Approximate inference methods make it possible to learn realistic models from big data by trading off computation time for accuracy, when exact learning...

that they support in polynomial time. Since the cost of inference may be very high, approximate algorithms have been developed. They either compute subsets...

epidemiology, and phylogeography. Approximate Bayesian computation can be understood as a kind of Bayesian version of indirect inference. Several efficient Monte...

approximate probabilistic inference to within an absolute error ɛ < 1/2. Second, they proved that no tractable randomized algorithm can approximate probabilistic...

accuracy of its predictions. This principle approximates an integration of Bayesian inference with active inference, where actions are guided by predictions...

I. Jordan Latent Dirichlet allocation, variational methods for approximate inference, expectation-maximization algorithm University of California, Berkeley...

Already in the original paper, the authors noted that "Learned approximate inference can be performed by training an auxiliary network to predict z {\displaystyle...

Bayesian inference (/ˈbeɪziən/ BAY-zee-ən or /ˈbeɪʒən/ BAY-zhən) is a method of statistical inference in which Bayes' theorem is used to calculate a probability...

Statistical inference is the process of using data analysis to infer properties of an underlying probability distribution. Inferential statistical analysis...

unsuitable for formal modeling. Approximate Bayesian computation can be understood as a kind of Bayesian version of indirect inference. Given a dataset of real...

generalized linear model Breslow, N. E.; Clayton, D. G. (1993), "Approximate Inference in Generalized Linear Mixed Models", Journal of the American Statistical...

variables, the processes of model training and inference are often computationally infeasible, so approximate inference and learning methods are used. An example...

propagation is used when an approximate solution is needed instead of the exact solution. It is an approximate inference. Cutset conditioning: Used with...

Abductive reasoning (also called abduction, abductive inference, or retroduction) is a form of logical inference that seeks the simplest and most likely conclusion...

Wang, Jue (2017). "Scalable learning and inference in Markov logic networks". International Journal of Approximate Reasoning. 82: 39–55. doi:10.1016/j.ijar...

a good model for the data is central in Bayesian inference. In most cases, models only approximate the true process, and may not take into account certain...

Variational message passing (VMP) is an approximate inference technique for continuous- or discrete-valued Bayesian networks, with conjugate-exponential...

Wenzel; Matthäus Deutsch; Théo Galy-Fajou; Marius Kloft; ”Scalable Approximate Inference for the Bayesian Nonlinear Support Vector Machine” Ferris, Michael...

expectations and approximate the expected sufficient statistics by using Markov chain Monte Carlo (MCMC). This approximate inference, which must be done...

license) libDAI: C++ library that provides implementations of various (approximate) inference methods for discrete graphical models; supports arbitrary factor...

Shakir; Wierstra, Daan (2014-06-18). "Stochastic Backpropagation and Approximate Inference in Deep Generative Models". International Conference on Machine...

single framework. Its inference system corresponds to a set of fuzzy IF–THEN rules that have learning capability to approximate nonlinear functions. Hence...

Gibbs sampling is commonly used as a means of statistical inference, especially Bayesian inference. It is a randomized algorithm (i.e. an algorithm that makes...

Bayesian methods are a family of techniques for approximating intractable integrals arising in Bayesian inference and machine learning. They are typically used...

also prominent in the formalisation of variational methods for approximate inference and the popularisation of the expectation–maximization algorithm...

a probability is assigned to a hypothesis, whereas under frequentist inference, a hypothesis is typically tested without being assigned a probability...

arXiv:math/0406301. Globerson, Amir; Jaakkola, Tommi (2007). "Approximate inference using planar graph decomposition" (PDF). Advances in Neural Information...

Raha, Arnab; Raghunathan, Vijay (2023-07-24). "Energy-Efficient Approximate Edge Inference Systems". ACM Transactions on Embedded Computing Systems. 22 (4):...

prediction, statistical syllogism, argument from analogy, and causal inference. There are also differences in how their results are regarded. A generalization...

Integrated nested Laplace approximations (INLA) is a method for approximate Bayesian inference based on Laplace's method. It is designed for a class of models...