Gaussian Quadrature Based Expectation Propagation
نویسندگان
چکیده
منابع مشابه
On numerical approximation schemes for expectation propagation
Several numerical approximation strategies for the expectation-propagation algorithm are studied in the context of large-scale learning: the Laplace method, a faster variant of it, Gaussian quadrature, and a deterministic version of variational sampling (i.e., combining quadrature with variational approximation). Experiments in training linear binary classifiers show that the expectation-propag...
متن کاملElliptical Slice Sampling with Expectation Propagation
Markov Chain Monte Carlo techniques remain the gold standard for approximate Bayesian inference, but their practical issues — including onerous runtime and sensitivity to tuning parameters — often lead researchers to use faster but typically less accurate deterministic approximations. Here we couple the fast but biased deterministic approximation offered by expectation propagation with elliptic...
متن کاملExpectation Propagation for Rectified Linear Poisson Regression
The Poisson likelihood with rectified linear function as non-linearity is a physically plausible model to discribe the stochastic arrival process of photons or other particles at a detector. At low emission rates the discrete nature of this process leads to measurement noise that behaves very differently from additive white Gaussian noise. To address the intractable inference problem for such m...
متن کاملLoop corrections for message passing algorithms in continuous variable models
In this paper we derive the equations for Loop Corrected Belief Propagation on a continuous variable Gaussian model. Using the exactness of the averages for belief propagation for Gaussian models, a different way of obtaining the covariances is found, based on Belief Propagation on cavity graphs. We discuss the relation of this loop correction algorithm to Expectation Propagation algorithms for...
متن کاملStochastic Expectation Propagation for Large Scale Gaussian Process Classification
A method for large scale Gaussian process classification has been recently proposed based on expectation propagation (EP). Such a method allows Gaussian process classifiers to be trained on very large datasets that were out of the reach of previous deployments of EP and has been shown to be competitive with related techniques based on stochastic variational inference. Nevertheless, the memory r...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2005