💡 Words with a Similar Meaning to "Probabilistic programming"
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. |
| probabilistic logic | Probabilistic logic (also probability logic and probabilistic reasoning) involves the use of probability and logic to deal with uncertain situations. |
| neural network gaussian process | A Neural Network Gaussian Process is a Gaussian process obtained as the limit of a certain type of sequence of neural networks. |
| probabilistic neural network | A probabilistic neural network is a feedforward neural network, which is widely used in classification and pattern recognition problems. |
| probabilistic classification | In machine learning, a probabilistic classifier is a classifier that is able to predict, given an observation of an input, a probability distribution over a set of classes, rather than only outputting the most likely class that the observation should belong to. |
| dynamic causal modeling | a framework for specifying models, fitting them to data and comparing their evidence using Bayesian model comparison. |
| 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 . |
| conformal prediction | a machine learning framework for uncertainty quantification that produces statistically valid prediction regions (prediction intervals) for any underlying point predictor (whether statistical, machine, or deep learning) only assuming exchangeability of the data. |
| 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. |
| algorithmic probability | In algorithmic information theory, algorithmic probability, also known as Solomonoff probability, is a mathematical method of assigning a prior probability to a given observation. |
| inductive logic programming | a subfield of symbolic artificial intelligence which uses logic programming as a uniform representation for examples, background knowledge and hypotheses. |
| feature learning | In machine learning, feature learning or representation learning is a set of techniques that allow a system to automatically discover the representations needed for feature detection or classification from raw data. |
| 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. |
| differentiable programming | a programming paradigm in which a numeric computer program can be differentiated throughout via automatic differentiation. |
| 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. |
| 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. |
| stochastic programming | In the field of mathematical optimization, stochastic programming is a framework for modeling optimization problems that involve uncertainty. |
| algorithmic paradigm | An algorithmic paradigm or algorithm design paradigm is a generic model or framework which underlies the design of a class of algorithms. |
| empirical bayes method | Empirical Bayes methods are procedures for statistical inference in which the prior probability distribution is estimated from the data. |
| predictive analytics | Predictive analytics encompasses a variety of statistical techniques from data mining, predictive modeling, and machine learning that analyze current and historical facts to make predictions about future or otherwise unknown events. |
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