Forward filtering backward sampling - Forward filtering backward sampling (FFBS) is a method for drawing complete hidden-state sequences from their posterior distribution given observed data.
Forward-mode automatic differentiation - Forward-mode automatic differentiation computes derivatives by carrying derivative information alongside values as a program evaluates a function.
Forward stagewise additive modeling - Forward stagewise additive modeling builds a predictive function by adding one fitted component at a time while leaving previously added components fixed.
Free bits objective - The free bits objective is a modification of a variational autoencoder training objective that stops rewarding further reductions in a latent group's KL...
Fully independent training conditional approximation - The fully independent training conditional approximation (FITC) is a sparse Gaussian process regression approximation that makes training function values...
g-computation - G-computation estimates outcomes under specified interventions by applying the g-formula to observed-data relationships.
Gaussian discriminant analysis - Gaussian discriminant analysis (GDA) is a classification approach that models the feature distribution within each class as Gaussian and combines those...
Gaussian-Gaussian model - A Gaussian-Gaussian model combines a Gaussian observation model with a Gaussian prior for an unknown mean or related continuous quantity.
Gaussian scale mixture - A Gaussian scale mixture is a probability distribution formed by combining Gaussian distributions that share a location but vary in scale.
Generalized advantage estimation - Generalized advantage estimation, or GAE, is a reinforcement learning method for estimating how much better an action was than the value predicted for its...
Generalized belief propagation - Generalized belief propagation, or GBP, is a family of probabilistic inference methods that exchange messages between regions containing groups of variables.
Generalized expectation maximization - Generalized expectation maximization, usually called generalized EM or GEM, is a version of expectation maximization that improves the expected...
Generalized linear mixed model - A generalized linear mixed model, or GLMM, combines a generalized linear response model with random effects that represent variation among groups or other...
Gibbs kernel - The Gibbs kernel is a Gaussian-process covariance function that allows the characteristic length scale to vary with the input location.
Graphite graph generative model - Graphite is a graph generative modeling framework that combines latent node representations with an iterative graph-neural-network decoder.
Hamiltonian Monte Carlo - Hamiltonian Monte Carlo, or HMC, is a Markov chain Monte Carlo method for sampling from continuous probability distributions using gradients of their log...
Hard attention - Hard attention is an attention mechanism that makes a discrete selection of input information, such as choosing one image region or a small set of positions...
Hard expectation maximization - Hard expectation maximization, or hard EM, fits a latent-variable model by replacing uncertainty about hidden variables with selected assignments and then...
Harmonic mean estimator - The harmonic mean estimator is a method for estimating a Bayesian model's marginal likelihood from posterior samples by taking the harmonic mean of their...
Hidden semi-Markov model - A hidden semi-Markov model, or HSMM, is a probabilistic model for sequences in which unobserved states can have explicitly modeled durations.
Hierarchical Bayesian model - A hierarchical Bayesian model describes related quantities through multiple connected levels of probability distributions.
Hierarchical softmax - Hierarchical softmax represents a probability distribution over many output classes by arranging them as leaves of a tree and multiplying conditional branch...
Hierarchical variational autoencoder - A hierarchical variational autoencoder is a variational autoencoder whose generative model contains multiple dependent layers or groups of latent variables.
Hierarchical variational posterior - A hierarchical variational posterior is an approximate posterior distribution constructed with additional layers of random variables or randomized...
Higher-order Markov model - A higher-order Markov model predicts the next state of a sequence using more than one preceding state.
Horseshoe distribution - The horseshoe distribution is a continuous probability distribution commonly used as a Bayesian shrinkage prior for sparse signals or coefficients.
Hybrid generative-discriminative training - Hybrid generative-discriminative training combines learning about the distribution of data with learning to predict a target from inputs.
Implicit neural network layer - An implicit neural network layer defines its output through conditions that the output must satisfy, such as an equation, fixed point or optimization problem.
Importance-weighted evidence lower bound - An importance-weighted evidence lower bound is a variational objective formed by taking the expected logarithm of an average of importance weights.
Independence Metropolis sampler - An independence Metropolis sampler is a Metropolis - Hastings algorithm that proposes each candidate from a fixed distribution independent of the chain's...
Inducing point approximation - An inducing point approximation is a method for making Gaussian process inference more manageable by representing important function information through a...
InfoNCE lower bound - The InfoNCE lower bound is a relationship connecting a particular contrastive-learning objective to mutual information.
Information arc in an influence diagram - An information arc in an influence diagram is an arrow into a decision node showing that the source variable's value is available when that decision is made.
Informative vector machine - The informative vector machine (IVM) is a sparse Gaussian process method that builds a limited active set of training examples using information-based...
InfoVAE - InfoVAE is a family of variational-autoencoder training objectives designed to give more explicit control over data modeling, latent-variable information...
Invariant risk minimization - Invariant risk minimization (IRM) is a learning principle that seeks a representation on which the same predictor is optimal across multiple training...
Inverse autoregressive flow - An inverse autoregressive flow (IAF) is an invertible transformation used to turn a simple probability distribution into a more flexible one.
Inverse transform sampling - Inverse transform sampling generates a random value by applying a distribution's quantile function to a uniform random number.
Ising model - The Ising model is a probability model for interacting binary variables, traditionally represented as spins taking values −1 or +1.
Iterated extended Kalman filter - The iterated extended Kalman filter refines an extended Kalman filter's measurement update by repeatedly linearizing the observation model around an...
Iterative soft thresholding algorithm - The iterative soft thresholding algorithm, often called ISTA or iterative shrinkage-thresholding, solves certain optimization problems by alternating a...
Jacobian-vector product - A Jacobian-vector product, or JVP, multiplies a function's Jacobian at a chosen input by an input-direction vector.
KL annealing - KL annealing is a training strategy that changes the weight of a Kullback - Leibler divergence term over time.
Knowledge gradient acquisition - A knowledge gradient acquisition function scores a possible measurement by the expected improvement it would produce in the value of the best final decision.
Label bias problem - The label bias problem is a limitation of certain locally normalized sequence models: each state's outgoing transition probabilities must sum to one, which...
Laplace approximation - A Laplace approximation replaces a probability density near a smooth mode with a Gaussian whose center and curvature match that local peak.
Latent coincidence analysis - Latent coincidence analysis (LCA) is a supervised latent-variable method for learning a linear transformation in which distances reflect labeled similarity...
LDA-HMM - LDA-HMM is a composite text model combining latent Dirichlet allocation's document-level topics with a hidden Markov model's local sequence structure.