Approximation of Bayesian Predictive p-Values with Regression ABC

DR-ABC: Approximate Bayesian Computation with Kernel-Based Distribution Regression

Performing exact posterior inference in complex generative models is often difficult or impossible due to an expensive to evaluate or intractable likelihood function. Approximate Bayesian computation (ABC) is an inference framework that constructs an approximation to the true likelihood based on the similarity between the observed and simulated data as measured by a predefined set of summary st...

Prior-Posterior Predictive P-values

Controlling false discoveries in Bayesian gene networks with lasso regression p-values

P-values are being computed for increasingly complicated statistics but lacking evaluations on their quality. Meanwhile, accurate p-values enable significance comparison across batches of hypothesis tests and consequently unified false discover rate (FDR) control. This article discusses two related questions in this setting. First, we propose statistical tests to evaluate the quality of p-value...

P-Values for High-Dimensional Regression

Assigning significance in high-dimensional regression is challenging. Most computationally efficient selection algorithms cannot guard against inclusion of noise variables. Asymptotically valid p-values are not available. An exception is a recent proposal by Wasserman and Roeder (2008) which splits the data into two parts. The number of variables is then reduced to a manageable size using the f...

Linear regression based Bayesian predictive classification for speech recognition

The uncertainty in parameter estimation due to the adverse environments deteriorates the classification performance for speech recognition. It becomes crucial to incorporate the parameter uncertainty into decision so that the classification robustness can be assured. In this paper, we propose a novel linear regression based Bayesian predictive classification (LRBPC) for robust speech recognitio...