Open-universe probability model - An open-universe probability model represents uncertainty about which objects exist, how many there are, and how observations refer to them.
Open world classification - Open world classification recognizes examples from known categories, allows unfamiliar examples to remain unknown, and supports adding newly established...
Ordinal probit regression - Ordinal probit regression models an ordered categorical outcome using a latent linear score with normally distributed noise.
Outlier exposure - Outlier exposure is a training approach that uses an auxiliary dataset of out-of-distribution examples to improve a model's ability to recognize unfamiliar...
Parallel tempering - Parallel tempering is a Monte Carlo sampling method that runs several related chains at different temperatures and occasionally proposes exchanges of their...
Partial least squares regression - Partial least squares regression, or PLS regression, predicts one or more responses using a smaller set of components constructed from the predictors with...
Partial regression - Partial regression examines the linear relationship between an outcome and a predictor after removing the linear contributions of other specified predictors.
Particle path degeneracy - Particle path degeneracy is the loss of diversity in the earlier parts of trajectories represented by a particle filter.
Periodic kernel - A periodic kernel is a covariance function that represents repeating relationships in a Gaussian process or another suitable kernel model.
Persistent contrastive divergence - Persistent contrastive divergence, or PCD, is a learning method for energy-based models that maintains sampling chains across parameter updates.
Perturb-and-MAP sampling - Perturb-and-MAP sampling generates random configurations by perturbing an objective or energy function and then optimizing the perturbed function.
Poisson regression - Poisson regression models a nonnegative count outcome using a Poisson distribution whose mean depends on predictors.
Policy gradient theorem - The policy gradient theorem expresses how an agent's expected cumulative reward changes when the parameters of its action-selection policy change.
Polyak-Ruppert averaging - Polyak-Ruppert averaging is a stochastic-approximation technique that averages successive parameter estimates rather than returning only the last estimate.
Posterior predictive check - A posterior predictive check compares observed data with replicated data generated from a fitted Bayesian model.
Posterior predictive p-value - A posterior predictive p-value is a tail probability comparing a chosen feature of replicated data from a fitted Bayesian model with the corresponding...
Potential scale reduction factor - The potential scale reduction factor, commonly associated with R-hat, is a family of diagnostics for comparing multiple Markov chain Monte Carlo runs.
Potts model - A Potts model is a model of interacting variables that each take one of several categorical states.
Preconditioned stochastic gradient descent - Preconditioned stochastic gradient descent modifies a stochastic gradient with a matrix before using it to update model parameters.
Probabilistic circuit - A probabilistic circuit represents a probability distribution through a computational graph of simple functions, weighted sums, and products.
Probability integral transform - The probability integral transform maps a random variable through its own cumulative distribution function.
Probability of improvement - Probability of improvement is an acquisition criterion used in Bayesian optimization.
Prototypical network - A prototypical network is a few-shot classification method that learns an embedding space and represents each class by the mean embedding of its labeled...
Quasi-Monte Carlo integration - Quasi-Monte Carlo integration estimates an integral by averaging a function over deliberately well-distributed points rather than independent random points.
Query by committee - Query by committee is an active-learning strategy that selects examples for labeling by examining disagreement among several plausible models.
RAND-WALK word embedding model - RAND-WALK is a latent-variable model of word generation that helps explain the structure of word embeddings learned from co-occurrence statistics.
Random-walk Metropolis - Random-walk Metropolis is a Markov chain Monte Carlo algorithm that proposes a new state by adding a random increment to the current state.
Randomized prior network - A randomized prior network is a randomly initialized neural network whose parameters remain fixed and whose output is added to a trainable model.
Rational quadratic kernel - The rational quadratic kernel is a covariance function that combines smooth relationships across a range of length scales.
Rauch-Tung-Striebel smoother - The Rauch-Tung-Striebel smoother is a backward-recursion algorithm that refines past state estimates using observations from an entire time interval.
Recurrent state space model - A recurrent state space model is a learned sequence model that combines a deterministic recurrent memory with a stochastic latent state.
Recursive least squares - Recursive least squares is an algorithm for updating a least-squares parameter estimate as new observations arrive.
Reference prior - A reference prior is a prior distribution constructed by a formal information-based procedure to provide a baseline Bayesian analysis for a specified...
Regularization path - A regularization path is the sequence of fitted model solutions obtained as the strength of a regularization penalty varies.
Regularized discriminant analysis - Regularized discriminant analysis is a classification approach that stabilizes estimated class covariance matrices through shrinkage.
REINFORCE - REINFORCE is a family of stochastic gradient methods that adjusts a policy using sampled rewards or returns multiplied by the gradient of the log...
Representational similarity analysis - Representational similarity analysis compares how different systems distinguish the same set of items or conditions.
Residual flow - A residual flow is a normalizing-flow model built from invertible residual transformations of the form f(x) = x + g(x).
Riemann manifold Hamiltonian Monte Carlo - Riemann manifold Hamiltonian Monte Carlo is an MCMC method that uses a position-dependent metric to adapt Hamiltonian sampling dynamics to local features of...
Robust logistic regression - Robust logistic regression refers to extensions of logistic regression designed to reduce sensitivity to specified departures from ordinary modeling...
Scale-invariant prior - A scale-invariant prior is a prior measure for a positive scale parameter that retains its form when measurement units change multiplicatively.
Score-based generative model - A score-based generative model learns gradients of log probability density with respect to the data and uses those gradients in a sampling process to create...
Score function estimator - A score function estimator estimates the derivative of an expected outcome by multiplying sampled outcomes by the parameter gradient of their log probability.
Score matching - Score matching is a method for fitting a continuous probability model by matching its gradient of log density with respect to the data to that of the data...
Self-normalized importance sampling - Self-normalized importance sampling estimates an expectation under a target distribution by drawing from a proposal distribution, weighting the draws, and...
Semi-amortized variational inference - Semi-amortized variational inference combines an inference network's fast prediction of variational parameters with additional optimization for each...
Semisupervised variational autoencoder - A semisupervised variational autoencoder is a latent-variable generative model trained using both labeled and unlabeled observations.