base measure, which does not allow smooth deviatio...
Space Shuttle RTOS Bayesian Network
Morris, A. Terry; Beling, Peter A.
2001-01-01
With shrinking budgets and the requirements to increase reliability and operational life of the existing orbiter fleet, NASA has proposed various upgrades for the Space Shuttle that are consistent with national space policy. The cockpit avionics upgrade (CAU), a high priority item, has been selected as the next major upgrade. The primary functions of cockpit avionics include flight control, guidance and navigation, communication, and orbiter landing support. Secondary functions include the provision of operational services for non-avionics systems such as data handling for the payloads and caution and warning alerts to the crew. Recently, a process to selection the optimal commercial-off-the-shelf (COTS) real-time operating system (RTOS) for the CAU was conducted by United Space Alliance (USA) Corporation, which is a joint venture between Boeing and Lockheed Martin, the prime contractor for space shuttle operations. In order to independently assess the RTOS selection, NASA has used the Bayesian network-based scoring methodology described in this paper. Our two-stage methodology addresses the issue of RTOS acceptability by incorporating functional, performance and non-functional software measures related to reliability, interoperability, certifiability, efficiency, correctness, business, legal, product history, cost and life cycle. The first stage of the methodology involves obtaining scores for the various measures using a Bayesian network. The Bayesian network incorporates the causal relationships between the various and often competing measures of interest while also assisting the inherently complex decision analysis process with its ability to reason under uncertainty. The structure and selection of prior probabilities for the network is extracted from experts in the field of real-time operating systems. Scores for the various measures are computed using Bayesian probability. In the second stage, multi-criteria trade-off analyses are performed between the scores
Bayesian second law of thermodynamics.
Bartolotta, Anthony; Carroll, Sean M; Leichenauer, Stefan; Pollack, Jason
2016-08-01
We derive a generalization of the second law of thermodynamics that uses Bayesian updates to explicitly incorporate the effects of a measurement of a system at some point in its evolution. By allowing an experimenter's knowledge to be updated by the measurement process, this formulation resolves a tension between the fact that the entropy of a statistical system can sometimes fluctuate downward and the information-theoretic idea that knowledge of a stochastically evolving system degrades over time. The Bayesian second law can be written as ΔH(ρ_{m},ρ)+〈Q〉_{F|m}≥0, where ΔH(ρ_{m},ρ) is the change in the cross entropy between the original phase-space probability distribution ρ and the measurement-updated distribution ρ_{m} and 〈Q〉_{F|m} is the expectation value of a generalized heat flow out of the system. We also derive refined versions of the second law that bound the entropy increase from below by a non-negative number, as well as Bayesian versions of integral fluctuation theorems. We demonstrate the formalism using simple analytical and numerical examples. PMID:27627241
Bayesian second law of thermodynamics
Bartolotta, Anthony; Carroll, Sean M.; Leichenauer, Stefan; Pollack, Jason
2016-08-01
We derive a generalization of the second law of thermodynamics that uses Bayesian updates to explicitly incorporate the effects of a measurement of a system at some point in its evolution. By allowing an experimenter's knowledge to be updated by the measurement process, this formulation resolves a tension between the fact that the entropy of a statistical system can sometimes fluctuate downward and the information-theoretic idea that knowledge of a stochastically evolving system degrades over time. The Bayesian second law can be written as Δ H (ρm,ρ ) + F |m≥0 , where Δ H (ρm,ρ ) is the change in the cross entropy between the original phase-space probability distribution ρ and the measurement-updated distribution ρm and F |m is the expectation value of a generalized heat flow out of the system. We also derive refined versions of the second law that bound the entropy increase from below by a non-negative number, as well as Bayesian versions of integral fluctuation theorems. We demonstrate the formalism using simple analytical and numerical examples.
Performance-complexity tradeoff in sequential decoding for the unconstrained AWGN channel
Abediseid, Walid
2013-06-01
In this paper, the performance limits and the computational complexity of the lattice sequential decoder are analyzed for the unconstrained additive white Gaussian noise channel. The performance analysis available in the literature for such a channel has been studied only under the use of the minimum Euclidean distance decoder that is commonly referred to as the lattice decoder. Lattice decoders based on solutions to the NP-hard closest vector problem are very complex to implement, and the search for low complexity receivers for the detection of lattice codes is considered a challenging problem. However, the low computational complexity advantage that sequential decoding promises, makes it an alternative solution to the lattice decoder. In this work, we characterize the performance and complexity tradeoff via the error exponent and the decoding complexity, respectively, of such a decoder as a function of the decoding parameter - the bias term. For the above channel, we derive the cut-off volume-to-noise ratio that is required to achieve a good error performance with low decoding complexity. © 2013 IEEE.
STACK DECODING OF LINEAR BLOCK CODES FOR DISCRETE MEMORYLESS CHANNEL USING TREE DIAGRAM
Directory of Open Access Journals (Sweden)
H. Prashantha Kumar
2012-03-01
Full Text Available The boundaries between block and convolutional codes have become diffused after recent advances in the understanding of the trellis structure of block codes and the tail-biting structure of some convolutional codes. Therefore, decoding algorithms traditionally proposed for decoding convolutional codes have been applied for decoding certain classes of block codes. This paper presents the decoding of block codes using tree structure. Many good block codes are presently known. Several of them have been used in applications ranging from deep space communication to error control in storage systems. But the primary difficulty with applying Viterbi or BCJR algorithms to decode of block codes is that, even though they are optimum decoding methods, the promised bit error rates are not achieved in practice at data rates close to capacity. This is because the decoding effort is fixed and grows with block length, and thus only short block length codes can be used. Therefore, an important practical question is whether a suboptimal realizable soft decision decoding method can be found for block codes. A noteworthy result which provides a partial answer to this question is described in the following sections. This result of near optimum decoding will be used as motivation for the investigation of different soft decision decoding methods for linear block codes which can lead to the development of efficient decoding algorithms. The code tree can be treated as an expanded version of the trellis, where every path is totally distinct from every other path. We have derived the tree structure for (8, 4 and (16, 11 extended Hamming codes and have succeeded in implementing the soft decision stack algorithm to decode them. For the discrete memoryless channel, gains in excess of 1.5dB at a bit error rate of 10-5 with respect to conventional hard decision decoding are demonstrated for these codes.
Molecular decoding using luminescence from an entangled porous framework
Takashima, Yohei; Martínez, Virginia Martínez; Furukawa, Shuhei; Kondo, Mio; Shimomura, Satoru; Uehara, Hiromitsu; Nakahama, Masashi; Sugimoto, Kunihisa; Kitagawa, Susumu
2011-01-01
Chemosensors detect a single target molecule from among several molecules, but cannot differentiate targets from one another. In this study, we report a molecular decoding strategy in which a single host domain accommodates a class of molecules and distinguishes between them with a corresponding readout. We synthesized the decoding host by embedding naphthalenediimide into the scaffold of an entangled porous framework that exhibited structural dynamics due to the dislocation of two chemically non-interconnected frameworks. An intense turn-on emission was observed on incorporation of a class of aromatic compounds, and the resulting luminescent colour was dependent on the chemical substituent of the aromatic guest. This unprecedented chemoresponsive, multicolour luminescence originates from an enhanced naphthalenediimide–aromatic guest interaction because of the induced-fit structural transformation of the entangled framework. We demonstrate that the cooperative structural transition in mesoscopic crystal domains results in a nonlinear sensor response to the guest concentration. PMID:21266971
Molecular decoding using luminescence from an entangled porous framework.
Takashima, Yohei; Martínez, Virginia Martínez; Furukawa, Shuhei; Kondo, Mio; Shimomura, Satoru; Uehara, Hiromitsu; Nakahama, Masashi; Sugimoto, Kunihisa; Kitagawa, Susumu
2011-01-25
Chemosensors detect a single target molecule from among several molecules, but cannot differentiate targets from one another. In this study, we report a molecular decoding strategy in which a single host domain accommodates a class of molecules and distinguishes between them with a corresponding readout. We synthesized the decoding host by embedding naphthalenediimide into the scaffold of an entangled porous framework that exhibited structural dynamics due to the dislocation of two chemically non-interconnected frameworks. An intense turn-on emission was observed on incorporation of a class of aromatic compounds, and the resulting luminescent colour was dependent on the chemical substituent of the aromatic guest. This unprecedented chemoresponsive, multicolour luminescence originates from an enhanced naphthalenediimide-aromatic guest interaction because of the induced-fit structural transformation of the entangled framework. We demonstrate that the cooperative structural transition in mesoscopic crystal domains results in a nonlinear sensor response to the guest concentration. PMID:21266971
HDL Implementation of Low Density Parity Check (LDPC Decoder
Directory of Open Access Journals (Sweden)
Pawandip Kaur
2012-03-01
Full Text Available Low-Density Parity-Check (LDPC codes are one of the most promising error-correcting codes approaching Shannon capacity and have been adopted in many applications. These codes offer huge advantages in terms of coding gain, throughput and power dissipation. Error correction algorithms are often implemented in hardware for fast processing to meet the real-time needs of communication systems. However hardwareimplementation of LDPC decoders using traditional Hardware Description Language (HDL based approach is a complex and time consuming task. In this paper HDL Implementation of Low Density Parity Check Decoder architecture is presented with different rates i.e. 1/2, 2/3, 3/4, 4/7, 8/9, 9/10 and variable data lengths i.e. 8, 16, 32, 64, 128, 256 bits and consequent changeable precision factor.
Two-Bit Bit Flipping Decoding of LDPC Codes
Nguyen, Dung Viet; Marcellin, Michael W
2011-01-01
In this paper, we propose a new class of bit flipping algorithms for low-density parity-check (LDPC) codes over the binary symmetric channel (BSC). Compared to the regular (parallel or serial) bit flipping algorithms, the proposed algorithms employ one additional bit at a variable node to represent its "strength." The introduction of this additional bit increases the guaranteed error correction capability by a factor of at least 2. An additional bit can also be employed at a check node to capture information which is beneficial to decoding. A framework for failure analysis of the proposed algorithms is described. These algorithms outperform the Gallager A/B algorithm and the min-sum algorithm at much lower complexity. Concatenation of two-bit bit flipping algorithms show a potential to approach the performance of belief propagation (BP) decoding in the error floor region, also at lower complexity.
Video watermarking with empirical PCA-based decoding.
Khalilian, Hanieh; Bajic, Ivan V
2013-12-01
A new method for video watermarking is presented in this paper. In the proposed method, data are embedded in the LL subband of wavelet coefficients, and decoding is performed based on the comparison among the elements of the first principal component resulting from empirical principal component analysis (PCA). The locations for data embedding are selected such that they offer the most robust PCA-based decoding. Data are inserted in the LL subband in an adaptive manner based on the energy of high frequency subbands and visual saliency. Extensive testing was performed under various types of attacks, such as spatial attacks (uniform and Gaussian noise and median filtering), compression attacks (MPEG-2, H. 263, and H. 264), and temporal attacks (frame repetition, frame averaging, frame swapping, and frame rate conversion). The results show that the proposed method offers improved performance compared with several methods from the literature, especially under additive noise and compression attacks.
Slack-Decode Simultaneously and Redundantly Threaded Architecture
Institute of Scientific and Technical Information of China (English)
YANG Hua; CUI Gang; LIU Hong-wei; YANG Xiao-zong
2005-01-01
Slack-Decode Simultaneously and Redundantly Threaded(SD-SRT) is proposed for detecting transient faults in processors. SD-SRT boosts the previously proposed SRT performance via definitely eliminating redundant instruction fetches. First, the fetch stage is moved out of the Spheres of Replication (SoR), and a unified instruction-fetch-queue(IFQ) is exploited by both the leading and trailing threads.Second, a scheme called slack-decode cooperates with the unified IFQ to harmonize proceeding of the two threads. The simulations show that SD-SRT outperforms original SRT in terms of IPC by 15%, and decreases I-cache access by42%. Meanwhile, SD-SRT leads to a lessened size and complexity for hardware structures such as load-value-queue and store-buffer.
Directory of Open Access Journals (Sweden)
Marcos Rodovalho
2014-11-01
Full Text Available The objective of this study was to examine genetic parameters of popping expansion and grain yield in a trial of 169 halfsib families using a Bayesian approach. The independence chain algorithm with informative priors for the components of residual and family variance (inverse-gamma prior distribution was used. Popping expansion was found to be moderately heritable, with a posterior mode of h2 of 0.34, and 90% Bayesian confidence interval of 0.22 to 0.44. The heritability of grain yield (family level was moderate (h2 = 0.4 with Bayesian confidence interval of 0.28 to 0.49. The target population contains sufficient genetic variability for subsequent breeding cycles, and the Bayesian approach is a useful alternative for scientific inference in the genetic evaluation of popcorn.
12th Brazilian Meeting on Bayesian Statistics
Louzada, Francisco; Rifo, Laura; Stern, Julio; Lauretto, Marcelo
2015-01-01
Through refereed papers, this volume focuses on the foundations of the Bayesian paradigm; their comparison to objectivistic or frequentist Statistics counterparts; and the appropriate application of Bayesian foundations. This research in Bayesian Statistics is applicable to data analysis in biostatistics, clinical trials, law, engineering, and the social sciences. EBEB, the Brazilian Meeting on Bayesian Statistics, is held every two years by the ISBrA, the International Society for Bayesian Analysis, one of the most active chapters of the ISBA. The 12th meeting took place March 10-14, 2014 in Atibaia. Interest in foundations of inductive Statistics has grown recently in accordance with the increasing availability of Bayesian methodological alternatives. Scientists need to deal with the ever more difficult choice of the optimal method to apply to their problem. This volume shows how Bayes can be the answer. The examination and discussion on the foundations work towards the goal of proper application of Bayesia...
LOW POWER SOFTWARE HEVC DECODER DEMO FOR MOBILE DEVICES
Nogues, Erwan; Lacour, Morgan; Raffin, Erwan; Pelcat, Maxime; Menard, Daniel
2015-01-01
Demo session International audience Video on mobile devices is a must-have feature with the prominence of new services and applications using video like streaming or conferencing. By improving the compression performance by a factor of two with a similar quality, the new video standard High Efficiency Video Coding (HEVC) is an appealing technology for service providers. Besides, with the recent progress of Systems-on-Chip (SoC) for mobile devices, software video decoders is now a realit...
Iterative Equalization and Source Decoding for Vector Quantized Sources
Yang, L-L.; Wang, J.; Maunder, R.G.; Hanzo, L
2006-01-01
In this contribution an iterative (turbo) channel equalization and source decoding scheme is considered. In our investigations the source is modelled as a Gaussian-Markov source, which is compressed with the aid of vector quantization. The communications channel is modelled as a time-invariant channel contaminated by intersymbol interference (ISI). Since the ISI channel can be viewed as a rate-1 encoder and since the redundancy of the source cannot be perfectly removed by source encoding, a j...
An LDPC Decoder Architecture for Wireless Sensor Network Applications
Guido Masera; Andrea Dario Giancarlo Biroli; Maurizio Martina
2012-01-01
The pervasive use of wireless sensors in a growing spectrum of human activities reinforces the need for devices with low energy dissipation. In this work, coded communication between a couple of wireless sensor devices is considered as a method to reduce the dissipated energy per transmitted bit with respect to uncoded communication. Different Low Density Parity Check (LDPC) codes are considered to this purpose and post layout results are shown for a low-area low-energy decoder, which offers ...
Comparing offline decoding performance in physiologically defined neuronal classes
Best, Matthew D.; Takahashi, Kazutaka; Suminski, Aaron J; Ethier, Christian; Miller, Lee E.; Hatsopoulos, Nicholas G.
2016-01-01
Objective Recently, several studies have documented the presence of a bimodal distribution of spike waveform widths in primary motor cortex. Although narrow and wide spiking neurons, corresponding to the two modes of the distribution, exhibit different response properties, it remains unknown if these differences give rise to differential decoding performance between these two classes of cells. Approach We used a Gaussian mixture model to classify neurons into narrow and wide physiological cla...
Coding Opportunity Densification Strategies for Instantly Decodable Network Coding
Sorour, Sameh; Valaee, Shahrokh
2012-01-01
In this paper, we aim to identify the strategies that can maximize and monotonically increase the density of the coding opportunities in instantly decodable network coding (IDNC).Using the well-known graph representation of IDNC, first derive an expression for the exact evolution of the edge set size after the transmission of any arbitrary coded packet. From the derived expressions, we show that sending commonly wanted packets for all the receivers can maximize the number of coding opportunit...
Design of LOG-MAP / MAX-LOG-MAP Decoder
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Mihai TIMIS, PhD Candidate, Dipl.Eng.
2007-01-01
Full Text Available The process of turbo-code decoding starts with the formation of a posteriori probabilities (APPs for each data bit, which is followed by choosing the data-bit value that corresponds to the maximum a posteriori (MAP probability for that data bit. Upon reception of a corrupted code-bit sequence, the process of decision making with APPs allows the MAP algorithm to determine the most likely information bit to have been transmitted at each bit time.
Fast brain decoding with random sampling and random projections
Hoyos-Idrobo, Andrés; Varoquaux, Gaël; Thirion, Bertrand
2016-01-01
Machine learning from brain images is a central tool for image-based diagnosis and diseases characterization. Predicting behavior from functional imaging, brain decoding, analyzes brain activity in terms of the behavior that it implies. While these multivariate techniques are becoming standard brain mapping tools, like mass-univariate analysis, they entail much larger computational costs. In an time of growing data sizes, with larger cohorts and higher-resolutions imaging, this cost is increa...
Social-sparsity brain decoders: faster spatial sparsity
Varoquaux, Gaël; Kowalski, Matthieu; Thirion, Bertrand
2016-01-01
Spatially-sparse predictors are good models for brain decoding: they give accurate predictions and their weight maps are interpretable as they focus on a small number of regions. However, the state of the art, based on total variation or graph-net, is computationally costly. Here we introduce sparsity in the local neighborhood of each voxel with social-sparsity, a structured shrinkage operator. We find that, on brain imaging classification problems, social-sparsity performs almost as well as ...
Decoding the visual and subjective contents of the human brain
Kamitani, Yukiyasu; Tong, Frank
2005-01-01
The potential for human neuroimaging to read-out the detailed contents of a person’s mental state has yet to be fully explored. We investigated whether the perception of edge orientation, a fundamental visual feature, can be decoded from human brain activity measured with functional magnetic resonance imaging (fMRI). Using statistical algorithms to classify brain states, we found that ensemble fMRI signals in early visual areas could reliably predict on individual trials which of eight stimul...
Decoding billions of integers per second through vectorization
Lemire, Daniel; Boytsov, Leonid
2012-01-01
In many important applications -- such as search engines and relational database systems -- data is stored in the form of arrays of integers. Encoding and, most importantly, decoding of these arrays consumes considerable CPU time. Therefore, substantial effort has been made to reduce costs associated with compression and decompression. In particular, researchers have exploited the superscalar nature of modern processors and SIMD instructions. Nevertheless, we introduce a novel vectorized sche...
LDPC Decoding for Signal Dependent Visible Light Communication Channels
Institute of Scientific and Technical Information of China (English)
YUAN Ming; SHA Xiaoshi; LIANG Xiao; JIANG Ming; WANG Jiaheng; ZHAO Chunming
2016-01-01
Avalanche photodiodes (APD) are widely employed in visible light communication (VLC) systems. The general signal dependent Gaussian channel is investigated. Experiment results reveal that symbols on different constellation points under official illumi⁃nance inevitably suffer from different levels of noise due to the multiplication process of APDs. In such a case, conventional log likely⁃hood ratio (LLR) calculation for signal independent channels may cause performance loss. The optimal LLR calculation for decoder is then derived because of the existence of non⁃ignorable APD shot noise. To find the decoding thresholds of the optimal and suboptimal detection schemes, the extrinsic information transfer (EXIT) chat is further analyzed. Finally a modified minimum sum algorithm is suggested with reduced complexity and acceptable performance loss. Numerical simulations show that, with a reg⁃ular (3, 6) low⁃density parity check (LDPC) code of block length 20,000, 0.7 dB gain is achieved with our proposed scheme over the LDPC decoder designed for signal independent noise. It is also found that the coding performance is improved for a larger modulation depth.
Compiling Relational Bayesian Networks for Exact Inference
DEFF Research Database (Denmark)
Jaeger, Manfred; Chavira, Mark; Darwiche, Adnan
2004-01-01
We describe a system for exact inference with relational Bayesian networks as defined in the publicly available \\primula\\ tool. The system is based on compiling propositional instances of relational Bayesian networks into arithmetic circuits and then performing online inference by evaluating...... and differentiating these circuits in time linear in their size. We report on experimental results showing the successful compilation, and efficient inference, on relational Bayesian networks whose {\\primula}--generated propositional instances have thousands of variables, and whose jointrees have clusters...
Bayesian Posterior Distributions Without Markov Chains
Cole, Stephen R.; Chu, Haitao; Greenland, Sander; Hamra, Ghassan; Richardson, David B.
2012-01-01
Bayesian posterior parameter distributions are often simulated using Markov chain Monte Carlo (MCMC) methods. However, MCMC methods are not always necessary and do not help the uninitiated understand Bayesian inference. As a bridge to understanding Bayesian inference, the authors illustrate a transparent rejection sampling method. In example 1, they illustrate rejection sampling using 36 cases and 198 controls from a case-control study (1976–1983) assessing the relation between residential ex...
Cameron, Ewan
2010-01-01
We present a critical review of popular techniques for estimating confidence intervals on binomial population proportions inferred from success counts in small-to-intermediate samples. Population proportions arise frequently as quantities of interest in astronomical research; most notably in studies of the fractions of galaxies exhibiting distinct structural components (stellar bars, supermassive blackholes, AGN, etc.), populating the ('quiescent') red sequence, or undergoing major/minor mergers. However, the two most widely-used techniques for estimating binomial confidence intervals - the 'normal approximation' and the Clopper & Pearson approach - perform poorly under sampling regimes routinely encountered in astronomical datasets. Hence, we provide here an overview of the fundamentals of binomial statistics with two principal aims: (i) to reveal the ease with which binomial confidence intervals with more satisfactory behaviour may be estimated from the quantiles of the beta distribution using modern ma...
Unsupervised Transient Light Curve Analysis Via Hierarchical Bayesian Inference
Sanders, Nathan; Soderberg, Alicia
2014-01-01
Historically, light curve studies of supernovae (SNe) and other transient classes have focused on individual objects with copious and high signal-to-noise observations. In the nascent era of wide field transient searches, objects with detailed observations are decreasing as a fraction of the overall known SN population, and this strategy sacrifices the majority of the information contained in the data about the underlying population of transients. A population level modeling approach, simultaneously fitting all available observations of objects in a transient sub-class of interest, fully mines the data to infer the properties of the population and avoids certain systematic biases. We present a novel hierarchical Bayesian statistical model for population level modeling of transient light curves, and discuss its implementation using an efficient Hamiltonian Monte Carlo technique. As a test case, we apply this model to the Type IIP SN sample from the Pan-STARRS1 Medium Deep Survey, consisting of 18,837 photometr...
Photometric Redshift with Bayesian Priors on Physical Properties of Galaxies
Tanaka, Masayuki
2015-01-01
We present a proof-of-concept analysis of photometric redshifts with Bayesian priors on physical properties of galaxies. This concept is particularly suited for upcoming/on-going large imaging surveys, in which only several broad-band filters are available and it is hard to break some of the degeneracies in the multi-color space. We construct model templates of galaxies using a stellar population synthesis code and apply Bayesian priors on physical properties such as stellar mass and star formation rate. These priors are a function of redshift and they effectively evolve the templates with time in an observationally motivated way. We demonstrate that the priors help reduce the degeneracy and deliver significantly improved photometric redshifts. Furthermore, we show that a template error function, which corrects for systematic flux errors in the model templates as a function of rest-frame wavelength, delivers further improvements. One great advantage of our technique is that we simultaneously measure redshifts...
A Bayesian nonlinear mixed-effects disease progression model
Kim, Seongho; Jang, Hyejeong; Wu, Dongfeng; Abrams, Judith
2016-01-01
A nonlinear mixed-effects approach is developed for disease progression models that incorporate variation in age in a Bayesian framework. We further generalize the probability model for sensitivity to depend on age at diagnosis, time spent in the preclinical state and sojourn time. The developed models are then applied to the Johns Hopkins Lung Project data and the Health Insurance Plan for Greater New York data using Bayesian Markov chain Monte Carlo and are compared with the estimation method that does not consider random-effects from age. Using the developed models, we obtain not only age-specific individual-level distributions, but also population-level distributions of sensitivity, sojourn time and transition probability. PMID:26798562
Design and Implementation of Single Chip WCDMA High Speed Channel Decoder
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
A memory and driving clock efficient design scheme to achieve WCDMA high-speed channel decoder on a single XILINX' XVC1000E FPGA chip is presented. Using a modified MAP algorithm, say parallel Sliding Window logarithmic Maximum A Posterior (PSW-log-MAP), the on-chip turbo decoder can decode an information bit by only an average of two clocks per iteration. On the other hand, a high-parallel pipeline Viterbi algorithm is adopted to realize the 256-state convolutional code decoding. The final decoder with an 8×chip-clock (30.72MHz) driving can concurrently process a data rate up to 2.5Mbps of turbo coded sequences and a data rate over 400kbps of convolutional codes. There is no extern memory needed. Test results show that the decoding performance is only 0.2～0.3dB or less lost comparing to float simulation.
Serial Min-max Decoding Algorithm Based on Variable Weighting for Nonbinary LDPC Codes
Directory of Open Access Journals (Sweden)
Zhongxun Wang
2013-09-01
Full Text Available In this paper, we perform an analysis on the min-max decoding algorithm for nonbinary LDPC(low-density parity-check codes and propose serial min-max decoding algorithm. Combining with the weighted processing of the variable node message, we propose serial min-max decoding algorithm based on variable weighting for nonbinary LDPC codes in the end. The simulation indicates that when the bit error rate is 10^-3,compared with serial min-max decoding algorithm ,traditional min-max decoding algorithm and traditional minsum algorithm ,serial min-max decoding algorithm based on variable weighting can offer additional coding gain 0.2dB、0.8dB and 1.4dB respectively in additional white Gaussian noise channel and under binary phase shift keying modulation.
Elimination of the Background Noise of the Decoded Image in Fresnel Zone Plate Scanning Holography
Institute of Scientific and Technical Information of China (English)
无
2005-01-01
A method of digitally high pass filtering in frequency domain is proposed to eliminate the background noise of the decoded image in Fresnel zone plate scanning holography. The high pass filter is designed as a circular stop, which should be suitable to suppressing the background noise significantly and remain much low frequency information of the object. The principle of high pass filtering is that the Fourier transform of the decoded image is multiplied with the high pass filter. Thus the frequency spectrum of the decoded image without the background noise is achieved. By inverse Fourier transform of the spectrum of the decoded image after multiplying operation, the decoded image without the background noise is obtained. Both of the computer simulations and the experimental results show that the contrast and the signal-to-noise ratio of the decoded image are significantly improved with digital filtering.
Extension of Welch-Berlekamp theorem and universal strategy of decoding algorithm beyond BCH bound
Institute of Scientific and Technical Information of China (English)
忻鼎稼
1995-01-01
In consideration of decoding cydic codes, a general formula for error pattern prediction is obtained. It extends the Welch-Berlekamp theorem and expands the Welch-Berlekamp algorithm, so that the limitation from BCH bound is broken, and the algorithm turns into a universal one which meets the actual minimum distance as well as complete decoding beyond the minimum distance in the case of decoding BCH codes and general cydic codes.
Tree expectation propagation for ml decoding of LDPC codes over the BEC
Salamanca Mino, Luis; Olmos, P. M.; Murillo-Fuentes, J. J.; Perez-Cruz, F
2013-01-01
We propose a decoding algorithm for LDPC codes that achieves the maximum likelihood (ML) solution over the bi- nary erasure channel (BEC). In this channel, the tree-structured expectation propagation (TEP) decoder improves the peeling decoder (PD) by processing check nodes of degree one and two. However, it does not achieve the ML solution, as the tree structure of the TEP allows only for approximate inference. In this paper, we provide the procedure to construct the structure needed for exac...
RVC-CAL dataflow implementations of MPEG AVC/H.264 CABAC decoding
Bezati, Endri; Mattavelli, Marco; Raulet, Mickael
2010-01-01
International audience This paper describes the implementation of the MPEG AVC CABAC entropy decoder using the RVC-CAL dataflow programming language. CABAC is the Context based Adaptive Binary Arithmetic Coding entropy decoder that is used by the MPEG AVC/H.264 main and high profile video standard. CABAC algorithm provides increased compression efficiency, however presents a higher complexity compared to other entropy coding algorithms. This implementation of the CABAC entropy decoder usin...
Analysis of the Convergence Process by EXIT Charts for Parallel Implementations of Turbo Decoders
Sanchez, O.; Jego, Christophe; JEZEQUEL, Michel
2013-01-01
International audience Iterative process is a general principle in decoding powerful FEC codes such as turbo codes. However, the mutual information exchange during the iterative process is not easy to analyze and to describe. A useful technique to help the designer is the EXtrinsic Information Transfer (EXIT) chart. Unfortunately, this method cannot be directly applied to the decoding convergence analysis if parallel processing has to be exploited for the design of turbo decoders. In this ...
A graphical model framework for decoding in the visual ERP-based BCI speller
Martens, S.M.M.; Mooij, J.M.; Hill, N J; Farquhar, J.D.R.; Schölkopf, B.
2011-01-01
We present a graphical model framework for decoding in the visual ERP-based speller system. The proposed framework allows researchers to build generative models from which the decoding rules are obtained in a straightforward manner. We suggest two models for generating brain signals conditioned on the stimulus events. Both models incorporate letter frequency information but assume different dependencies between brain signals and stimulus events. For both models, we derive decoding rules and p...
Bayesian Methods and Universal Darwinism
Campbell, John
2009-12-01
Bayesian methods since the time of Laplace have been understood by their practitioners as closely aligned to the scientific method. Indeed a recent Champion of Bayesian methods, E. T. Jaynes, titled his textbook on the subject Probability Theory: the Logic of Science. Many philosophers of science including Karl Popper and Donald Campbell have interpreted the evolution of Science as a Darwinian process consisting of a `copy with selective retention' algorithm abstracted from Darwin's theory of Natural Selection. Arguments are presented for an isomorphism between Bayesian Methods and Darwinian processes. Universal Darwinism, as the term has been developed by Richard Dawkins, Daniel Dennett and Susan Blackmore, is the collection of scientific theories which explain the creation and evolution of their subject matter as due to the Operation of Darwinian processes. These subject matters span the fields of atomic physics, chemistry, biology and the social sciences. The principle of Maximum Entropy states that Systems will evolve to states of highest entropy subject to the constraints of scientific law. This principle may be inverted to provide illumination as to the nature of scientific law. Our best cosmological theories suggest the universe contained much less complexity during the period shortly after the Big Bang than it does at present. The scientific subject matter of atomic physics, chemistry, biology and the social sciences has been created since that time. An explanation is proposed for the existence of this subject matter as due to the evolution of constraints in the form of adaptations imposed on Maximum Entropy. It is argued these adaptations were discovered and instantiated through the Operations of a succession of Darwinian processes.
An Efficient Soft Decoder of Block Codes Based on Compact Genetic Algorithm
Directory of Open Access Journals (Sweden)
Ahmed Azouaoui
2012-09-01
Full Text Available Soft-decision decoding is an NP-hard problem with great interest to developers of communication systems. We present an efficient soft-decision decoder of linear block codes based on compact genetic algorithm (cGA and compare its performances with various other decoding algorithms including Shakeel algorithm. The proposed algorithm uses the dual code in contrast to Shakeel algorithm which uses the code itself. Hence, this new approach reduces the decoding complexity of high rates codes. The complexity and an optimized version of this new algorithm are also presented and discussed.
Sensitivity analysis of the channel estimation deviation to the MAP decoding algorithm
Institute of Scientific and Technical Information of China (English)
WAN Ke; FAN Ping-zhi
2006-01-01
As a necessary input parameter for maximum a-posteriori(MAP) decoding algorithm,SNR is normally obtained from the channel estimation unit.Corresponding research indicated that SNR estimation deviation degraded the performance of Turbo decoding significantly.In this paper,MAP decoding algorithm with SNR estimation deviation was investigated in detail,and the degradation mechanism of Turbo decoding was explained analytically.The theoretical analysis and computer simulation disclosed the specific reasons for the performance degradation when SNR estimation was less than the actual value,and for the higher sensitivity of SNR estimation to long-frame Turbo codes.
Parallel iterative decoding of transform domain Wyner-Ziv video using cross bitplane correlation
DEFF Research Database (Denmark)
Luong, Huynh Van; Huang, Xin; Forchhammer, Søren
2011-01-01
In recent years, Transform Domain Wyner-Ziv (TDWZ) video coding has been proposed as an efficient Distributed Video Coding (DVC) solution, which fully or partly exploits the source statistics at the decoder to reduce the computational burden at the encoder. In this paper, a parallel iterative LDPC...... decoding scheme is proposed to improve the coding efficiency of TDWZ video codecs. The proposed parallel iterative LDPC decoding scheme is able to utilize cross bitplane correlation during decoding, by iteratively refining the soft-input, updating a modeled noise distribution and thereafter enhancing the...
Zhang, Qin; Xiong, Caihua; Chen, Wenbin
2014-01-01
Surface Electromyography (EMG) is popularly used to decode human motion intention for robot movement control. Traditional motion decoding method uses pattern recognition to provide binary control command which can only move the robot as predefined limited patterns. In this work, we proposed a motion decoding method which can accurately estimate 3-dimensional (3-D) continuous upper limb motion only from multi-channel EMG signals. In order to prevent the muscle activities from motion artifacts and muscle crosstalk which especially obviously exist in upper limb motion, the independent component analysis (ICA) was applied to extract the independent source EMG signals. The motion data was also transferred from 4-manifold to 2-manifold by the principle component analysis (PCA). A hidden Markov model (HMM) was proposed to decode the motion from the EMG signals after the model trained by an adaptive model identification process. Experimental data were used to train the decoding model and validate the motion decoding performance. By comparing the decoded motion with the measured motion, it is found that the proposed motion decoding strategy was feasible to decode 3-D continuous motion from EMG signals.
Polymeric Optical Code-Division Multiple-Access (CDMA Encoder and Decoder Modules
Directory of Open Access Journals (Sweden)
Ray T. Chen
2011-09-01
Full Text Available We propose a low cost polymeric optical waveguides-based optical CDMA encoder and decoder modules. The structures of the optical CDMA encoder and decoder modules are presented. The performance of the optical CDMA encoder and decoder modules is simulated using 10-chip binary phase-shift keying (BPSK coding schemes. The optical CDMA encoder and decoder modules can effectively transmit and recover optical CDMA data streams. The SNR of the received signal is analyzed and determined to be primarily from the cross correlation with other channels.
On the Joint Error-and-Erasure Decoding for Irreducible Polynomial Remainder Codes
Yu, Jiun-Hung
2012-01-01
A general class of polynomial remainder codes is considered. Such codes are very flexible in rate and length and include Reed-Solomon codes as a special case. As an extension of previous work, two joint error-and-erasure decoding approaches are proposed. In particular, both the decoding approaches by means of a fixed transform are treated in a way compatible with the error-only decoding. In the end, a collection of gcd-based decoding algorithm is obtained, some of which appear to be new even when specialized to Reed-Solomon codes.
VLSI Architectures for Sliding-Window-Based Space-Time Turbo Trellis Code Decoders
Directory of Open Access Journals (Sweden)
Georgios Passas
2012-01-01
Full Text Available The VLSI implementation of SISO-MAP decoders used for traditional iterative turbo coding has been investigated in the literature. In this paper, a complete architectural model of a space-time turbo code receiver that includes elementary decoders is presented. These architectures are based on newly proposed building blocks such as a recursive add-compare-select-offset (ACSO unit, A-, B-, Γ-, and LLR output calculation modules. Measurements of complexity and decoding delay of several sliding-window-technique-based MAP decoder architectures and a proposed parameter set lead to defining equations and comparison between those architectures.
Numeracy, frequency, and Bayesian reasoning
Directory of Open Access Journals (Sweden)
Gretchen B. Chapman
2009-02-01
Full Text Available Previous research has demonstrated that Bayesian reasoning performance is improved if uncertainty information is presented as natural frequencies rather than single-event probabilities. A questionnaire study of 342 college students replicated this effect but also found that the performance-boosting benefits of the natural frequency presentation occurred primarily for participants who scored high in numeracy. This finding suggests that even comprehension and manipulation of natural frequencies requires a certain threshold of numeracy abilities, and that the beneficial effects of natural frequency presentation may not be as general as previously believed.
Bayesian Query-Focused Summarization
Daumé, Hal
2009-01-01
We present BayeSum (for ``Bayesian summarization''), a model for sentence extraction in query-focused summarization. BayeSum leverages the common case in which multiple documents are relevant to a single query. Using these documents as reinforcement for query terms, BayeSum is not afflicted by the paucity of information in short queries. We show that approximate inference in BayeSum is possible on large data sets and results in a state-of-the-art summarization system. Furthermore, we show how BayeSum can be understood as a justified query expansion technique in the language modeling for IR framework.
Bayesian Sampling using Condition Indicators
DEFF Research Database (Denmark)
Faber, Michael H.; Sørensen, John Dalsgaard
2002-01-01
The problem of control quality of components is considered for the special case where the acceptable failure rate is low, the test costs are high and where it may be difficult or impossible to test the condition of interest directly. Based on the classical control theory and the concept...... of condition indicators introduced by Benjamin and Cornell (1970) a Bayesian approach to quality control is formulated. The formulation is then extended to the case where the quality control is based on sampling of indirect information about the condition of the components, i.e. condition indicators...
Using Bayesian Networks to Improve Knowledge Assessment
Millan, Eva; Descalco, Luis; Castillo, Gladys; Oliveira, Paula; Diogo, Sandra
2013-01-01
In this paper, we describe the integration and evaluation of an existing generic Bayesian student model (GBSM) into an existing computerized testing system within the Mathematics Education Project (PmatE--Projecto Matematica Ensino) of the University of Aveiro. This generic Bayesian student model had been previously evaluated with simulated…
Bayesian analysis of exoplanet and binary orbits
Schulze-Hartung, Tim; Launhardt, Ralf; Henning, Thomas
2012-01-01
We introduce BASE (Bayesian astrometric and spectroscopic exoplanet detection and characterisation tool), a novel program for the combined or separate Bayesian analysis of astrometric and radial-velocity measurements of potential exoplanet hosts and binary stars. The capabilities of BASE are demonstrated using all publicly available data of the binary Mizar A.
Bayesian credible interval construction for Poisson statistics
Institute of Scientific and Technical Information of China (English)
ZHU Yong-Sheng
2008-01-01
The construction of the Bayesian credible (confidence) interval for a Poisson observable including both the signal and background with and without systematic uncertainties is presented.Introducing the conditional probability satisfying the requirement of the background not larger than the observed events to construct the Bayesian credible interval is also discussed.A Fortran routine,BPOCI,has been developed to implement the calculation.
Modeling Diagnostic Assessments with Bayesian Networks
Almond, Russell G.; DiBello, Louis V.; Moulder, Brad; Zapata-Rivera, Juan-Diego
2007-01-01
This paper defines Bayesian network models and examines their applications to IRT-based cognitive diagnostic modeling. These models are especially suited to building inference engines designed to be synchronous with the finer grained student models that arise in skills diagnostic assessment. Aspects of the theory and use of Bayesian network models…
Advances in Bayesian Modeling in Educational Research
Levy, Roy
2016-01-01
In this article, I provide a conceptually oriented overview of Bayesian approaches to statistical inference and contrast them with frequentist approaches that currently dominate conventional practice in educational research. The features and advantages of Bayesian approaches are illustrated with examples spanning several statistical modeling…
Learning dynamic Bayesian networks with mixed variables
DEFF Research Database (Denmark)
Bøttcher, Susanne Gammelgaard
This paper considers dynamic Bayesian networks for discrete and continuous variables. We only treat the case, where the distribution of the variables is conditional Gaussian. We show how to learn the parameters and structure of a dynamic Bayesian network and also how the Markov order can be learned...
The Bayesian Revolution Approaches Psychological Development
Shultz, Thomas R.
2007-01-01
This commentary reviews five articles that apply Bayesian ideas to psychological development, some with psychology experiments, some with computational modeling, and some with both experiments and modeling. The reviewed work extends the current Bayesian revolution into tasks often studied in children, such as causal learning and word learning, and…
Bayesian Network for multiple hypthesis tracking
W.P. Zajdel; B.J.A. Kröse
2002-01-01
For a flexible camera-to-camera tracking of multiple objects we model the objects behavior with a Bayesian network and combine it with the multiple hypohesis framework that associates observations with objects. Bayesian networks offer a possibility to factor complex, joint distributions into a produ
2nd Bayesian Young Statisticians Meeting
Bitto, Angela; Kastner, Gregor; Posekany, Alexandra
2015-01-01
The Second Bayesian Young Statisticians Meeting (BAYSM 2014) and the research presented here facilitate connections among researchers using Bayesian Statistics by providing a forum for the development and exchange of ideas. WU Vienna University of Business and Economics hosted BAYSM 2014 from September 18th to 19th. The guidance of renowned plenary lecturers and senior discussants is a critical part of the meeting and this volume, which follows publication of contributions from BAYSM 2013. The meeting's scientific program reflected the variety of fields in which Bayesian methods are currently employed or could be introduced in the future. Three brilliant keynote lectures by Chris Holmes (University of Oxford), Christian Robert (Université Paris-Dauphine), and Mike West (Duke University), were complemented by 24 plenary talks covering the major topics Dynamic Models, Applications, Bayesian Nonparametrics, Biostatistics, Bayesian Methods in Economics, and Models and Methods, as well as a lively poster session ...
BAYESIAN BICLUSTERING FOR PATIENT STRATIFICATION.
Khakabimamaghani, Sahand; Ester, Martin
2016-01-01
The move from Empirical Medicine towards Personalized Medicine has attracted attention to Stratified Medicine (SM). Some methods are provided in the literature for patient stratification, which is the central task of SM, however, there are still significant open issues. First, it is still unclear if integrating different datatypes will help in detecting disease subtypes more accurately, and, if not, which datatype(s) are most useful for this task. Second, it is not clear how we can compare different methods of patient stratification. Third, as most of the proposed stratification methods are deterministic, there is a need for investigating the potential benefits of applying probabilistic methods. To address these issues, we introduce a novel integrative Bayesian biclustering method, called B2PS, for patient stratification and propose methods for evaluating the results. Our experimental results demonstrate the superiority of B2PS over a popular state-of-the-art method and the benefits of Bayesian approaches. Our results agree with the intuition that transcriptomic data forms a better basis for patient stratification than genomic data.
BAYESIAN BICLUSTERING FOR PATIENT STRATIFICATION.
Khakabimamaghani, Sahand; Ester, Martin
2016-01-01
The move from Empirical Medicine towards Personalized Medicine has attracted attention to Stratified Medicine (SM). Some methods are provided in the literature for patient stratification, which is the central task of SM, however, there are still significant open issues. First, it is still unclear if integrating different datatypes will help in detecting disease subtypes more accurately, and, if not, which datatype(s) are most useful for this task. Second, it is not clear how we can compare different methods of patient stratification. Third, as most of the proposed stratification methods are deterministic, there is a need for investigating the potential benefits of applying probabilistic methods. To address these issues, we introduce a novel integrative Bayesian biclustering method, called B2PS, for patient stratification and propose methods for evaluating the results. Our experimental results demonstrate the superiority of B2PS over a popular state-of-the-art method and the benefits of Bayesian approaches. Our results agree with the intuition that transcriptomic data forms a better basis for patient stratification than genomic data. PMID:26776199
Bayesian adaptive survey protocols for resource management
Halstead, Brian J.; Wylie, Glenn D.; Coates, Peter S.; Casazza, Michael L.
2011-01-01
Transparency in resource management decisions requires a proper accounting of uncertainty at multiple stages of the decision-making process. As information becomes available, periodic review and updating of resource management protocols reduces uncertainty and improves management decisions. One of the most basic steps to mitigating anthropogenic effects on populations is determining if a population of a species occurs in an area that will be affected by human activity. Species are rarely detected with certainty, however, and falsely declaring a species absent can cause improper conservation decisions or even extirpation of populations. We propose a method to design survey protocols for imperfectly detected species that accounts for multiple sources of uncertainty in the detection process, is capable of quantitatively incorporating expert opinion into the decision-making process, allows periodic updates to the protocol, and permits resource managers to weigh the severity of consequences if the species is falsely declared absent. We developed our method using the giant gartersnake (Thamnophis gigas), a threatened species precinctive to the Central Valley of California, as a case study. Survey date was negatively related to the probability of detecting the giant gartersnake, and water temperature was positively related to the probability of detecting the giant gartersnake at a sampled location. Reporting sampling effort, timing and duration of surveys, and water temperatures would allow resource managers to evaluate the probability that the giant gartersnake occurs at sampled sites where it is not detected. This information would also allow periodic updates and quantitative evaluation of changes to the giant gartersnake survey protocol. Because it naturally allows multiple sources of information and is predicated upon the idea of updating information, Bayesian analysis is well-suited to solving the problem of developing efficient sampling protocols for species of
Shetty, Rahul; Bigiel, Frank
2012-01-01
We develop a Bayesian linear regression method which rigorously treats measurement uncertainties, and accounts for hierarchical data structure for investigating the relationship between the star formation rate and gas surface density. The method simultaneously estimates the intercept, slope, and scatter about the regression line of each individual subject (e.g. a galaxy) and the population (e.g. an ensemble of galaxies). Using synthetic datasets, we demonstrate that the Bayesian method accurately recovers the parameters of both the individuals and the population, especially when compared to commonly employed least squares methods, such as the bisector. We apply the Bayesian method to estimate the Kennicutt-Schmidt (KS) parameters of a sample of spiral galaxies compiled by Bigiel et al. (2008). We find significant variation in the KS parameters, indicating that no single KS relationship holds for all galaxies. This suggests that the relationship between molecular gas and star formation differs between galaxies...
Fast Control Channel Decoding for LTE UE Power Saving
DEFF Research Database (Denmark)
Lauridsen, Mads; Jensen, Anders Riis; Mogensen, Preben
2012-01-01
This work examines the energy saving potential of powering down RRC_connected but unscheduled User Equipment (UE). The idea is to power down energy consuming circuits in RF and BB, when it is determined by Fast Control Channel Decoding (FCCD) that the UE is not scheduled to receive downlink data...... and an evaluation of how the relation affects power consumption. The FCCD method can complement DRX sleep mode since it is applicable when the signal is too aperiodic or fast switching for DRX....
Iterative Demodulation and Decoding Scheme with 16QAM
Institute of Scientific and Technical Information of China (English)
LIU Lin-nan; KUANG Jing-ming; LI Ming; FEI Ze-song
2006-01-01
Iterative demodulation and decoding scheme is analyzed and modulation labeling is considered to be one of the crucial factors to this scheme. By analyzing the existent mapping design criterion, four aspects are found as the key techniques for choosing a label mapping. Based on this discovery, a novel mapping design criterion is proposed and two label mappings are searched according to it. Simulation results show that the performance of BICM-ID using the novel mappings is better than the former ones. The extrinsic information transfer(EXIT) chart is introduced and it is used to evaluate the proposed mapping design criteria.
A New Simple Ultrahigh Speed Decoding Algorithm for BCH Code
Institute of Scientific and Technical Information of China (English)
唐建军; 纪越峰
2002-01-01
In order to content with forward error correction (FEC) technology of the high-speed optical communication system, a new simple decoding algorithm for triple-error correcting Bose, Chaudhuri, and Hocquenghem (BCH) code is proposed. Without complicated matrix-operation or division-operation or intricate iterative algorithm, the algorithm is high efficient and high-speed because of its simplicity in structure. The result of hardware emulation confirms that the algorithm is feasible completely. Introduction of the parallel structure increases the speed of coding greatly. The algorithm can be used in the high-speed optical communication system and other fields.
Iterated decoding of modified product codes in optical networks
DEFF Research Database (Denmark)
Justesen, Jørn
2009-01-01
Appendix I of the standard ITU-T G.975 contains several codes that have been proposed for improved performance of optical transmission. While the original application was submarine cables, the codes are now also used in terrestrial systems where wavelength-division multiplexing (WDM) is introduced....... Currently codes suitable for data rates of 100 Gbits/sec in each channel are being studied. We discuss performance limits for codes with the specified block lengths of about 130,000 and rates about 15/16. The most promising codes appear to be modified product codes, and iterated decoding is used in several...
An LDPC decoder architecture for wireless sensor network applications.
Biroli, Andrea Dario Giancarlo; Martina, Maurizio; Masera, Guido
2012-01-01
The pervasive use of wireless sensors in a growing spectrum of human activities reinforces the need for devices with low energy dissipation. In this work, coded communication between a couple of wireless sensor devices is considered as a method to reduce the dissipated energy per transmitted bit with respect to uncoded communication. Different Low Density Parity Check (LDPC) codes are considered to this purpose and post layout results are shown for a low-area low-energy decoder, which offers percentage energy savings with respect to the uncoded solution in the range of 40%-80%, depending on considered environment, distance and bit error rate. PMID:22438724
An LDPC Decoder Architecture for Wireless Sensor Network Applications
Directory of Open Access Journals (Sweden)
Guido Masera
2012-02-01
Full Text Available The pervasive use of wireless sensors in a growing spectrum of human activities reinforces the need for devices with low energy dissipation. In this work, coded communication between a couple of wireless sensor devices is considered as a method to reduce the dissipated energy per transmitted bit with respect to uncoded communication. Different Low Density Parity Check (LDPC codes are considered to this purpose and post layout results are shown for a low-area low-energy decoder, which offers percentage energy savings with respect to the uncoded solution in the range of 40%–80%, depending on considered environment, distance and bit error rate.
Implants and Decoding for Intracortical Brain Computer Interfaces
Homer, Mark L.; Nurmikko, Arto V.; Donoghue, John P.; Hochberg, Leigh R.
2013-01-01
Intracortical brain computer interfaces (iBCIs) are being developed to enable a person to drive an output device, such as a computer cursor, directly from their neural activity. One goal of the technology is to help people with severe paralysis or limb loss. Key elements of an iBCI are the implanted sensor that records the neural signals and the software which decodes the user’s intended movement from those signals. Here, we focus on recent advances in these two areas, with special attention ...
An efficient scheme of IDCT optimization in H.264 decoding
Bao, Guoxing
2011-02-01
This paper proposes an efficient scheme of IDCT in H.264 decoder. Firstly, motion compensation residuals of macro-block get from the bit-stream are classified into four cases: only dc coefficient is non-zero, only first row coefficients are non-zero, only first column coefficients are non-zero and others, and it is obvious that inverse transform processing of previous cases can be optimized, so in the second, we use different processing of IDCT in different cases to reduce their complexity. Compared with traditional IDCT scheme, the proposed scheme achieves an average 51.8% reduction in computation complexity without degradation in visual quality.
Precoder and decoder prediction in time-varying MIMO channel
DEFF Research Database (Denmark)
Nguyen, Tuan Hung; Leus, Geert; Khaled, Nadia
2005-01-01
system throughput. Thus, predicting the future channel conditions can improve not only the performance but also the throughput of many types of wireless systems. This is especially true for a wireless system where multiple antennas are applied at both link ends. In this report we propose and evaluate the...... performance of a prediction scheme for multiple input multiple output (MIMO) systems that apply spatial multiplexing. We aim at predicting the future precoder/decoder directly without going through the prediction of the channel matrix. The results show that in a slowly time varying channel an increase in the...
Hierarchical Bayesian spatial models for multispecies conservation planning and monitoring.
Carroll, Carlos; Johnson, Devin S; Dunk, Jeffrey R; Zielinski, William J
2010-12-01
Biologists who develop and apply habitat models are often familiar with the statistical challenges posed by their data's spatial structure but are unsure of whether the use of complex spatial models will increase the utility of model results in planning. We compared the relative performance of nonspatial and hierarchical Bayesian spatial models for three vertebrate and invertebrate taxa of conservation concern (Church's sideband snails [Monadenia churchi], red tree voles [Arborimus longicaudus], and Pacific fishers [Martes pennanti pacifica]) that provide examples of a range of distributional extents and dispersal abilities. We used presence-absence data derived from regional monitoring programs to develop models with both landscape and site-level environmental covariates. We used Markov chain Monte Carlo algorithms and a conditional autoregressive or intrinsic conditional autoregressive model framework to fit spatial models. The fit of Bayesian spatial models was between 35 and 55% better than the fit of nonspatial analogue models. Bayesian spatial models outperformed analogous models developed with maximum entropy (Maxent) methods. Although the best spatial and nonspatial models included similar environmental variables, spatial models provided estimates of residual spatial effects that suggested how ecological processes might structure distribution patterns. Spatial models built from presence-absence data improved fit most for localized endemic species with ranges constrained by poorly known biogeographic factors and for widely distributed species suspected to be strongly affected by unmeasured environmental variables or population processes. By treating spatial effects as a variable of interest rather than a nuisance, hierarchical Bayesian spatial models, especially when they are based on a common broad-scale spatial lattice (here the national Forest Inventory and Analysis grid of 24 km(2) hexagons), can increase the relevance of habitat models to multispecies
Directory of Open Access Journals (Sweden)
Morita Mitsuo
2011-06-01
Full Text Available Abstract Background A Bayesian approach based on a Dirichlet process (DP prior is useful for inferring genetic population structures because it can infer the number of populations and the assignment of individuals simultaneously. However, the properties of the DP prior method are not well understood, and therefore, the use of this method is relatively uncommon. We characterized the DP prior method to increase its practical use. Results First, we evaluated the usefulness of the sequentially-allocated merge-split (SAMS sampler, which is a technique for improving the mixing of Markov chain Monte Carlo algorithms. Although this sampler has been implemented in a preceding program, HWLER, its effectiveness has not been investigated. We showed that this sampler was effective for population structure analysis. Implementation of this sampler was useful with regard to the accuracy of inference and computational time. Second, we examined the effect of a hyperparameter for the prior distribution of allele frequencies and showed that the specification of this parameter was important and could be resolved by considering the parameter as a variable. Third, we compared the DP prior method with other Bayesian clustering methods and showed that the DP prior method was suitable for data sets with unbalanced sample sizes among populations. In contrast, although current popular algorithms for population structure analysis, such as those implemented in STRUCTURE, were suitable for data sets with uniform sample sizes, inferences with these algorithms for unbalanced sample sizes tended to be less accurate than those with the DP prior method. Conclusions The clustering method based on the DP prior was found to be useful because it can infer the number of populations and simultaneously assign individuals into populations, and it is suitable for data sets with unbalanced sample sizes among populations. Here we presented a novel program, DPART, that implements the SAMS
BEAST: Bayesian evolutionary analysis by sampling trees
Directory of Open Access Journals (Sweden)
Drummond Alexei J
2007-11-01
Full Text Available Abstract Background The evolutionary analysis of molecular sequence variation is a statistical enterprise. This is reflected in the increased use of probabilistic models for phylogenetic inference, multiple sequence alignment, and molecular population genetics. Here we present BEAST: a fast, flexible software architecture for Bayesian analysis of molecular sequences related by an evolutionary tree. A large number of popular stochastic models of sequence evolution are provided and tree-based models suitable for both within- and between-species sequence data are implemented. Results BEAST version 1.4.6 consists of 81000 lines of Java source code, 779 classes and 81 packages. It provides models for DNA and protein sequence evolution, highly parametric coalescent analysis, relaxed clock phylogenetics, non-contemporaneous sequence data, statistical alignment and a wide range of options for prior distributions. BEAST source code is object-oriented, modular in design and freely available at http://beast-mcmc.googlecode.com/ under the GNU LGPL license. Conclusion BEAST is a powerful and flexible evolutionary analysis package for molecular sequence variation. It also provides a resource for the further development of new models and statistical methods of evolutionary analysis.