96 citations · 228 across the 14 of their papers we have counts for
6 papers · 1 filter
Improved Bounds for Context-Dependent Evolutionary Models Using Sequential Monte Carlo
Joseph Mathews, Scott C. Schmidler
Statistical inference in evolutionary models with site-dependence is a long-standing challenge in phylogenetics and computational biology. We consider the problem of approximating…
Importance Sampling Approximation of Sequence Evolution Models with Site-Dependence
Joseph Mathews, Scott C. Schmidler
We consider models for molecular sequence evolution in which the transition rates at each site depend on the local sequence context, giving rise to a time-inhomogeneous Markov proc…
Finite Sample Complexity of Sequential Monte Carlo Estimators on Multimodal Target Distributions
Joseph Mathews, Scott C. Schmidler
We prove finite sample complexities for sequential Monte Carlo (SMC) algorithms which require only local mixing times of the associated Markov kernels. Our bounds are particularly…
Finite Sample Bounds for Sequential Monte Carlo and Adaptive Path Selection Using the Norm
Joe Marion, Joseph Mathews, Scott C. Schmidler
We prove a bound on the finite sample error of sequential Monte Carlo (SMC) on static spaces using the distance between interpolating distributions and the mixing times of Ma…
Finite Sample Complexity of Sequential Monte Carlo Estimators
Joe Marion, Joseph Mathews, Scott C. Schmidler
We present bounds for the finite sample error of sequential Monte Carlo samplers on static spaces. Our approach explicitly relates the performance of the algorithm to properties of…
Parallel Markov Chain Monte Carlo
Douglas N. VanDerwerken, Scott C. Schmidler
Markov chain Monte Carlo is an inherently serial algorithm. Although likelihood calculations for individual steps can sometimes be parallelized, the serial evolution of the process…