Zobrazeno 1 - 10
of 164
pro vyhledávání: '"Bouchard‐Côté, Alexandre"'
Autor:
Salehi, Sohrab, Dorri, Fatemeh, Chern, Kevin, Kabeer, Farhia, Rusk, Nicole, Funnell, Tyler, Williams, Marc J., Lai, Daniel, Andronescu, Mirela, Campbell, Kieran R., McPherson, Andrew, Aparicio, Samuel, Roth, Andrew, Shah, Sohrab P., Bouchard-Côté, Alexandre
Publikováno v:
Peer Community Journal, Vol 3, Iss , Pp - (2023)
A new generation of scalable single cell whole genome sequencing (scWGS) methods allows unprecedented high resolution measurement of the evolutionary dynamics of cancer cell populations. Phylogenetic reconstruction is central to identifying sub-popul
Externí odkaz:
https://doaj.org/article/7d336692a2954e17ba3b00dfa26ea966
Autor:
Luu, Son, Xu, Zuheng, Surjanovic, Nikola, Biron-Lattes, Miguel, Campbell, Trevor, Bouchard-Côté, Alexandre
The Hamiltonian Monte Carlo (HMC) algorithm is often lauded for its ability to effectively sample from high-dimensional distributions. In this paper we challenge the presumed domination of HMC for the Bayesian analysis of GLMs. By utilizing the struc
Externí odkaz:
http://arxiv.org/abs/2410.03630
Annealed Sequential Monte Carlo (SMC) samplers are special cases of SMC samplers where the sequence of distributions can be embedded in a smooth path of distributions. Using this underlying path of distributions and a performance model based on the v
Externí odkaz:
http://arxiv.org/abs/2408.12057
Non-reversible parallel tempering (NRPT) is an effective algorithm for sampling from target distributions with complex geometry, such as those arising from posterior distributions of weakly identifiable and high-dimensional Bayesian models. In this w
Externí odkaz:
http://arxiv.org/abs/2405.11384
This paper is intended to appear as a chapter for the Handbook of Markov Chain Monte Carlo. The goal of this chapter is to unify various problems at the intersection of Markov chain Monte Carlo (MCMC) and machine learning$\unicode{x2014}$which includ
Externí odkaz:
http://arxiv.org/abs/2402.09598
Autor:
Biron-Lattes, Miguel, Surjanovic, Nikola, Syed, Saifuddin, Campbell, Trevor, Bouchard-Côté, Alexandre
Selecting the step size for the Metropolis-adjusted Langevin algorithm (MALA) is necessary in order to obtain satisfactory performance. However, finding an adequate step size for an arbitrary target distribution can be a difficult task and even the b
Externí odkaz:
http://arxiv.org/abs/2310.16782
Autor:
Sidrow, Evan, Heckman, Nancy, Bouchard-Côté, Alexandre, Fortune, Sarah M. E., Trites, Andrew W., Auger-Méthé, Marie
Hidden Markov models (HMMs) are popular models to identify a finite number of latent states from sequential data. However, fitting them to large data sets can be computationally demanding because most likelihood maximization techniques require iterat
Externí odkaz:
http://arxiv.org/abs/2310.04620
Simulated Tempering (ST) is an MCMC algorithm for complex target distributions that operates on a path between the target and a more amenable reference distribution. Crucially, if the reference enables i.i.d. sampling, ST is regenerative and can be p
Externí odkaz:
http://arxiv.org/abs/2309.05578
Autor:
Surjanovic, Nikola, Biron-Lattes, Miguel, Tiede, Paul, Syed, Saifuddin, Campbell, Trevor, Bouchard-Côté, Alexandre
We introduce a software package, Pigeons.jl, that provides a way to leverage distributed computation to obtain samples from complicated probability distributions, such as multimodal posteriors arising in Bayesian inference and high-dimensional distri
Externí odkaz:
http://arxiv.org/abs/2308.09769
Sampling from complex target distributions is a challenging task fundamental to Bayesian inference. Parallel tempering (PT) addresses this problem by constructing a Markov chain on the expanded state space of a sequence of distributions interpolating
Externí odkaz:
http://arxiv.org/abs/2206.00080