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Results for “Bayesian inference”

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At least 181 records · Page 10Linked to original sources
PubMed2005

Coarse master equation from Bayesian analysis of replica molecular dynamics simulations.

We use Bayesian inference to derive the rate coefficients of a coarse master equation from molecular dynamics simulations. Results from multiple short simulation trajectories are used to estimate propagators. A likelihood function constructed as a product of the propagators provides a posterior distribution of the free coefficients in the rate matrix determining the Markovian master equation. Extensions to non-Markovian dynamics are discussed, using the trajectory "paths" as observations. The Markovian approach is illustrated for the filling and emptying transitions of short carbon nanotubes dissolved in water. We show that accurate thermodynamic and kinetic properties, such as free energy surfaces and kinetic rate coefficients, can be computed from coarse master equations obtained through Bayesian inference.

Bayes Theorem↗
PubMed2006

Chemical plume source localization.

This paper addresses the problem of estimating a likelihood map for the location of the source of a chemical plume using an autonomous vehicle as a sensor probe in a fluid flow. The fluid flow is assumed to have a high Reynolds number. Therefore, the dispersion of the chemical is dominated by turbulence, resulting in an intermittent chemical signal. The vehicle is capable of detecting above-threshold chemical concentration and sensing the fluid flow velocity at the vehicle location. This paper reviews instances of biological plume tracing and reviews previous strategies for a vehicle-based plume tracing. The main contribution is a new source-likelihood mapping approach based on Bayesian inference methods. Using this Bayesian methodology, the source-likelihood map is propagated through time and updated in response to both detection and nondetection events. Examples are included that use data from in-water testing to compare the mapping approach derived herein with the map derived using a previously existing technique.

Air Pollution↗
PubMed1999

Bayesian phylogenetic inference via Markov chain Monte Carlo methods.

We derive a Markov chain to sample from the posterior distribution for a phylogenetic tree given sequence information from the corresponding set of organisms, a stochastic model for these data, and a prior distribution on the space of trees. A transformation of the tree into a canonical cophenetic matrix form suggests a simple and effective proposal distribution for selecting candidate trees close to the current tree in the chain. We illustrate the algorithm with restriction site data on 9 plant species, then extend to DNA sequences from 32 species of fish. The algorithm mixes well in both examples from random starting trees, generating reproducible estimates and credible sets for the path of evolution.

Algorithms↗
PubMed2007

Technical note: how to use Winbugs to draw inferences in animal models.

This paper deals with Bayesian inferences of animal models using Gibbs sampling. First, we suggest a general and efficient method for updating additive genetic effects, in which the computational cost is independent of the pedigree depth and increases linearly only with the size of the pedigree. Second, we show how this approach can be used to draw inferences from a wide range of animal models using the computer package Winbugs. Finally, we illustrate the approach in a simulation study, in which the data are generated and analyzed using Winbugs according to a linear model with i.i.d errors having Student's t distributions. In conclusion, Winbugs can be used to make inferences in small-sized, quantitative, genetic data sets applying a wide range of animal models that are not yet standard in the animal breeding literature.

Animals↗
PubMed2005

MEG source localization under multiple constraints: an extended Bayesian framework.

To use Electroencephalography (EEG) and Magnetoencephalography (MEG) as functional brain 3D imaging techniques, identifiable distributed source models are required. The reconstruction of EEG/MEG sources rests on inverting these models and is ill-posed because the solution does not depend continuously on the data and there is no unique solution in the absence of prior information or constraints. We have described a general framework that can account for several priors in a common inverse solution. An empirical Bayesian framework based on hierarchical linear models was proposed for the analysis of functional neuroimaging data [Friston, K., Penny, W., Phillips, C., Kiebel, S., Hinton, G., Ashburner, J., 2002. Classical and Bayesian inference in neuroimaging: theory. NeuroImage 16, 465-483] and was evaluated recently in the context of EEG [Phillips, C., Mattout, J., Rugg, M.D., Maquet, P., Friston, K., 2005. An empirical Bayesian solution to the source reconstruction problem in EEG. NeuroImage 24, 997-1011]. The approach consists of estimating the expected source distribution and its conditional variance that is constrained by an empirically determined mixture of prior variance components. Estimation uses Expectation-Maximization (EM) to give the Restricted Maximum Likelihood (ReML) estimate of the variance components (in terms of hyperparameters) and the Maximum A Posteriori (MAP) estimate of the source parameters. In this paper, we extend the framework to compare different combinations of priors, using a second level of inference based on Bayesian model selection. Using Monte-Carlo simulations, ReML is first compared to a classic Weighted Minimum Norm (WMN) solution under a single constraint. Then, the ReML estimates are evaluated using various combinations of priors. Both standard criterion and ROC-based measures were used to assess localization and detection performance. The empirical Bayes approach proved useful as: (1) ReML was significantly better than WMN for single priors; (2) valid location priors improved ReML source localization; (3) invalid location priors did not significantly impair performance. Finally, we show how model selection, using the log-evidence, can be used to select the best combination of priors. This enables a global strategy for multiple prior-based regularization of the MEG/EEG source reconstruction.

Bayes Theorem↗
PubMed2006

On the phylogenetic position of a rare Iberian endemic mammal, the Pyrenean desman (Galemys pyrenaicus).

The nucleotide sequences of the complete mitochondrial genome and nine partial nuclear genes of the Pyrenean desman (Galemys pyrenaicus) were determined in order to establish the relative phylogenetic position of this species at different taxonomic levels within the placental tree. Phylogenetic relationships of desman within the family Talpidae were inferred based on complete mitochondrial cytochrome b gene nucleotide sequence data. The Pyrenean desman was unambiguously recovered as sister group of the Russian desman (Desmana moschata) confirming the monophyly of the subfamily Desmaninae. However, phylogenetic relationships among major lineages within the Talpidae could not be confidently resolved. Phylogenetic analyses based on mitochondrial (at the amino acid level) and nuclear (at the nucleotide level) sequence data sets confidently placed desman within the Eulipotyphla (that also included moles, shrews, and hedgehogs), and partially recovered placental interordinal relationships. The monophyly of Laurasiatheria (including Eulipotyphla, Chiroptera, Carnivora, Pholidota, Perissodactyla, and Cetartiodactyla) was strongly supported. Mitochondrial amino acid sequences of Pholidota (pangolins) were found to bias phylogenetic inferences due to long-branch attraction effects. A Bayesian inference based on a combined mitochondrial and nuclear data set without Pholidota arrived at an almost fully resolved tree that supported the basal position of Eulipotyphla within Laurasiatheria.

Animals↗
PubMed2006

Revised assessment of cancer risk to dichloromethane: part I Bayesian PBPK and dose-response modeling in mice.

The current USEPA cancer risk assessment for dichloromethane (DCM) is based on deterministic physiologically based pharmacokinetic (PBPK) modeling involving comparative metabolism of DCM by the GST pathway in the lung and liver of humans and mice. Recent advances in PBPK modeling include probabilistic methods and, in particular, Bayesian inference to quantitatively address variability and uncertainty separately. Although Bayesian analysis of human PBPK models has been published, no such efforts have been reported specifically addressing the mouse, apart from results included in the OSHA final rule on DCM. Certain aspects of the OSHA model, however, are not consistent with current approaches or with the USEPA's current DCM cancer risk assessment. Therefore, Bayesian analysis of the mouse PBPK model and dose-response modeling was undertaken to support development of an improved cancer risk assessment for DCM. A hierarchical population model was developed and prior parameter distributions were selected to reflect parameter values that were considered the most appropriate and best available. Bayesian modeling was conducted using MCSim, a publicly available software program for Markov Chain Monte Carlo analysis. Mean posterior values from the calibrated model were used to develop internal dose metrics, i.e., mg DCM metabolized by the GST pathway/L tissue/day in the lung and liver using exposure concentrations and results from the NTP mouse bioassay, consistent with the approach used by the USEPA for its current DCM cancer risk assessment. Internal dose metrics were 3- to 4-fold higher than those that support the current USEPA IRIS assessment. A decrease of similar magnitude was also noted in dose-response modeling results. These results show that the Bayesian PBPK model in the mouse provides an improved basis for a cancer risk assessment of DCM.

Animals↗
PubMed2005

Phylogenetic relationships, divergence time estimation, and global biogeographic patterns of calopterygoid damselflies (odonata, zygoptera) inferred from ribosomal DNA sequences.

The calopterygoid superfamily (Calopterygidae + Hetaerinidae) is composed of more than twenty genera in two families: the Calopterygidae (at least 17) and the Hetaerinidae (at least 4). Here, 62 calopterygoid (ingroup) taxa representing 18 genera and 15 outgroup taxa are subjected to phylogenetic analysis using the ribosomal 18S and 5.8S genes and internal transcribed spacers (ITS1, ITS2). The five other families of calopterid affinity (Polythoridae, Dicteriadidae, Amphipterygidae, Euphaeidae, and Chlorocyphidae) are included in the outgroup. For phylogenetic inference, we applied maximum parsimony, maximum likelihood, and the Bayesian inference methods. A molecular phylogeny combined with a geographic analysis produced a well-supported phylogenetic hypothesis that partly confirms the traditional taxonomy and describes distributional patterns. A monophyletic origin of the calopterygoids emerges, revealing the Hetaerinid clade as sister group to the Calopterygidae sensu strictu. Within Calopterygidae, seven clades of subfamily rank are recognized. Phylogenetic dating was performed with semiparametric rate smoothing by penalized likelihood, using seven reference fossils for calibration. Divergence time based on the ribosomal genes and spacers and fossil constraints indicate that Calopteryginae (10 genera, approximately 50% of all Calopterygid taxa studied here), Vestalinae (1 genus), and Hetaerinidae (1 genus out of 4 studied here) started radiating around 65 Mya (K/T boundary). The South American Iridictyon (without distinctive morphology except for wing venation) and Southeast Asian Noguchiphaea (with distinctive morphology) are older (about 86 My) and may be survivors of old clades with a Gondwanian range that went extinct at the K/T boundary. The same reasoning (and an even older age, ca. 150 My) applies to the amphipterygids Rimanella and Pentaphlebia (South America-Africa). The extant Calopterygidae show particular species and genus richness between west China and Japan, with genera originating between the early Oligocene and Pleistocene. Much of that richness probably extended much wider in preglacial times. The Holarctic Calopteryx, of Miocene age, was deeply affected by the climatic cooling of the Pliocene and by the Pleistocene glaciations. Its North American and Japanese representatives are of Miocene and Pliocene age, respectively, but its impoverished Euro-Siberian taxa are late Pliocene-Pleistocene, showing reinvasion, speciation, and introgression events. The five other calopterid families combine with the Calopterygidae and Hetaerinidae to form the monophyletic cohort Caloptera, with Polythoridae, Dicteriadidae, and Amphipterygidae sister group to Calopterygoidea. The crown node age of the latter three families has an age of about 157 My, but the Dicteriadidae and Polythoridae themselves are of Eocene age, and the same is true for the Euphaeidae and Chlorocyphidae. The cohort Caloptera itself, with about 197 My of age, goes back to the early Jurassic.

Animals↗
PubMed2000

Bayesian accelerated failure time analysis with application to veterinary epidemiology.

Standard methods for analysing survival data with covariates rely on asymptotic inferences. Bayesian methods can be performed using simple computations and are applicable for any sample size. We propose a practical method for making prior specifications and discuss a complete Bayesian analysis for parametric accelerated failure time regression models. We emphasize inferences for the survival curve rather than regression coefficients. A key feature of the Bayesian framework is that model comparisons for various choices of baseline distribution are easily handled by the calculation of Bayes factors. Such comparisons between non-nested models are difficult in the frequentist setting. We illustrate diagnostic tools and examine the sensitivity of the Bayesian methods.

Abortion, Veterinary↗
PubMed2004

Estimation in Bayesian disease mapping.

Recent work on Bayesian inference of disease mapping models discusses the advantages of the fully Bayesian (FB) approach over its empirical Bayes (EB) counterpart, suggesting that FB posterior standard deviations of small-area relative risks are more reflective of the uncertainty associated with the relative risk estimation than counterparts based on EB inference, since the latter fail to account for the variability in the estimation of the hyperparameters. In this article, an EB bootstrap methodology for relative risk inference with accurate parametric EB confidence intervals is developed, illustrated, and contrasted with the hyperprior Bayes. We elucidate the close connection between the EB bootstrap methodology and hyperprior Bayes, present a comparison between FB inference via hybrid Markov chain Monte Carlo and EB inference via penalized quasi-likelihood, and illustrate the ability of parametric bootstrap procedures to adjust for the undercoverage in the "naive" EB interval estimates. We discuss the important roles that FB and EB methods play in risk inference, map interpretation, and real-life applications. The work is motivated by a recent analysis of small-area infant mortality rates in the province of British Columbia in Canada.

Algorithms↗
PubMed2006

Inference of DNA sequences from mechanical unzipping: an ideal-case study.

The performances of Bayesian inference to predict the sequence of DNA molecules from fixed-force unzipping experiments are investigated. We show that the probability of misprediction decreases exponentially with the amount of collected data. The decay rate is calculated as a function of biochemical parameters (binding free energies), the sequence content, the applied force, the elastic properties of a DNA single strand, and time resolution.

Bacteriophage lambda↗
PubMed1997

Bayesian adaptive alignment and inference.

Sequence alignment without the specification of gap penalties or a scoring matrix is attained by using Bayesian inference and a recursive algorithm. This procedure's recursive algorithm sums over all possible alignments on the forward step to obtain normalizing constants essential to Bayesian inferences, and samples from the exact posterior distribution on the backward step. Since both terminal and intervening unrelated subsequences will often be excluded from an alignment, the resulting alignments may be seen as extensions of local alignments. An alignment's significance is assessed using the Bayesian evidence. A shuffling simulation shows that Bayesian evidence against the null hypothesis tends to be a conservative measure of significance compared to classical p-values. An application to proteins from the GTPase superfamily shows that the posterior distribution of the number of gaps is often flat and that the posterior distribution of the evolutionary distance is often flat and sometimes bimodal. An alignment of 1GIA with 1ETU shows good correspondence with a structural alignment.

Algorithms↗
PubMedDate not supplied

Interim analyses in clinical trials: classical vs. Bayesian approaches.

This paper concerns interim analysis in clinical trials involving two treatments from the points of view of both classical and Bayesian inference. I criticize classical hypothesis testing in this setting and describe and recommend a Bayesian approach in which sampling stops when the probability that one treatment is the better exceeds a specified value. I consider application to normal sampling analysed in stages and evaluate the gain in average sample number as a function of the number of interim analyses.

Bayes Theorem↗
PubMed2005

Using the quantitative genetic threshold model for inferences between and within species.

Sewall Wright's threshold model has been used in modelling discrete traits that may have a continuous trait underlying them, but it has proven difficult to make efficient statistical inferences with it. The availability of Markov chain Monte Carlo (MCMC) methods makes possible likelihood and Bayesian inference using this model. This paper discusses prospects for the use of the threshold model in morphological systematics to model the evolution of discrete all-or-none traits. There the threshold model has the advantage over 0/1 Markov process models in that it not only accommodates polymorphism within species, but can also allow for correlated evolution of traits with far fewer parameters that need to be inferred. The MCMC importance sampling methods needed to evaluate likelihood ratios for the threshold model are introduced and described in some detail.

Bayes Theorem↗
PubMed1999

Toward evidence-based medical statistics. 2: The Bayes factor.

Bayesian inference is usually presented as a method for determining how scientific belief should be modified by data. Although Bayesian methodology has been one of the most active areas of statistical development in the past 20 years, medical researchers have been reluctant to embrace what they perceive as a subjective approach to data analysis. It is little understood that Bayesian methods have a data-based core, which can be used as a calculus of evidence. This core is the Bayes factor, which in its simplest form is also called a likelihood ratio. The minimum Bayes factor is objective and can be used in lieu of the P value as a measure of the evidential strength. Unlike P values, Bayes factors have a sound theoretical foundation and an interpretation that allows their use in both inference and decision making. Bayes factors show that P values greatly overstate the evidence against the null hypothesis. Most important, Bayes factors require the addition of background knowledge to be transformed into inferences--probabilities that a given conclusion is right or wrong. They make the distinction clear between experimental evidence and inferential conclusions while providing a framework in which to combine prior with current evidence.

Bayes Theorem↗
PubMed2003

Interactive decision support system to predict print quality.

Customers using printers occasionally experience problems such as fuzzy images, bands, or streaks. The customer may call or otherwise contact the manufacturer, who attempts to diagnose the problem based on the customer's description of the problem. This study evaluated Bayesian inference as a tool for identifying or diagnosing 16 different types of print defects from such descriptions. The Bayesian model was trained using 1701 narrative descriptions of print defects obtained from 60 subjects with varying technical backgrounds. The Bayesian model was then implemented as an interactive decision support system, which was used by eight 'agents' to diagnose print defects reported by 16 'customers' in a simulated call centre. The 'agents' and 'customers' in the simulated call centre were all students at Purdue University. Each customer made eight telephone calls, resulting in a total of 128 telephone calls in which the customer reported defects to the agents. The results showed that the Bayesian model closely fitted the data in the training set of narratives. Overall, the model correctly predicted the actual defect category with its top prediction 70% of the time. The actual defect was in the top five predictions 94% of the time. The model in the simulated call centre performed nearly as well for the test subjects. The top prediction was correct 50% of the time, and the defect was one of the top five predictions 80% of the time. Agent accuracy in diagnosing the problem improved when using the tool. These results demonstrated that the Bayesian system learned enough from the existing narratives to accurately classify print defect categories.

Adolescent↗
PubMed2003

Molecular phylogenetic analysis of the genus Gyrodactylus (Platyhelminthes: Monogenea) inferred from rDNA ITS region: subgenera versus species groups.

Analyses of small subunit ribosomal RNA gene sequences of representatives of major taxa of Monopisthocotylea were performed to identify the sister group of Gyrodactylus. Nuclear ribosomal DNA sequences from the complete internal transcribed spacer (ITS) region were used to infer phylogeny of 37 Gyrodactylus species and Gyrodactyloides bychowskii, Macrogyrodactylus polypteri and Gyrdicotylus gallieni, using maximum likelihood, parsimony and Bayesian inference. The genus Gyrodactylus appeared to be a monophyletic group in all analyses, based on the present data set. Within the genus, there were 3 major groups recognized by high bootstrap values and posterior probabilities. None of the 6 subgenera appeared to be monophyletic, and the most basal subgenus G. (Gyrodactylus) was paraphyletic. Characteristics of the excretory system of Gyrodactylus do not seem to be conservative enough to reveal subgenera within Gyrodactylus and we suggest abandoning existing subgenera as indicators of phylogeny. The grouping of species based on the morphology of the ventral bar and marginal hooks seems to have sufficient power to infer relationships between the Gyrodactylus species.

Animals↗