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...
Posterior probability - Posterior probability is probability conditional on specified evidence after updating a prior probability through a model.
Posterior sampling - Posterior sampling generates draws from, or approximately from, a posterior probability distribution.
Postings - Postings are the records in an inverted search index that associate an indexed term with documents containing it. A basic posting identifies a document.
Postposition - A postposition is an adposition that follows the noun phrase it accompanies.
Potential field - A potential field is a scalar function over positions or robot configurations that guides motion through its slope.
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 diagram - A Potts diagram is a visualization of a word's association with ordered rating categories, such as review scores.
Potts model - A Potts model is a model of interacting variables that each take one of several categorical states.
Powell's dog leg method - Powell's dogleg method is a trust-region optimization technique that selects a step along a two-segment path connecting a steepest-descent-based step to a...
Powell's method - Powell's method is a derivative-free optimization approach that improves a scalar objective through a sequence of one-dimensional searches along selected...
Power - In statistical hypothesis testing, power is the probability that a specified testing procedure rejects the null hypothesis under a particular alternative...
Power spectrum - A power spectrum describes how a signal's power or squared-amplitude content is distributed across frequencies.
PPO-Clip - PPO-Clip is the clipped-objective version of proximal policy optimization.
PPO-Penalty - PPO-Penalty is a proximal policy optimization variant that subtracts a penalty for KL divergence between the old and candidate policies from a...
Practical significance - Practical significance is the importance of an observed or estimated effect in its real-world context.
Pragmatic mapping - Pragmatic mapping is the linking of linguistic expressions with the situations, objects, and communicative contexts they concern. In John W.
Pragmatics - Pragmatics is the study of how people use language in context and how listeners infer what speakers intend to communicate.
Pre-processing algorithm - A pre-processing algorithm is a defined procedure that prepares or transforms data before a model uses it.
Pre-trained model - A pre-trained model is a model whose parameters have already been learned through an earlier training process before its use in a new workflow or task.
Pre-training - Pre-training is an earlier stage of model training that learns broadly useful patterns before the model is used directly or adapted to more specific tasks.
Precedence constraints - Precedence constraints specify required temporal relationships between tasks. In finish-to-start form, one task must finish before another may begin.
Precision - Precision is the fraction of a model's positive predictions that are actually positive according to the reference labels.
Precision at k (precision@k) - Precision at k, written precision@k or P@k, is the fraction of the first k items in a ranked result list that are relevant.
Precision-recall curve - A precision-recall curve shows how a classifier's precision and recall change as its decision threshold varies.
Precomputation - Precomputation means calculating information before the moment it is needed, then storing it for later reuse.
Precomputed Features - Precomputed features are input variables calculated in advance and stored, so that training or serving reads them ready-made instead of recomputing them...
Precondition - A precondition is a condition that must hold for an action to be applicable in a specified model.
Precondition axioms - Precondition axioms are logical statements requiring an action's preconditions to hold whenever that action occurs in a planning model.
Preconditioned stochastic gradient descent - Preconditioned stochastic gradient descent modifies a stochastic gradient with a matrix before using it to update model parameters.
Preconditioning - Preconditioning transforms a numerical problem into an equivalent or suitably related form that is easier for an iterative solver to handle.
Predicate (logic) - In logic, a predicate represents a property of an individual or a relation among individuals.
Prediction - A prediction is a model's answer for a case it was not told the answer to: the estimated price of a house, the class of an email, the demand for next month...
Prediction bias - Prediction bias is the difference between the average of a model's predictions and the average of the corresponding observed labels in a dataset.
Prediction by partial matching - Prediction by partial matching, or PPM, is an adaptive statistical approach to lossless compression that predicts the next symbol from preceding symbols.
Prediction interval - A prediction interval is a range constructed to cover a future observation, or another specified future quantity, at a stated probability or long-run...
Predictive analytics - Predictive analytics uses data and statistical or machine-learning methods to estimate future or otherwise unknown outcomes.
Predictive estimation - Predictive estimation is judging a model or a parameter choice by how well it predicts unseen data, rather than by how well it explains or fits the data...
Predictive learning - Predictive learning, in the world-model sense associated with Yann LeCun, learns from observations to predict aspects of an environment's future state.
Predictive ML - Predictive ML is the branch of machine learning that learns from historical examples to predict unknown outcomes: what will happen, which class a case...
Predictive modeling - Predictive modeling is the process of building and evaluating a model that estimates an unknown target from available information.
Predictive parity - Predictive parity is a group fairness criterion requiring equal precision, also called positive predictive value, across the groups being compared.
Predictive state representation - A predictive state representation, or PSR, represents the state of a controlled dynamical system using predictions about future observations under specified...
Predictive text - Predictive text is a text-entry feature that suggests words or phrases a person may want to type, using information such as entered characters and...
Preference alignment - Preference alignment is the process of adjusting an AI system's behavior to better match specified preferences about its outputs or actions.
Preference constraints - Preference constraints express which assignments are more desirable in a constraint problem, rather than simply declaring which assignments are permitted.
Preference elicitation - Preference elicitation is the process of obtaining information about what a person or group prefers so a system can support decisions on their behalf.