- Conditionals
- >
- Conditional probability
- >
- Bayesian statistics
- >
- Bayesian inference

- Logical consequence
- >
- Inference
- >
- Statistical inference
- >
- Bayesian inference

- Probability theory
- >
- Conditional probability
- >
- Bayesian statistics
- >
- Bayesian inference

- Statistical methods
- >
- Statistical analysis
- >
- Statistical inference
- >
- Bayesian inference

- Statistics
- >
- Statistical theory
- >
- Bayesian statistics
- >
- Bayesian inference

- Statistics
- >
- Statistical theory
- >
- Statistical inference
- >
- 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

© 2023 Useful Links.