Improving Markov Chain Monte Carlo algorithms in LISA Pathfinder Data Analysis

Improving Operational Intensity in Data Bound Markov Chain Monte Carlo

Original algorithms Restructured algorithms Each core accesses all data Data is partitioned across cores Multiple Chain: Each core runs an independent chain Multiple Chain: Each core works on part of a each chain (#chains = #cores) (#chains 6= #cores) Multiple Proposal: Each core evaluates the whole Bayesian likelihood at a proposal Multiple Proposal: Each core evaluates part of the Bayesian li...

On Adaptive Markov Chain Monte Carlo Algorithms

Abstract We look at adaptive MCMC algorithms that generate stochastic processes based on sequences of transition kernels, where each transition kernel is allowed to depend on the past of the process. We show under certain conditions that the generated stochastic process is ergodic, with appropriate stationary distribution. We then consider the Random Walk Metropolis (RWM) algorithm with normal ...

Speeeding Up Markov Chain Monte Carlo Algorithms

We prove an upper bound on the convergence rate of Markov Chain Monte Carlo (MCMC) algorithms for the important special case when the state space can be aggregated into a smaller space, such that the aggregated chain approximately preserves the Markov property.

Markov chain Monte Carlo searches for Galactic binaries in Mock LISA Data Challenge 1B data sets

We are developing a Bayesian approach based on Markov chain Monte Carlo techniques to search for and extract information about white dwarf binary systems with the Laser Interferometer Space Antenna (LISA). Here we present results obtained by applying an initial implementation of this method to some of the data sets released in Round 1B of the Mock LISA Data Challenges. For Challenges 1B.1.1a an...

Markov Chain Monte Carlo in Image Analysis