2 citations · 2 across the 7 of their papers we have counts for
7 papers
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…
Posterior bounds on divergence time of two sequences under dependent-site evolutionary models
Joseph Mathews, Scott C. Schmidler
Let $\x$ and $\y$ be two length DNA sequences, and suppose we would like to estimate the divergence time . Under suitable conditions, a well-known simple but crude estimate…
Sequential Monte Carlo for Cut-Bayesian Posterior Computation
Joseph Mathews, Giri Gopalan, James Gattiker +2
We propose a sequential Monte Carlo (SMC) method to efficiently and accurately compute cut-Bayesian posterior quantities of interest, variations of standard Bayesian approaches con…
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…