Category: Bayesian inference

Bayesian epistemology
Bayesian epistemology is a formal approach to various topics in epistemology that has its roots in Thomas Bayes' work in the field of probability theory. One advantage of its formal method in contrast
Bernstein–von Mises theorem
In Bayesian inference, the Bernstein-von Mises theorem provides the basis for using Bayesian credible sets for confidence statements in parametric models. It states that under some conditions, a poste
Bayes factor
The Bayes factor is a ratio of two competing statistical models represented by their marginal likelihood, and is used to quantify the support for one model over the other. The models in questions can
Bayesian inference in motor learning
Bayesian inference is a statistical tool that can be applied to motor learning, specifically to adaptation. Adaptation is a short-term learning process involving gradual improvement in performance in
Expected value of sample information
In decision theory, the expected value of sample information (EVSI) is the expected increase in utility that a decision-maker could obtain from gaining access to a sample of additional observations be
Credal set
A credal set is a set of probability distributions or, more generally, a set of (possibly finitely additive) probability measures. A credal set is often assumed or constructed to be a closed convex se
Chain rule (probability)
In probability theory, the chain rule (also called the general product rule) permits the calculation of any member of the joint distribution of a set of random variables using only conditional probabi
An Essay towards solving a Problem in the Doctrine of Chances
An Essay towards solving a Problem in the Doctrine of Chances is a work on the mathematical theory of probability by Thomas Bayes, published in 1763, two years after its author's death, and containing
Information field theory
Information field theory (IFT) is a Bayesian statistical field theory relating to signal reconstruction, cosmography, and other related areas. IFT summarizes the information available on a physical fi
Spike-and-slab regression
Spike-and-slab regression is a type of Bayesian linear regression in which a particular hierarchical prior distribution for the regression coefficients is chosen such that only a subset of the possibl
Bayesian inference
Bayesian inference is a method of statistical inference in which Bayes' theorem is used to update the probability for a hypothesis as more evidence or information becomes available. Bayesian inference
Cutaneous rabbit illusion
The cutaneous rabbit illusion (also known as cutaneous saltation and sometimes the cutaneous rabbit effect or CRE) is a tactile illusion evoked by tapping two or more separate regions of the skin in r
Kappa effect
The kappa effect or perceptual time dilation is a temporal perceptual illusion that can arise when observers judge the elapsed time between sensory stimuli applied sequentially at different locations.
Bayesian information criterion
In statistics, the Bayesian information criterion (BIC) or Schwarz information criterion (also SIC, SBC, SBIC) is a criterion for model selection among a finite set of models; models with lower BIC ar
Integrated nested Laplace approximations
Integrated nested Laplace approximations (INLA) is a method for approximate Bayesian inference based on Laplace's method. It is designed for a class of models called latent Gaussian models (LGMs), for
Mathematical models of social learning
Mathematical models of social learning aim to model opinion dynamics in social networks. Consider a social network in which people (agents) hold a belief or opinion about the state of something in the
Bayesian quadrature
Bayesian quadrature is a numerical method for solving numerical integration problems which falls within the class of probabilistic numerical methods. Bayesian quadrature views numerical integration as
Tau effect
The tau effect is a spatial perceptual illusion that arises when observers judge the distance between consecutive stimuli in a stimulus sequence. When the distance from one stimulus to the next is con
Bayesian linear regression
Bayesian linear regression is a type of conditional modeling in which the mean of one variable is described by a linear combination of other variables, with the goal of obtaining the posterior probabi
Bayesian multivariate linear regression
In statistics, Bayesian multivariate linear regression is aBayesian approach to multivariate linear regression, i.e. linear regression where the predicted outcome is a vector of correlated random vari
Credal network
Credal networks are probabilistic graphical models based on imprecise probability. Credal networks can be regarded as an extension of Bayesian networks, where credal sets replace probability mass func
Checking whether a coin is fair
In statistics, the question of checking whether a coin is fair is one whose importance lies, firstly, in providing a simple problem on which to illustrate basic ideas of statistical inference and, sec
Conservatism (belief revision)
In cognitive psychology and decision science, conservatism or conservatism bias is a bias which refers to the tendency to revise one's belief insufficiently when presented with new evidence. This bias
Uniqueness thesis (epistemology)
The uniqueness thesis is “the idea that a body of evidence justifies at most one proposition out of a competing set of propositions (e.g., one theory out of a bunch of exclusive alternatives) and that