Proposals which speed up function-space MCMC

MCMC Methods for Sampling Function Space

Applied mathematics is concerned with developing models with predictive capability, and with probing those models to obtain qualitative and quantitative insight into the phenomena being modelled. Statistics is data-driven and is aimed at the development of methodologies to optimize the information derived from data. The increasing complexity of phenomena that scientists and engineers wish to mo...

Improving the Acceptance Rate of Reversible Jump Mcmc Proposals

Recent articles have commented on the difficulty of proposing efficient reversible jump moves within MCMC. We suggest a new way to make proposals more acceptable using a secondary Markov chain to modify proposed moves — at little extra programming cost.

MCMC for State Space Models

In this chapter we look at MCMC methods for a class of time-series models, called statespace models. The idea of state-space models is that there is an unobserved state of interest the evolves through time, and that partial observations of the state are made at successive time-points. We will denote the state by X and observations by Y , and assume that our state space model has the following s...

Neural Network Weight Space Symmetries Can Speed up Genetic Learning

A functional equivalence of feed-forward networks has been proposed to reduce the search space of learning algorithms. A novel genetic learning algorithm for RBF networks and perceptrons with one hidden layer that makes use of this theoretical property is proposed. Experimental results show that our procedure outperforms the standard genetic learning. Key-Words: Feedforward neural networks, gen...

Graviton: Twisting Space and Time to Speed-up CoFlows