Artificial precision - Artificial precision is the misleading appearance of certainty created by reporting more numerical detail than the evidence or measurement process supports.
Asymmetric cointegration - Asymmetric cointegration describes long-run relationships between time series in which the adjustment process or the relationship itself permits different...
Asymmetric relationships - Asymmetric relationships, in the categorical-data terminology used by the supplied glossary, are relationships analyzed with designated predictor and...
Attenuation - Attenuation is a weakening of an observed signal or statistical relationship.
Aumann's agreement theorem - Aumann's agreement theorem states that two Bayesian agents with a common prior must assign the same posterior probability to an event if those posterior...
Auto-regressive model - An auto-regressive model predicts or models the next element of a sequence using earlier elements of that sequence.
Autologistic actor attribute models - Autologistic actor attribute models, or ALAAMs, are statistical models for binary attributes of actors connected by a network.
Autoregressive conditional duration - Autoregressive conditional duration, or ACD, is a time-series modeling framework for the positive waiting times between successive events.
Auxiliary particle filter - An auxiliary particle filter is a sequential Monte Carlo method that uses the latest observation to help select which existing particles should produce the...
Backed-up value - A backed-up value is a value assigned to an earlier state or search-tree node by combining values from its successors according to a specified decision rule.
Backoff model - A backoff model uses a less specific model when the more specific context lacks sufficient evidence for a prediction.
Backward elimination - Backward elimination is a model-selection procedure that starts with a specified full set of candidate predictors and removes terms one at a time according...
Ball divergence - Ball divergence is a statistical measure of how two probability distributions differ, constructed by comparing the probability they assign to collections of...
Barnard exact test - The Barnard exact test is an unconditional exact procedure for comparing the success probabilities of two independent binomial samples, represented by a...
Bartlett's test - Bartlett's test is a statistical test of whether several independent normal populations have the same variance.
Base model - A base model is a previously trained model used as the starting point for further adaptation, especially fine-tuning for a particular task or application.
Bayes classifier - A Bayes classifier is a decision rule that minimizes expected classification error when the true distribution of inputs and class labels is known and every...
Bayes error rate - The Bayes error rate is the smallest possible expected misclassification rate for a specified joint distribution of observed features and class labels, when...
Bayes estimator - A Bayes estimator is an estimation rule that minimizes expected loss under a specified probability model and loss function.
Bayes linear statistics - Bayes linear statistics is an approach to representing and revising uncertainty using expectations, variances, and covariances rather than requiring a fully...
Bayes-Nash equilibrium - A Bayes-Nash equilibrium, also called a Bayesian Nash equilibrium, is a profile of strategies in a game with incomplete information such that each player's...
Bayes theorem - Bayes theorem, usually written Bayes' theorem, is a probability identity relating the probability of a hypothesis after observing evidence to its prior...
Bayesian experimental design - Bayesian experimental design selects an experiment by maximizing expected utility under a probabilistic model of unknown quantities and possible observations.
Bayesian game - A Bayesian game is a game-theoretic model in which players have incomplete information about relevant features of the interaction, such as another player's...
Bayesian history matching - Bayesian history matching is an uncertainty-aware approach to identifying simulator input settings that have not been ruled out as inconsistent with...
Bayesian inference - Bayesian inference is the process of updating probability distributions for unknown quantities using observed data and a specified statistical model.
Bayesian learning - Bayesian learning is an approach to learning from data that updates a probability distribution over hypotheses or model parameters using Bayes' theorem.
Bayesian model reduction - Bayesian model reduction is a method for obtaining evidence and posterior quantities for alternative probabilistic models from an already fitted parent...
Bayesian network - A Bayesian network is a probabilistic model represented by a directed acyclic graph and a conditional probability distribution for each variable given its...
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 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 Regression - Bayesian regression models an outcome conditional on predictors while assigning prior distributions to unknown model parameters and updating them with...
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 survival analysis - Bayesian survival analysis models time until a defined event using probability distributions and Bayesian updating, while accounting for incomplete...
Bayesian vector autoregression - Bayesian vector autoregression is a Bayesian approach to modeling several time series jointly as functions of their past values.
Behrens - Fisher problem - The Behrens - Fisher problem concerns inference about the difference between two normal-population means using independent samples when the population...
Belief aggregation - Belief aggregation combines probability assessments from several people, models, or other sources into a single probability distribution.
Bellman filter - The Bellman filter is an approximate state-estimation method developed from Bellman's dynamic-programming principle for state-space models.
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...
Berlin procedure - The Berlin procedure is a method for decomposing economic time series into estimated underlying and recurring components.
Bernoulli Naive Bayes - Bernoulli Naive Bayes is a supervised classification method for binary features, such as whether a word appears in a document.
Between-group design experiment - A between-group design experiment compares outcomes from separate groups exposed to different experimental conditions.
Betweensubjects designs - Between-subjects designs are study arrangements in which different participants experience the different levels of a factor.
Bimodal - Bimodal describes a distribution with two distinct peaks, or modes. Values tend to concentrate around two regions rather than one.
Binary cross-entropy - Binary cross-entropy, often abbreviated BCE, is a loss function for assessing predicted probabilities of a binary outcome.