Bayesian neural network - A Bayesian neural network is a neural model that represents uncertainty about its parameters using probability distributions and updates that uncertainty in...
Bayesian optimization - Bayesian optimization is a method for choosing successive evaluations of an expensive objective using a probabilistic surrogate model and an acquisition rule.
Bayesian optimization acquisition function - A Bayesian optimization acquisition function scores possible evaluations using the current probabilistic model of an objective.
Bayesian probability - Bayesian probability interprets probability as a quantified degree of uncertainty about a proposition, conditional on available information and modeling...
Bayesian program synthesis - Bayesian program synthesis constructs or selects programs by treating candidate programs as hypotheses in a Bayesian model.
Bayesian programming - Bayesian programming is an approach to specifying computational problems through probability models and answering questions by probabilistic inference.
Bayesian Regression - Bayesian regression models an outcome conditional on predictors while assigning prior distributions to unknown model parameters and updating them with...
Bayesian regret - Bayesian regret is expected regret averaged over a prior distribution of possible environments or unknown parameters.
Bayesian reinforcement learning - Bayesian reinforcement learning is a family of approaches that represent uncertainty about a decision problem using probability distributions and update...
Bayesian Ridge Regression - Bayesian ridge regression is linear regression with a Gaussian prior on the coefficients, producing a probability distribution over those coefficients.
Bayesian search theory - Bayesian search theory is a framework for allocating search effort using probabilities of a target's location, models of detection, and Bayesian updates as...
Bayesian statistics - Bayesian statistics is an approach to statistical analysis that represents uncertainty about unknown quantities with probability distributions and updates...
Bayesian structural time series - Bayesian structural time series, or BSTS, is a family of models that represents a time series through components such as trend, seasonality and regression...
Bayesian survival analysis - Bayesian survival analysis models time until a defined event using probability distributions and Bayesian updating, while accounting for incomplete...
Bayesian t-test - A Bayesian t-test is a Bayesian approach to comparing population means in a setting traditionally analyzed with a t-test.
Bayesian vector autoregression - Bayesian vector autoregression is a Bayesian approach to modeling several time series jointly as functions of their past values.
BCPNN - BCPNN stands for Bayesian Confidence Propagation Neural Network.
Beam search - Beam search is a search strategy that keeps only a limited number of promising partial candidates and discards the rest. The limit is called the beam width.
Beam width - Beam width is the maximum number of candidate partial solutions retained at a step of a beam-search procedure.
Behavior-based robotics - Behavior-based robotics is an approach to robot control that builds overall activity from interacting behaviors, such as avoiding obstacles, following a...
Behavior tree - A behavior tree is a hierarchical structure for controlling how an agent or system carries out tasks. Its nodes organize conditions, actions and control flow.
Behavioral Bias - Behavioral bias, in the source terminology used here, concerns systematic differences in user behavior across platforms, contexts or populations represented...
Behavioral clustering - Behavioral clustering groups entities according to similarities in observed behavior.
Behrens - Fisher problem - The Behrens - Fisher problem concerns inference about the difference between two normal-population means using independent samples when the population...
Beier - Neely field warping - Beier - Neely field warping is an image-deformation method controlled by corresponding line segments.
Belief aggregation - Belief aggregation combines probability assessments from several people, models, or other sources into a single probability distribution.
Belief function - A belief function is a mathematical representation of evidential support used in Dempster - Shafer theory.
Belief propagation - Belief propagation is a message-passing method for computing marginal probabilities in a probabilistic graphical model.
Belief revision - Belief revision is the systematic change of a set of accepted statements when new information arrives, especially when that information conflicts with...
Belief state - A belief state represents an agent's information about the situation it may currently be in.
Bellman equation - A Bellman equation expresses the value of a decision as an immediate expected reward plus the discounted value of what happens next.
Bellman filter - The Bellman filter is an approximate state-estimation method developed from Bellman's dynamic-programming principle for state-space models.
Bellman operator - A Bellman operator is a mathematical transformation that takes a proposed value function and returns updated values using immediate rewards and...
Bellman pseudospectral method - The Bellman pseudospectral method is an optimal-control computation approach that combines pseudospectral trajectory optimization with Bellman's principle...
Bellman residual - A Bellman residual measures the discrepancy between a proposed value function and the result of applying a Bellman update to it.
Bellman update - A Bellman update revises an estimated state value or action value using an immediate reward and the estimated value of what follows.
Benchmarking - Benchmarking is the systematic measurement and comparison of algorithms or systems using a defined set of tasks, data, metrics and test conditions.
Benchmarks - Benchmarks are specified tasks, data and evaluation procedures used to compare systems under shared conditions.
Benson's algorithm - Benson's algorithm is an optimization approach that constructs the trade-off geometry of a multi-objective problem by successively refining a polyhedral...
Berk-Jones test - The Berk-Jones test is a goodness-of-fit approach that assesses discrepancies between an empirical distribution and a specified reference distribution using...
Berlekamp - Welch algorithm - The Berlekamp - Welch algorithm recovers a low-degree polynomial from evaluations that include a limited number of incorrect values.
Berlin procedure - The Berlin procedure is a method for decomposing economic time series into estimated underlying and recurring components.
Berndt - Hall - Hall - Hausman algorithm - The Berndt - Hall - Hall - Hausman algorithm, usually called BHHH, is a numerical method for maximum-likelihood estimation.
Bernoulli - A Bernoulli distribution describes a random variable with two possible values: 1 with probability p and 0 with probability 1 − p.
Bernoulli bandit - A Bernoulli bandit is a multi-armed bandit problem in which each chosen action produces a binary reward, commonly 1 for success and 0 for failure.
Bernoulli mixture model - A Bernoulli mixture model represents binary observations as coming from a mixture of hidden components.