Bayesian Inference
Bayesian Inference - Related Videos
10:08
Categories and Inductive Inferences
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2023
Source: Laboratories of Nicholaus Noles and Judith Danovitch—University of Louisville It might be possible for the human brain to keep track of each individual person, place, or thing encountered, but that would be a very inefficient use of time and cognitive resources. Instead, humans develop categories. Categories are mental representations of real things that can be used for a variety of purposes. For example, individuals can use the perceptual features of animals to place them into a given...
01:15
Theory of Attribution I: Correspondent Inference Theory
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2025
Correspondent inference theory, proposed by Jones and Davis in 1965, seeks to explain how individuals infer stable personality traits from observed behaviors. It suggests that people attribute actions to underlying dispositions rather than external circumstances, particularly when the behavior appears intentional and socially significant.Voluntary Behavior and Dispositional AttributionAccording to this theory, individuals are more likely to attribute behavior to personal traits when it appears...
12:39
A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
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Cited by 6 •
2012
Our Bayesian Change Point (BCP) algorithm builds on state-of-the-art advances in modeling change-points via Hidden Markov Models and applies them to chromatin immunoprecipitation sequencing (ChIPseq) data analysis. BCP performs well in both broad and punctate data types, but excels in accurately identifying robust, reproducible islands of diffuse histone enrichment.
10:44
Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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Cited by 3 •
2021
The Inherent Dynamics Visualizer is an interactive visualization package that connects to a gene regulatory network inference tool for enhanced, streamlined generation of functional network models. The visualizer can be used to make more informed decisions for parameterizing the inference tool, thus increasing confidence in the resulting models.
01:16
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data
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2024
Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics. Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance, comparing...
FAQs
The prior distribution establishes how plausible different hypotheses or parameter values are before considering the current data. The likelihood then indicates how compatible the observed evidence is with each possibility. Bayes’ theorem combines these contributions, so the posterior can shift toward explanations that both fit the data and remain consistent with existing knowledge. This makes assumptions about prior knowledge consequential.
A posterior distribution preserves uncertainty across possible hypotheses or parameter values instead of reducing the result to a single choice. That distribution can guide predictions by weighting outcomes according to updated plausibility. It also supports model comparison, allowing researchers to evaluate competing explanations in light of both prior expectations and how well each accounts for the observed evidence.
In sensory processing, incoming signals may be uncertain, while prior expectations provide additional information about possible interpretations. Bayesian inference offers a way to combine these sources and update the resulting belief when new sensory evidence arrives. This provides a computational account of perception in which interpretations reflect both the available signal and expectations brought to the process.
The framework can represent uncertainty about hypotheses and about parameters used in a model. In neuroscience, those uncertainties may be connected to neural activity, behavior, or brain imaging data, depending on the analysis. Rather than treating an estimate as unquestionable, the resulting posterior distribution records how plausible different explanations or parameter values remain after considering the evidence.
A typical workflow begins by specifying the hypotheses or parameters of interest and representing existing knowledge with a prior distribution. Researchers then describe how probable the observed data are under those possibilities through a likelihood. Applying Bayes’ theorem produces a posterior distribution, which can subsequently support prediction or comparison among models of neural, behavioral, or imaging data.
Researchers can use it when they need to reason about uncertain explanations or parameter values in neural activity, behavior, or brain imaging measurements. The framework connects observed data with computational principles relevant to perception, learning, and decision-making. It can therefore help relate patterns in measurements to hypotheses about how nervous systems process evidence and use expectations.