6 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…
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…
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…