Variational Message Passing for Elaborate Response Regression Models
نویسندگان
چکیده
منابع مشابه
Non-conjugate Variational Message Passing for Multinomial and Binary Regression
Variational Message Passing (VMP) is an algorithmic implementation of the Variational Bayes (VB) method which applies only in the special case of conjugate exponential family models. We propose an extension to VMP, which we refer to as Non-conjugate Variational Message Passing (NCVMP) which aims to alleviate this restriction while maintaining modularity, allowing choice in how expectations are ...
متن کاملVariational Message Passing
Bayesian inference is now widely established as one of the principal foundations for machine learning. In practice, exact inference is rarely possible, and so a variety of approximation techniques have been developed, one of the most widely used being a deterministic framework called variational inference. In this paper we introduce Variational Message Passing (VMP), a general purpose algorithm...
متن کاملStein Variational Message Passing for Continuous Graphical Models
We propose a novel distributed inference algorithm for continuous graphical models, by extending Stein variational gradient descent (SVGD) (Liu & Wang, 2016) to leverage the Markov dependency structure of the distribution of interest. Our approach combines SVGD with a set of structured local kernel functions defined on the Markov blanket of each node, which alleviates the curse of high dimensio...
متن کاملd-VMP: Distributed Variational Message Passing
Motivated by a real-world financial dataset, we propose a distributed variational message passing scheme for learning conjugate exponential models. We show that the method can be seen as a projected natural gradient ascent algorithm, and it therefore has good convergence properties. This is supported experimentally, where we show that the approach is robust wrt. common problems like imbalanced ...
متن کاملApproximate Message Passing for Bilinear Models
Approach: We take a Bayesian approach to the inference problems (in particular, posterior estimation) that revolve around the bilinear model (1). In particular, we leverage the approximate message passing (AMP) framework of [2], [3] and extend it to the bilinear domain. Compared to Bayesian approaches that rely on Gibbs sampling methods or variational inference, the AMP framework allows us to f...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Bayesian Analysis
سال: 2019
ISSN: 1936-0975
DOI: 10.1214/18-ba1098