25 papers · ranked by Valyu relevance
Don van Ravenzwaaij, Pete Cassey, Scott D. Brown
Markov Chain Monte-Carlo (MCMC) is an increasingly popular method for obtaining information about distributions, especially for estimating posterior distributions in Bayesian inference. This article provides a very basic introduction to MCMC sampling. It describes what MCMC is, and what it can be used for, with simple…
Yun Yu, Christopher Jermaine, Luay Nakhleh
Background Phylogenetic networks are leaf-labeled graphs used to model and display complex evolutionary relationships that do not fit a single tree. There are two classes of phylogenetic networks: Data-display networks and evolutionary networks. While data-display networks are very commonly used to explore data, they…
István Miklós, Heather Smith
Background Even for moderate size inputs, there are a tremendous number of optimal rearrangement scenarios, regardless what the model is and which specific question is to be answered. Therefore giving one optimal solution might be misleading and cannot be used for statistical inferring. Statistically well funded…
Anthony Trezza, Donald J. Bucci, Pramod K. Varshney
—Gibbs sampling is one of the most popular Markov chain Monte Carlo algorithms because of its simplicity, scalability, and wide applicability within many fields of statistics, science, and engineering. In the labeled random finite sets literature, Gibbs sampling procedures have recently been applied to efficiently…
Tobias Siems
We elaborate the idea behind Markov chain Monte Carlo (MCMC) methods in a mathematically coherent, yet simple and understandable way. To this end, we proof a pivotal convergence theorem for finite Markov chains and a minimal version of the Perron-Frobenius theorem. Subsequently, we briefly discuss two fundamental MCMC…
Benjamin Trendelkamp-Schroer, Frank Noé
Direct simulation of biomolecular dynamics in thermal equilibrium is challenging due to the metastable nature of conformation dynamics and the computational cost of molecular dynamics. Biased or enhanced sampling methods may improve the convergence of expectation values of equilibrium probabilities and expectation…
Sumeetpal S. Singh, Fredrik Lindsten, Éric Moulines
Sampling from the conditional (or posterior) probability distribution of the latent states of a Hidden Markov Model, given the realization of the observed process, is a non-trivial problem in the context of Markov Chain Monte Carlo. To do this Andrieu et al. (2010) constructed a Markov kernel which leaves this…
Steven Luu, Zuheng Xu, Nikola Surjanovic, Miguel Biron-Lattes + 2 more
'Trevor Campbell' 'Alexandre Bouchard‐Côté'] 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 structure of the compute graph…
Marc Manceau
The SARS-CoV-2 outbreak started in late 2019 in the Hubei province in China and the first viral sequence was made available to the scientific community on early January 2020. From there, viral genomes from all over the world have followed at an outstanding rate, reaching already more than 10^5^ on early May 2020, and…
Pedro L. Baldoni, Lizhong Chen, Gordon K. Smyth
Differential transcript expression analysis of RNA-seq data is an increasingly popular tool to assess changes in expression of individual transcripts between biological conditions. Software designed for transcript-level differential expression analyses account for the uncertainty of transcript quantification, the…
Kevin Mcgregor, Aurélie Labbe, Celia M.T. Greenwood, Todd Parsons + 1 more
The human microbiome comprises the microorganisms that inhabit the various locales of the human body and plays a vital role in human health. The composition of a microbial population is often quantified through measures of species diversity, which summarize the number of species along with their relative abundances…
Alicia A. Johnson, Owen Burbank
In any Markov chain Monte Carlo analysis, rapid convergence of the chain to its target probability distribution is of practical and theoretical importance. A chain that converges at a geometric rate is geometrically ergodic. In this paper, we explore geometric ergodicity for two-component Gibbs samplers which, under a…
Conor Rosato, Peter L. Green, John Harris, Simon Maskell + 3 more
Antimicrobial resistance (AMR) emerges when disease-causing microorganisms develop the ability to withstand the effects of antimicrobial therapy. This phenomenon is often fueled by the human-to-human transmission of pathogens and the overuse of antibiotics. Over the past 50 years, increased computational power has…
Ko Abe, Kodai Minoura, Yuka Maeda, Hiroyoshi Nishikawa + 1 more
'Teppei Shimamura'] Background High-dimensional flow cytometry and mass cytometry allow systemic-level characterization of more than 10 protein profiles at single-cell resolution and provide a much broader landscape in many biological applications, such as disease diagnosis and prediction of clinical outcome. When…
Anastasis Georgoulas, Jane Hillston, Guido Sanguinetti
We consider continuous time Markovian processes where populations of individual agents interact stochastically according to kinetic rules. Despite the increasing prominence of such models in fields ranging from biology to smart cities, Bayesian inference for such systems remains challenging, as these are continuous…
Thomas Amby Ottosen
Since the middle of the 1940's scientists have used Monte Carlo (MC) simulations to obtain information about physical processes. This has proved a accurate and and reliable method to obtain this information. Through out resent years researchers has begone to use the slightly newer Markov Chain Monte Carlo (MCMC)…
Hao Cheng, Long Qu, Dorian J. Garrick, Rohan L. Fernando
Background In whole-genome analyses, the number p of marker covariates is often much larger than the number n of observations. Bayesian multiple regression models are widely used in genomic selection to address this problem of $p\gg n.$ The primary difference between these models is the prior assumed for the effects of…
Cheng Hao, Garrick Dorian, Rohan Fernando
Bayesian multiple regression methods are widely used in whole-genome analyses to solve the problem that the number p of marker covariates is usually larger than the number n of observations. Inferences from most Bayesian methods are based on Markov chain Monte Carlo methods, where statistics are computed from a Markov…
Authors not listed
Accurately modeling the binding free energies associated with molecular cluster formation is critical for understanding atmospheric new particle formation. Conventional quantum-chemistry methods, however, often struggle to describe thermodynamic contributions, particularly in systems exhibiting significant…
Authors not listed
We review the use of state-of-the-art enhanced sampling techniques in molecular dynamics (MD) simu- lations. We briefly introduce the principles and practical considerations underlying the application of these methods, making reference to recent reviews, and then discuss their application to membrane transporters as…
Authors not listed
The concept of thermodynamic free energy plays a central role across various disciplines, providing critical insights into system dynamics, energy flow, and sustainability. In materials science, Gibbs free energies are typically determined through phase diagram modeling, which relies on a broad range of thermodynamic…
Aisaku Arakawa, Takeshi Hayashi, Masaaki Taniguchi, Satoshi Mikawa + 1 more
A Hamiltonian Monte Carlo algorithm is a Markov Chain Monte Carlo method that is considered more effective than the conventional Gibbs sampling method. Hamiltonian Monte Carlo is based on Hamiltonian dynamics, and it follows Hamilton’s equations, which are expressed as two differential equations. In the sampling…
Mario BARBATTI
The microcanonical temperature of an isolated molecule is derived in terms of Boltzmann and Gibbs volume entropies within the quantum harmonic vibrational approximation. The effects of the entropy functional choice and various approximations are examined. The difference between Boltzmann and Gibbs volume temperatures…
Juan Viguera Diez, Sara Romeo Atance, Ola Engkvist, Simon Olsson
The accurate prediction of thermodynamic properties is crucial in various fields such as drug discovery and materials design. This task relies on sampling from the underlying Boltzmann distribution, which is challenging using conventional approaches such as simulations. In this work, we introduce Surrogate…
Stéphane Dupas
Ecological patterns result from historical contingency and deterministic processes. Taking apart these processes to extract probabilistic models of ecological dynamics is of major importance for ecological forecasting. Due to the high dimensionality of historical contingency it is usually difficult to sample history…