96 citations · 228 across the 14 of their papers we have counts for
14 papers
Multilevel and Sequential Monte Carlo for Training-Free Diffusion Guidance
Aidan Gleich, Scott C. Schmidler
We address the problem of accurate, training-free guidance for conditional generation in trained diffusion models. Existing methods typically rely on point-estimates to approximate…
Algorithms for Reconstructing B Cell Lineages in the Presence of Context-Dependent Somatic Hypermutation
Yongkang Li, Kevin J. Wiehe, Scott C. Schmidler
We introduce a method for approximating posterior probabilities of phylogenetic trees and reconstructing ancestral sequences under models of sequence evolution with site-dependence…
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…
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…
Minimax Mixing Time of the Metropolis-Adjusted Langevin Algorithm for Log-Concave Sampling
Keru Wu, Scott Schmidler, Yuansi Chen
We study the mixing time of the Metropolis-adjusted Langevin algorithm (MALA) for sampling from a log-smooth and strongly log-concave distribution. We establish its optimal minimax…
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…
Bayesian protein structure alignment
Abel Rodriguez, Scott C. Schmidler
The analysis of the three-dimensional structure of proteins is an important topic in molecular biochemistry. Structure plays a critical role in defining the function of proteins an…
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…
Geometric Ergodicity of Two--dimensional Hamiltonian systems with a Lennard--Jones--like Repulsive Potential
Ben Cooke, David P. Herzog, Jonathan C. Mattingly +2
In this paper we establish the ergodicity of Langevin dynamics for simple two-particle system involving a Lennard-Jones type potential. To the best of our knowledge, this is the fi…
Bayesian model search and multilevel inference for SNP association studies
Melanie A. Wilson, Edwin S. Iversen, Merlise A. Clyde +2
Technological advances in genotyping have given rise to hypothesis-based association studies of increasing scope. As a result, the scientific hypotheses addressed by these studies…
Conditions for rapid mixing of parallel and simulated tempering on multimodal distributions
Dawn B. Woodard, Scott C. Schmidler, Mark Huber
We give conditions under which a Markov chain constructed via parallel or simulated tempering is guaranteed to be rapidly mixing, which are applicable to a wide range of multimodal…