Fast approximate inference for multivariate longitudinal data

Logistic Regression for Multivariate Longitudinal Ordinal Data

Dynamical Correlation for Multivariate Longitudinal Data

Nonparametric methodology for longitudinal data analysis is becoming increasingly popular. The analysis of multivariate longitudinal data, where data on several time courses are recorded per subject, has received considerably less attention, in spite of its importance for practical data analysis. In particular, there is a need for measures and estimates to capture dependency between the compone...

Constrained Kronecker Deltas for Fast Approximate Inference and Estimation

We are interested in constructing fast, principled algorithms for scaling up decoding and parameter estimation in probability models such as Hidden Markov Models (HMMs) and linear-chain Conditional Random Fields (CRFs) with large state spaces. In this paper we present a principled extension of beam search to beam inference and learning. We present a new approach for approximate inference based ...

Fast Convergent Algorithms for Expectation Propagation Approximate Bayesian Inference

We propose a novel algorithm to solve the expectation propagation relaxation of Bayesian inference for continuous-variable graphical models. In contrast to most previous algorithms, our method is provably convergent. By marrying convergent EP ideas from [15] with covariance decoupling techniques [23, 13], it runs at least an order of magnitude faster than the most common EP solver.

A New Nonparametric Regression for Longitudinal Data

In many area of medical research, a relation analysis between one response variable and some explanatory variables is desirable. Regression is the most common tool in this situation. If we have some assumptions for such normality for response variable, we could use it. In this paper we propose a nonparametric regression that does not have normality assumption for response variable and we focus ...