Variational Bayes survival analysis for unemployment modelling

Constrained linear basis sets for HRF modelling using Variational Bayes.

FMRI modelling requires flexible haemodynamic response function (HRF) modelling, with the HRF being allowed to vary spatially and between subjects. To achieve this flexibility, voxelwise parameterised HRFs have been proposed; however, inference on such models is very slow. An alternative approach is to use basis functions allowing inference to proceed in the more manageable General Linear Model...

Modeling Factors Affecting Unemployment Duration Using Survival Analysis

Abstract. In many statistical studies‎, ‎we are faced with variables such as duration of unemployment‎, ‎duration of illness to death‎, ‎the lifespan of a piece‎, ‎duration of approval of a bill in the parliament‎, ‎and other similar cases‎. ‎In such studies‎, ‎the aim is to find and model the sources that affect the duration of the study period to control the mentioned period by identifying an...

Auto-Encoding Variational Bayes

How can we perform efficient inference and learning in directed probabilistic models, in the presence of continuous latent variables with intractable posterior distributions, and large datasets? We introduce a stochastic variational inference and learning algorithm that scales to large datasets and, under some mild differentiability conditions, even works in the intractable case. Our contributi...

Streaming Variational Bayes

Overview • Large, streaming data sets are increasingly the norm • Inference for Big Data has generally been non-Bayesian • Advantages of Bayes: complex models, coherent treatment of uncertainty, etc. We deliver: • SDA-Bayes, a framework for Streaming, Distributed, Asynchronous Bayesian inference • Experiments demonstrating streaming topic discovery with comparable predictive performance to non-...

Chapter 24 : Variational Bayes