A Linear Approximation Method for Probabilistic Inference
An approximation method is presented for probabilistic inference with continuous random variables. These problems can arise in many practical problems, in particular where there are "second order" probabilities. The approximation, based on the Gaussian influence diagram, iterates over linear approximations to the inference problem.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Quantifying the probable approximation error of probabilistic inference programs
This paper introduces a new technique for quantifying the approximation error of a broad class of probabilistic inference programs, including ones based on both variational and Monte Carlo approaches. The key idea is to …
Optimal DR-Submodular Maximization and Applications to Provable Mean Field Inference
Mean field inference in probabilistic models is generally a highly nonconvex problem. Existing optimization methods, e.g., coordinate ascent algorithms, can only generate local optima. In this work we propose provable …
Neural Network Approximators for Marginal MAP in Probabilistic Circuits
Probabilistic circuits (PCs) such as sum-product networks efficiently represent large multi-variate probability distributions. They are preferred in practice over other probabilistic representations such as Bayesian and …
Barron-Wiener-Laguerre models
We propose a probabilistic extension of Wiener-Laguerre models for causal operator learning. Classical Wiener-Laguerre models parameterize stable linear dynamics using orthonormal Laguerre bases and apply a static nonlin…
Bayesian InferenceStructured Inference Networks for Nonlinear State Space Models
Gaussian state space models have been used for decades as generative models of sequential data. They admit an intuitive probabilistic interpretation, have a simple functional form, and enjoy widespread adoption. We intro…
Multivariate Time Series ForecastingState Space Models