Approximate inference information

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...

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

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

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

and of training itself is often computationally infeasible and approximate inference and learning methods are used. For example, the problem of translating...

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

such as variational autoencoders. Active inference applies the techniques of approximate Bayesian inference to infer the causes of sensory data from a...

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

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

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...

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

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

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

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...

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

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

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

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

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

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

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

Gibbs sampling highly resembles that of the coordinate ascent variational inference in that both algorithms utilize the full-conditional distributions in...

algebraic systems in vision and learning, primal/dual optimization for approximate inference in MRF and Graphical models, and (since 2014) deep layered networks...

called amortized inference. All in all, we have found a problem of variational Bayesian inference. A basic result in variational inference is that minimizing...

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