Minimax decision - A minimax decision is an action selected because it leads to the best value the player can secure against optimal opposing play.
Minimax loss - Minimax loss, in the supplied glossary's generative-adversarial-network context, is the original GAN objective that a generator tries to minimize while a...
Minimax search - Minimax search is a game-tree search procedure that evaluates possible continuations by alternating maximizing and minimizing choices.
Minimax value - The minimax value of a game state is the utility obtained from that state when both players make optimal opposing choices in the specified game model.
Minimization (clinical trials) - Minimization in clinical trials is a sequential allocation method that seeks to keep treatment groups balanced on selected participant characteristics.
Minimum bounding rectangle - A minimum bounding rectangle is a smallest enclosing rectangle under a stated orientation constraint and size criterion.
Minimum chi-square estimation - Minimum chi-square estimation chooses model parameters to minimize a chi-square discrepancy between observed data and model predictions.
Minimum description length - Minimum description length, or MDL, is a principle for choosing models by how compactly they describe observed data.
Minimum edit distance - Minimum edit distance is the smallest total cost of editing operations needed to transform one sequence into another.
Minimum information standard - A minimum information standard specifies the essential information that should accompany a particular kind of study, dataset or result so that other people...
Minimum Message Length - Minimum Message Length is a model-selection principle from information theory: the best model is the one that gives the shortest total description of the...
Minimum norm estimation - Minimum norm estimation selects an estimate with the smallest specified norm among solutions satisfying a fitting criterion.
Minimum-remaining-values - Minimum-remaining-values, usually abbreviated MRV, is a variable-selection heuristic for constraint satisfaction.
Minimum slack - Minimum slack is a scheduling priority rule that favors the eligible activity or job with the least remaining timing flexibility.
Minimum spanning tree - A minimum spanning tree, or MST, connects every vertex of a connected, weighted, undirected graph using a subset of its edges, without cycles, while...
Minkowski distance - Minkowski distance is a family of distances between numerical vectors, defined for p≥1 by d_p(x,y)=(sum_i |x_i−y_i|^p)^(1/p).
Minority class - The minority class is the rare outcome in a classification dataset: the few fraud cases among millions of honest ones, the few churners among loyal...
Minors - A graph minor is a graph obtainable from another graph by deleting vertices, deleting edges, and contracting edges.
Mipmap - A mipmap is a precomputed sequence of progressively smaller, filtered versions of a texture image.
Mirror descent - Mirror descent is an optimization method that generalizes gradient descent by using a chosen geometry to control how far each update moves.
Mirrorless - Mirrorless describes a camera design that omits the reflex mirror used by an SLR to direct light from the taking lens into an optical viewfinder.
Missing data - Missing data are values that should be available for an observation but are absent, unknown, corrupted, or otherwise unusable.
Missing effect - A missing effect is a real consequence of an action that is absent from the agent's action model.
Missing precondition - A missing precondition is a necessary condition for an action that has been left out of the action's model.
Missing state variable - A missing state variable is a relevant aspect of the world that an agent's state representation does not include.
Missing values - Missing values are the empty cells in a dataset: measurements never taken, questions never answered, sensors that failed, customers who skipped a field...
Misuse detection - Misuse detection identifies activity by comparing it with descriptions of known unwanted or prohibited behavior.
Misuse of p-values - Misuse of p-values means calculating, selecting, or interpreting p-values in ways that support conclusions the statistical procedure does not justify.
Mitchell - Netravali - Mitchell - Netravali refers to a parameterized family of cubic reconstruction filters used to estimate image values between samples.
Mixed-data sampling - Mixed-data sampling, usually abbreviated MIDAS, is a regression approach for relating time series observed at different frequencies.
Mixed integer linear programming - Mixed integer linear programming, usually abbreviated MILP, is an optimization framework with a linear objective, linear constraints, and integer...
Mixed methods - Mixed methods is a research approach that deliberately integrates quantitative and qualitative evidence to answer a question.
Mixed model designs - Mixed model designs commonly refer to studies combining at least one between-subjects factor with at least one within-subjects, or repeated-measures, factor.
Mixed Precision - Mixed precision is the use of more than one numerical precision within a computation.
Mixed sampling - Mixed sampling is a sampling plan that combines two or more basic selection methods.
Mixed strategy - A mixed strategy is a probability distribution over a player's pure strategies in a game.
Mixing gradients - Mixing gradients is a Poisson image-editing technique that combines source and destination image structure by selecting stronger local variations from...
Mixing rate - Mixing rate describes how quickly a stochastic process loses the influence of its initial state and approaches its long-run distribution, under conditions...
Mixing time - Mixing time is the number of steps a Markov chain needs to bring its distribution sufficiently close to its stationary distribution, according to a...
Mixture density - A mixture density is a probability density formed by adding component densities with nonnegative weights that sum to one.
Mixture distribution - A mixture distribution is a probability distribution formed by combining component distributions with specified probabilities, called mixing weights.
Mixture experiment - A mixture experiment studies how the proportions of components in a blend affect a measured response.
Mixture of conjugate priors - A mixture of conjugate priors is a weighted combination of prior distributions that are individually conjugate to the same likelihood.
Mixture of experts - A mixture of experts, or MoE, is a model design that combines multiple component networks called experts through a gating or routing mechanism.
Mixture of factor analyzers - A mixture of factor analyzers is a probabilistic model that combines several factor-analysis components.
Mixture of Gaussians - A mixture of Gaussians is a probability distribution built from a weighted combination of Gaussian, or normal, distributions.
Mixture of subspaces - A mixture of subspaces is a modeling approach that treats high-dimensional data as points drawn from several lower-dimensional flat regions, each cluster...
Mobile business intelligence - Mobile business intelligence is the delivery and use of business data, reports, and analytical insights through mobile devices such as smartphones and tablets.