Non-reversible parallel tempering: a scalable highly parallel MCMC scheme
S Syed, A Bouchard-Côté… - Journal of the Royal …, 2022 - academic.oup.com
Parallel tempering (PT) methods are a popular class of Markov chain Monte Carlo schemes
used to sample complex high-dimensional probability distributions. They rely on a collection …
used to sample complex high-dimensional probability distributions. They rely on a collection …
Universal probabilistic programming offers a powerful approach to statistical phylogenetics
Statistical phylogenetic analysis currently relies on complex, dedicated software packages,
making it difficult for evolutionary biologists to explore new models and inference strategies …
making it difficult for evolutionary biologists to explore new models and inference strategies …
Cancer phylogenetic tree inference at scale from 1000s of single cell genomes
A new generation of scalable single cell whole genome sequencing (scWGS) methods
allows unprecedented high resolution measurement of the evolutionary dynamics of cancer …
allows unprecedented high resolution measurement of the evolutionary dynamics of cancer …
[PDF][PDF] Efficient Bayesian inference of phylogenetic trees from large scale, low-depth genome-wide single-cell data
A new generation of scalable single cell whole genome sequencing (scWGS) methods
[Zahn et al., 2017, Laks et al., 2019], allows unprecedented high resolution measurement of …
[Zahn et al., 2017, Laks et al., 2019], allows unprecedented high resolution measurement of …
TreePPL: A Universal Probabilistic Programming Language for Phylogenetics
We present TreePPL, a language for probabilistic modeling and inference in statistical
phylogenetics. Specifically, TreePPL is a domain-specific universal probabilistic …
phylogenetics. Specifically, TreePPL is a domain-specific universal probabilistic …
[PDF][PDF] Reflections on Bayesian inference and Markov chain Monte Carlo.
RV Craiu, P Gustafson… - Canadian Journal of …, 2022 - utstat.utoronto.ca
Bayesian inference and Markov chain Monte Carlo methods are vigorous areas of statistical
research. Here we reflect on some recent developments and future directions in these fields …
research. Here we reflect on some recent developments and future directions in these fields …
Analysis of high-dimensional continuous time Markov chains using the local bouncy particle sampler
T Zhao, A Bouchard-Côté - Journal of Machine Learning Research, 2021 - jmlr.org
Sampling the parameters of high-dimensional Continuous Time Markov Chains (CTMCs) is
a challenging problem with important applications in many fields of applied statistics. In this …
a challenging problem with important applications in many fields of applied statistics. In this …
Probabilistic programming: a powerful new approach to statistical phylogenetics
Statistical phylogenetic analysis currently relies on complex, dedicated software packages,
making it difficult for evolutionary biologists to explore new models and inference strategies …
making it difficult for evolutionary biologists to explore new models and inference strategies …
Sequential Core-Set Monte Carlo
Abstract Sequential Monte Carlo (SMC) is a general-purpose methodology for recursive
Bayesian inference, and is widely used in state space modeling and probabilistic …
Bayesian inference, and is widely used in state space modeling and probabilistic …