💡 Words with a Similar Meaning to "Neural network gaussian process"
Found via reverse dictionary — words that share a conceptual meaning.
| Word | Definition |
|---|---|
| approximate bayesian computation | Approximate Bayesian computation constitutes a class of computational methods rooted in Bayesian statistics that can be used to estimate the posterior distributions of model parameters. |
| bayesian structural time series | Bayesian structural time series model is a statistical technique used for feature selection, time series forecasting, nowcasting, inferring causal impact and other applications. |
| bayesian networknoun | (statistics) A directed acyclic graph whose vertices represent random variables and whose directed edges represent conditional dependencies. Each random variable can fall into any of at least two mutually disjoint states, and has a probability function which takes as inputs the states of its parent nodes and returns as output the probability of being in a certain state for a given combination of the states of its parent nodes. A node without parent nodes just has an unconditioned probability of being in some given state. |
| dynamic causal modeling | a framework for specifying models, fitting them to data and comparing their evidence using Bayesian model comparison. |
| bayesian inference | (or ) a method of statistical inference in which Bayes' theorem is used to calculate a probability of a hypothesis, given prior evidence, and update it as more information becomes available. |
| bayesian hierarchical modeling | Bayesian hierarchical modelling is a statistical model written in multiple levels (hierarchical form) that estimates the parameters of the posterior distribution using the Bayesian method. |
| empirical bayes method | Empirical Bayes methods are procedures for statistical inference in which the prior probability distribution is estimated from the data. |
| bayesian statistics | (or ) a theory in the field of statistics based on the Bayesian interpretation of probability, where probability expresses a degree of belief in an event. |
| markov logic network | A Markov logic network is a probabilistic logic which applies the ideas of a Markov network to first-order logic, defining probability distributions on possible worlds on any given domain . |
| probabilistic programming | a programming paradigm in which probabilistic models are specified and inference for these models is performed automatically. |
| dynamic bayesian network | A dynamic Bayesian network is a Bayesian network which relates variables to each other over adjacent time steps. |
| bayesian experimental design | Bayesian experimental design provides a general probability-theoretical framework from which other theories on experimental design can be derived. |
| probabilistic logic | Probabilistic logic (also probability logic and probabilistic reasoning) involves the use of probability and logic to deal with uncertain situations. |
| bayesian game | In game theory, a Bayesian game is a strategic decision-making model which assumes players have incomplete information. |
| posterior predictive distribution | In Bayesian statistics, the posterior predictive distribution is the distribution of possible unobserved values conditional on the observed values. |
| probabilistic neural network | A probabilistic neural network is a feedforward neural network, which is widely used in classification and pattern recognition problems. |
| bayesian epistemology | a formal approach to various topics in epistemology that has its roots in Thomas Bayes' work in the field of probability theory. |
| likelihood function | A likelihood function (often simply called the likelihood) measures how well a statistical model explains observed data by calculating the probability of seeing that data under different parameter values of the model. |
| bayesian vector autoregression | In statistics and econometrics, Bayesian vector autoregression uses Bayesian methods to estimate a vector autoregression model. |
| nested sampling algorithm | The nested sampling algorithm is a computational approach to the Bayesian statistics problems of comparing models and generating samples from posterior distributions. |
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