SEARCH · PubMed Health

Results for “Bayesian inference”

Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 37 records · Page 2Linked to original sources

PubMed2024

BICEP: Bayesian inference for rare genomic variant causality evaluation in pedigrees.

Next-generation sequencing is widely applied to the investigation of pedigree data for gene discovery. However, identifying plausible disease-causing variants within a robust statistical framework is challenging. Here, we introduce BICEP: a Bayesian inference tool for rare variant causality evaluation in pedigree-based cohorts. BICEP calculates the posterior odds that a genomic variant is causal for a phenotype based on the variant cosegregation as well as a priori evidence such as deleteriousness and functional consequence. BICEP can correctly identify causal variants for phenotypes with both Mendelian and complex genetic architectures, outperforming existing methodologies. Additionally, BICEP can correctly down-weight common variants that are unlikely to be involved in phenotypic liability in the context of a pedigree, even if they have reasonable cosegregation patterns. The output metrics from BICEP allow for the quantitative comparison of variant causality within and across pedigrees, which is not possible with existing approaches.

Pedigree↗

PubMed1998

Approximate Bayesian inference for random effects meta-analysis.

Whilst meta-analysis is becoming a more commonplace statistical technique, Bayesian inference in meta-analysis requires complex computational techniques to be routinely applied. We consider simple approximations for the first and second moments of the parameters of a Bayesian random effects model for meta-analysis. These computationally inexpensive methods are based on simple analytical formulae that provide an efficient tool for a qualitative analysis and a quick numerical estimation of posterior quantities. They are shown to lead to sensible approximations in two examples of meta-analyses and to be in broad agreement with the more computationally intensive Gibbs sampling.

Antibiotic Prophylaxis↗

PubMed2001

Using Bayesian inference to perform meta-analysis.

Bayesian modeling offers an elegant approach to meta-analysis that efficiently incorporates all sources of variability and relevant quantifiable external information. It provides a more informative summary of the likely value of parameters after observing the data than do non-Bayesian approaches. This leads to direct probabilistic inference about model parameters such as the average treatment effect, the between-study variance, and individual study treatment effects. The latter are weighted averages of the common mean and individual study means with weights reflecting the amount of information provided by each study relative to the others. Homogeneity among these posterior study estimates indicates that pooling these studies is appropriate; heterogeneity suggests that some cause of between-study variation should be explored. The author describes the construction of such models and shows how to use them to estimate a common mean and regression slopes. Two examples illustrate the additional inferences available with the Bayesian methodology.

Angiotensin-Converting Enzyme Inhibitors↗

PubMed2003

Bayesian inference of inbreeding depression in controlled crosses.

This study shows how a Gibbs sampling approach can be used for Bayesian inference of inbreeding depression. The method presented is mainly concerned with organisms that can be both selfed and outcrossed. Tests performed on simulated data with unequal variances and missing observations show that the method works well. Real data from the plant Scabiosa canescens is also analyzed.

Analysis of Variance↗

PubMedDate not supplied

Some adaptive monte carlo methods for Bayesian inference.

Monte Carlo methods, in particular Markov chain Monte Carlo methods, have become increasingly important as a tool for practical Bayesian inference in recent years. A wide range of algorithms is available, and choosing an algorithm that will work well on a specific problem is challenging. It is therefore important to explore the possibility of developing adaptive strategies that choose and adjust the algorithm to a particular context based on information obtained during sampling as well as information provided with the problem. This paper outlines some of the issues in developing adaptive methods and presents some preliminary results.

Algorithms↗

PubMed1996

Bayesian inference on prevalence using a missing-data approach with simulation-based techniques: applications to HIV screening.

Health departments and other health-related authorities seek accurate assessment of the spread of human immunodeficiency virus (HIV) among populations. Although screening for HIV provides a direct means for estimating its prevalence, it is complicated by the heterogeneity of available diagnostic tests and the degree to which they can diagnose HIV accurately. To integrate the limited precision of screening tests with prior results, Bayesian inference becomes a method of choice. Current Bayesian methods, however, have limited applications and do not readily generalize for complicated sampling designs and for modelling needs, particularly those that relate to HIV screening. By utilizing recent developments in the theories of missing-data analysis and simulation-based techniques, we develop an approach to Bayesian analysis of prevalence. This methodology is quite general for a variety of sampling schemes and sufficiently flexible to accommodate various practical considerations that arise from HIV screening. We illustrate the methodology with real as well as simulated data sets. Further, by utilizing the methodology, we performed simulations to demonstrate that pooled testing provides a cost-effective means to improve the precision of estimates of prevalence under the currently limited screening technology.

Algorithms↗

PubMed1993

Bayesian inference in multipoint gene mapping.

The problem of ordering and mapping genes on the basis of recombinant data and radiation hybrid data is formulated as a problem of Bayesian inference for an unknown permutation. The challenging computational problems posed by this approach are shown to be resolvable using Markov chain Monte Carlo methods.

Algorithms↗

PubMed2001

Bayesian inference for a random tessellation process.

This article describes an inhomogeneous Poisson point process in the plane with an intensity function based on a Dirichlet tessellation process and a method for using observations on the point process to make fully Bayesian inferences about the underlying tessellation. The method is implemented using a Markov chain Monte Carlo approach. An application to modeling the territories of clans of badgers, Meles meles, is described.

Animals↗

PubMed1996

Bayesian inference for hierarchical mixtures-of-experts with applications to regression and classification.

This paper studies the problems of inference and prediction in a class of models known as hierarchical mixtures-of-experts (HME). The statistical model underlying an HME is a mixture model in which both the mixture coefficients and the mixture components are generalized linear models. Bayesian inference regarding an HME's parameters is presented in the contexts of regression and classification using Markov chain Monte Carlo methods. A benefit of this Bayesian approach is the ability to obtain a sample from the posterior distribution of any functional of the parameters of the given model. In this way, more information is obtained than provided by a point estimate. The methods are illustrated on a nonlinear regression problem and on a breast cancer classification problem. The results indicate that the HME showed good prediction performance, and also gave the additional benefit of providing for the opportunity to assess the degree of certainty of the model in its predictions.

Algorithms↗

PubMed1985

Estimation of auditory brainstem response, ABR, by means of Bayesian inference.

The present paper describes a new method to estimate the auditory brainstem response when the electrical activity from the recording electrodes displays non-stationarity, i.e. varies between low and high levels. The method is based on a statistical approach called Bayesian inference and weights the individual components (here blocks of 250 sweeps) inversely proportional to the level of the noise activity during the recording. Fifty sets of data from 10 consecutive patients obtained during stimulation at high intensity are used to evaluate the difference between the classic averaging and the present method which is called Bayes estimation. In approximately 30% of the cases, a significant all-over improvement is obtained by the new method. The classic averaging technique would here require 50% more sweeps to be taken to obtain the same precision of the ABR estimate, on average. Also the latency and amplitude parameters of the Jv wave complex are evaluated and it is shown that the parameter variance decreases by a factor of approximately 2 by using the Bayes estimation. The new technique is compared with a similar technique recently presented by Hoke et al. (1984) and the differences and similarities are discussed.

Bayes Theorem↗

PubMedDate not supplied

Bayesian inference on genetic merit under uncertain paternity.

A hierarchical animal model was developed for inference on genetic merit of livestock with uncertain paternity. Fully conditional posterior distributions for fixed and genetic effects, variance components, sire assignments and their probabilities are derived to facilitate a Bayesian inference strategy using MCMC methods. We compared this model to a model based on the Henderson average numerator relationship (ANRM) in a simulation study with 10 replicated datasets generated for each of two traits. Trait 1 had a medium heritability (h2) for each of direct and maternal genetic effects whereas Trait 2 had a high h2 attributable only to direct effects. The average posterior probabilities inferred on the true sire were between 1 and 10% larger than the corresponding priors (the inverse of the number of candidate sires in a mating pasture) for Trait 1 and between 4 and 13% larger than the corresponding priors for Trait 2. The predicted additive and maternal genetic effects were very similar using both models; however, model choice criteria (Pseudo Bayes Factor and Deviance Information Criterion) decisively favored the proposed hierarchical model over the ANRM model.

Animals↗

PubMed2001

Bayesian inference of phylogeny and its impact on evolutionary biology.

As a discipline, phylogenetics is becoming transformed by a flood of molecular data. These data allow broad questions to be asked about the history of life, but also present difficult statistical and computational problems. Bayesian inference of phylogeny brings a new perspective to a number of outstanding issues in evolutionary biology, including the analysis of large phylogenetic trees and complex evolutionary models and the detection of the footprint of natural selection in DNA sequences.

Algorithms↗

PubMed2001

Application of Bayesian inference to characterize risks associated with low doses of low-LET radiation.

Improved risk characterization for stochastic biological effects of low doses of low-LET radiation is important for protecting nuclear workers and the public from harm from radiation exposure. Here we present a Bayesian approach to characterize risks of stochastic effects from low doses of low-LET radiation. The stochastic effect considered is neoplastic transformation of cells because it relates closely to cancer induction. We have used a published model of neoplastic transformation called NEOTRANS1. It is based on two different classes of cellular sensitivity for asynchronous, exponentially growing populations (in vitro). One sensitivity class is the hypersensitive cell; the other is the resistant cell. NEOTRANS1 includes the effects of genomic damage accumulation, DNA repair during cell cycle arrest, and DNA misrepair (non-lethal repair errors). The model-associated differential equations are solved for conditions of in vitro irradiation at a fixed rate. Previously published solutions apply only to high dose rates and were incorrectly assumed to apply to only high-LET radiation. Solutions provided here apply to any fixed dose rate and to both high- and low-LET radiations. Markov chain Monte Carlo methods are used to carry out the Bayesian inference of the low-dose risk for neoplastic transformation of aneuploid C3H 10T1/2 cells for X-ray doses from 0 to 1000 mGy. We have assumed that for this low-dose range only the hypersensitive fraction of the cells are affected. Our results indicate that the initial slope of the risk vs dose relationship for neoplastic transformation is as follows: (1) directly proportional to the fraction, f1, of hypersensitive cells; (2) directly proportional to the radiosensitivity of the genomic target; and (3) inversely proportional to the rate at which hypersensitive cells with radiation-induced damage are committed to undergo correct repair of genomic damage. Further, our results indicate that very fast molecular events are associated with the commitment of cells to the correct repair pathway. Results also indicate a relatively large probability for misrepair that leads to genomic instability. Our results are consistent with the view that for very low doses, dose rate is not an important variable for characterizing low-LET radiation risks so long as age-related changes in sensitivity do not occur during irradiation.

Animals↗

PubMed1991

A computational model of approximate Bayesian inference for associating clinical algorithms with decision analyses.

The lack of rationale or explanation is a major deficiency of clinical algorithms. To address this issue, the authors present a computational model for associating decision analyses with clinical algorithms. Automata theory is used to model categorical reasoning with approximate Bayesian inference based on probability intervals. This approximation reduces the number of computations to linear-order instead of the exponential-order combinations of clinical findings in exact Bayes. The linkage of decision analyses and clinical algorithms by means of this model exploits a new concept of "regular" clinical algorithms and their equivalency in theory and provides valuable perspectives in practice for developers of clinical algorithms.

Algorithms↗

PubMed2003

[Quantitative basis for medical imaging analysis: Information theory and Bayesian inference].

One of the main issues in radiology is image analysts and report. The two stages of this process are the extraction of the information from the image and the diagnostic exercise. This paper is a theoretical analysis of image interpretation from a quantitative perspective, using information theory and Bayesian inference, and relating it to the concepts of evidence based medicine. It is concluded that the amount of information is dependent on the previous state of knowledge and the acquaintance with the clinical history and complementary exams. The three determined stages of the diagnostic process are a priori, conditioned by the image and a posteriori analyses. The first stage is related to the degree of information and knowledge of the case. The second stage is dependent on the conditional probability for the presence of image signs of the disease and it is thus directly dependent on the sensitivity and specificity of the exam. The third stage is dependent of the previous stages, that in the simplest case can be expressed in function of the odds or likelihood ratios. These three stages allow to reach a decision about the usefulness of a given exam and they orient the questions that a given image may answer. They also orient the research on image analysis for an adequate decision making, minimizing diagnostic uncertainty.

Bayes Theorem↗

PubMed2003

Hierarchical Bayesian inference in the visual cortex.

Traditional views of visual processing suggest that early visual neurons in areas V1 and V2 are static spatiotemporal filters that extract local features from a visual scene. The extracted information is then channeled through a feedforward chain of modules in successively higher visual areas for further analysis. Recent electrophysiological recordings from early visual neurons in awake behaving monkeys reveal that there are many levels of complexity in the information processing of the early visual cortex, as seen in the long-latency responses of its neurons. These new findings suggest that activity in the early visual cortex is tightly coupled and highly interactive with the rest of the visual system. They lead us to propose a new theoretical setting based on the mathematical framework of hierarchical Bayesian inference for reasoning about the visual system. In this framework, the recurrent feedforward/feedback loops in the cortex serve to integrate top-down contextual priors and bottom-up observations so as to implement concurrent probabilistic inference along the visual hierarchy. We suggest that the algorithms of particle filtering and Bayesian-belief propagation might model these interactive cortical computations. We review some recent neurophysiological evidences that support the plausibility of these ideas.

Animals↗

PubMed2004

Bayesian inference of the metazoan phylogeny; a combined molecular and morphological approach.

Metazoan phylogeny remains one of evolutionary biology's major unsolved problems. Molecular and morphological data, as well as different analytical approaches, have produced highly conflicting results due to homoplasy resulting from more than 570 million years of evolution. To date, parsimony has been the only feasible combined approach but is highly sensitive to long-branch attraction. Recent development of stochastic models for discrete morphological characters and computationally efficient methods for Bayesian inference has enabled combined molecular and morphological data analysis with rigorous statistical approaches less prone to such inconsistencies. We present the first statistically founded analysis of a metazoan data set based on a combination of morphological and molecular data and compare the results with a traditional parsimony analysis. Interestingly, the Bayesian analyses demonstrate a high degree of congruence between morphological and molecular data, and both data sets contribute to the result of the combined analysis. Additionally, they resolve several irregularities obtained in previous studies and show high credibility values for controversial groups such as the ecdysozoans and lophotrochozoans. Parsimony, on the contrary, shows conflicting results, with morphology being congruent to the Bayesian results and the molecular data set producing peculiarities that are largely reflected in the combined analysis.

Animals↗

PubMed2006

Vision as Bayesian inference: analysis by synthesis?

We argue that the study of human vision should be aimed at determining how humans perform natural tasks with natural images. Attempts to understand the phenomenology of vision from artificial stimuli, although worthwhile as a starting point, can lead to faulty generalizations about visual systems, because of the enormous complexity of natural images. Dealing with this complexity is daunting, but Bayesian inference on structured probability distributions offers the ability to design theories of vision that can deal with the complexity of natural images, and that use 'analysis by synthesis' strategies with intriguing similarities to the brain. We examine these strategies using recent examples from computer vision, and outline some important implications for cognitive science.

Algorithms↗