Expectation propagation in the large data limit

Stochastic 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...

Approximate Expectation Propagation for Bayesian Inference on Large-scale Problems

where k indexes experimental replicates, i indexes the probe positions, j indexes the binding positions, andN ( jPj aji jjsjbj; i) represents the probability density function of a Gaussian distribution with mean Pj aji jjsjbj and variance i. We assign prior distributions on the binding event bj and the binding strength sj: p(bjj j) = bj j (1 j)1 bj (3) p0(sj) = Gamma(sjjc0; d0) (4) where Gamma(...

Expectation propagation for large scale Bayesian inference of non-linear molecular networks from perturbation data

Inferring the structure of molecular networks from time series protein or gene expression data provides valuable information about the complex biological processes of the cell. Causal network structure inference has been approached using different methods in the past. Most causal network inference techniques, such as Dynamic Bayesian Networks and ordinary differential equations, are limited by ...

Improving on Expectation Propagation

A series of corrections is developed for the fixed points of Expectation Propagation (EP), which is one of the most popular methods for approximate probabilistic inference. These corrections can lead to improvements of the inference approximation or serve as a sanity check, indicating when EP yields unrealiable results.

Buoyancy-driven crack propagation: the limit of large fracture toughness