Dependency grammar - Dependency grammar is an approach to describing sentence structure through relationships between words, typically linking a head word to its dependents.
Dependency network (graphical model) - A dependency network is a probabilistic graphical model that describes each variable through a conditional distribution given selected other variables.
Dependency tree - A dependency tree represents the syntactic structure of a sentence through directed relations between its words.
Dependent Dirichlet process - A dependent Dirichlet process is a Bayesian construction for a collection of related random probability distributions indexed by predictors, groups, time...
Dependent variable - A dependent variable is the outcome or response a statistical analysis seeks to explain, compare, or predict.
Deployment - Deployment is the process of placing an AI system or component into its intended operating environment and making it available for use.
Deployment Bias - Deployment bias arises when the way an AI system is actually used differs from the role or context for which it was designed and evaluated, creating...
Depth - Depth is how many layers or levels a model has, counting from input to output, and more depth lets a model build more complicated ideas from simpler ones.
Depth First Search - Depth First Search, usually abbreviated DFS, is a graph or tree traversal strategy that explores one branch as far as it can before returning to explore...
Depth First Search on Edges - Depth First Search on Edges is a traversal that explores graph connections in depth-first order while tracking edges as the items to visit.
Depth image - A depth image is an image-shaped array whose values describe the depth of visible scene surfaces rather than their ordinary color or brightness.
Depth map - A depth map associates image locations with estimates or measurements of scene depth.
Depthwise separable convolutional neural network - A depthwise separable convolutional neural network is a convolutional network that uses depthwise separable convolutions as important building blocks.
Deriche edge detector - The Deriche edge detector is a classical image-edge method based on recursively implemented filters derived from edge-detection criteria related to Canny's...
Derivational morphemes - Derivational morphemes are meaningful word-building units that help form new lexical words from a base. They can change meaning, grammatical category or both.
Derivative-free optimization - Derivative-free optimization searches for good decision-variable values without requiring derivatives of the objective or constraints to be supplied.
Derived Relation - A derived relation is a relation obtained by applying an expression or query to other relations rather than treating its contents as independently supplied...
Description logic - Description logic is a family of formal knowledge-representation languages for describing classes of things, their relationships and individual instances.
Descriptive analytics - Descriptive analytics summarizes recorded data to show what has happened or what is currently observed.
Descriptive (or summary) statistics - Descriptive, or summary, statistics organize and condense observed data so that their main features are easier to understand.
Design effect - A design effect is the ratio of an estimator's variance under a specified sampling design to its variance under a reference simple random sampling design of...
Design of experiments - Design of experiments is the planned organization of interventions, experimental units and measurements to answer a defined question efficiently and credibly.
Detailed balance - Detailed balance is a pairwise flow condition for a Markov chain and a probability distribution.
Detectable difference - A detectable difference is a true population effect that would give a specified statistical test a chosen power under stated design and modeling assumptions.
Detection Bias - Detection bias is a systematic distortion caused by differences in how outcomes are noticed, measured, or classified across groups.
Detection error tradeoff - Detection error tradeoff describes the relationship between false alarms and missed detections as a binary detector's decision threshold changes.
Determinant - The determinant is a scalar associated with a square matrix.
Determiner - A determiner is a grammatical word associated with a noun phrase that helps express how its referent should be understood in context.
Deterministic algorithm - A deterministic algorithm follows rules that produce the same result whenever it starts with the same complete inputs and state.
Deterministic annealing - Deterministic annealing is an optimization method that starts by treating the problem as soft and blurred, then gradually sharpens it, so the search avoids...
Deterministic global optimization - Deterministic global optimization uses systematic mathematical procedures to seek a globally best feasible solution and establish an optimality guarantee...
Deterministic imputation - Deterministic imputation, in the statistical-editing sense used by the OECD glossary, fills a missing or designated field when the other accepted values and...
Deterministic nodes - Deterministic nodes are variables in a graphical model whose values are fixed functions of their parent variables.
Deterministic parser - A deterministic parser follows a single parsing course rather than maintaining several competing analyses at each decision.
Deterministic policy gradient - A deterministic policy gradient is the derivative used to improve a policy that chooses a specific continuous action for each state.
Deterministic Sampling - Deterministic sampling selects records or evaluation points using a specified rule that produces the same selection when its inputs and settings are unchanged.
Deterministic training conditional approximation - The deterministic training conditional approximation, or DTC, is a sparse Gaussian process approximation that makes training function values deterministic...
Detrended correspondence analysis - Detrended correspondence analysis, or DCA, is an ordination method developed to summarize ecological community data while modifying certain patterns...
Development set - A development set is a collection of examples used to guide choices while building a machine-learning system.
Developmental robotics - Developmental robotics studies how robots can acquire abilities progressively through experience, interaction and learning, drawing inspiration from human...
Deviance information criterion - The deviance information criterion, or DIC, is a Bayesian model-comparison measure that combines posterior average fit with an estimate of effective model...
Deviance (statistics) - Deviance is a likelihood-based measure of how much a fitted statistical model falls short of a saturated model that can fit the observed responses exactly.
Deviation scores - Deviation scores express observations as signed distances from their mean. For an observation x and arithmetic mean x-bar, its deviation score is x - x-bar.
Diagnosis - In this machine-learning context, diagnosis means investigating a trained model's performance by comparing training error, validation error and baselines.
Diagnostic analytics - Diagnostic analytics investigates observed outcomes to identify plausible explanations, contributing factors, and problems requiring further examination.
Diagnostic checking - Diagnostic checking is the examination of a fitted model for signs that its assumptions or structure are inadequate for the intended use.