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…
David W. Hogg, Daniel Foreman-Mackey
Markov Chain Monte Carlo (MCMC) methods for sampling probability density functions (combined with abundant computational resources) have transformed the sciences, especially in performing probabilistic inferences, or fitting models to data. In this primarily pedagogical contribution, we give a brief overview of the…
Samuel Thomas, Wanzhu Tu
Hamiltonian Monte Carlo (HMC) is a powerful tool for Bayesian computation. In comparison with the traditional Metropolis-Hastings algorithm, HMC offers greater computational efficiency, especially in higher dimensional or more complex modeling situations. To most statisticians, however, the idea of HMC comes from a…
Christian P. Robert, V́ıctor Elvira, Nick Tawn, Changye Wu
Markov chain Monte Carlo algorithms are used to simulate from complex statistical distributions by way of a local exploration of these distributions. This local feature avoids heavy requests on understanding the nature of the target, but it also potentially induces a lengthy exploration of this target, with a…
R.L. Harms, A. Roebroeck
In diffusion MRI analysis, advances in biophysical multi-compartment modeling have gained popularity over the conventional Diffusion Tensor Imaging (DTI), because they possess greater specificity in relating the dMRI signal to underlying cellular microstructure. Biophysical multi-compartment models require parameter…
Robbert L. Harms, Alard Roebroeck
In diffusion MRI analysis, advances in biophysical multi-compartment modeling have gained popularity over the conventional Diffusion Tensor Imaging (DTI), because they can obtain a greater specificity in relating the dMRI signal to underlying cellular microstructure. Biophysical multi-compartment models require a…
Y. Curtis Wang, Nirvik Sinha, Johann Rudi, James Velasco + 4 more
Experimental data-based parameter search for Hodgkin–Huxley-style (HH) neuron models is a major challenge for neuroscientists and neuroengineers. Current search strategies are often computationally expensive, are slow to converge, have difficulty handling nonlinearities or multimodalities in the objective function, or…
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)…
Dorsa Mohammadi Arezooji
A hierarchical logistic regression Bayesian model is proposed and implemented in R to model the probability of patient improvement corresponding to any given dosage of a certain drug. RStan is used to obtain samples from the posterior distributions via Markov Chain Monte-Carlo (MCMC) sampling. The effects of selecting…
Longfei Chang, Zhendong Li, Wei-Hai Fang
Variational Monte Carlo Authors: Longfei Chang, Zhendong Li, Wei-Hai Fang Solving the ground state of quantum many-body systems remains a fundamental challenge in physics and chemistry. Recent advancements in quantum hardware have opened new avenues for addressing this challenge. Inspired by the quantum-enhanced Markov…
Faming Liang, Chuanhai Liu, Raymond J. Carroll
where dπ is a probability measure, may prove intractable, from the shape of the domain X to the dimension of X (and x), to the complexity of one of the functions h or π. Standard numerical methods may be hindered by the same reasons. Similar difficulties (may) occur when attempting to find the extrema of π over the…
Fatemeh Beigmohammadi, Jordan J.A. Weaver, Solène Hegarty-Cremer, Cailan Jeynes-Smith + 2 more
The inherent heterogeneity of complex biological systems makes it difficult to experimentally and clinically explore individual outcomes within them. Mechanistic mathematical models are essential tools for studying such heterogeneity. Thus, there is increasing interest in integrating newer mechanistic model-based…
Joëlle Barido-Sottani, Orlando Schwery, Rachel C. M. Warnock, Chi Zhang + 1 more
'Chi Zhang' 'April Marie Wright'] Phylogenetic estimation is, and has always been, a complex endeavor. Estimating a phylogenetic tree involves evaluating many possible solutions and possible evolutionary histories that could explain a set of observed data, typically by using a model of evolution. Values for all model…
Mohamed S. Eliwa, Laila A. Al-Essa, Amr M. Abou-Senna, Mahmoud El-Morshedy + 1 more
'Mahmoud El-Morshedy' 'Rashad M. EL-Sagheer'] The importance of biomedical physical data is underscored by its crucial role in advancing our comprehension of human health, unraveling the mechanisms underlying diseases, and facilitating the development of innovative medical treatments and interventions. This data serves…
L. Mihaela Paun, Dirk Husmeier
The past few decades have witnessed an explosive synergy between physics and the life sciences. In particular, physical modelling in medicine and physiology is a topical research area. The present work focuses on parameter inference and uncertainty quantification in a 1D fluid-dynamics model for quantitative…
P. L. Green, K. Worden
In this paper, the authors outline the general principles behind an approach to Bayesian system identification and highlight the benefits of adopting a Bayesian framework when attempting to identify models of nonlinear dynamical systems in the presence of uncertainty. It is then described how, through a summary of some…
Zhixiao Zhu, Maria Christodoulou, David Steinsaltz
Many complex systems are modelled using modular models, where individual sub-models are estimated separately and then combined. While this simplifies inference, it fails to account for interactions between components. A natural solution is to estimate all components jointly, but this is often impractical due to…
Samuel Gill, Nathan M. Lim, Patrick Grinaway, Ariën S. Rustenburg + 4 more
Accurately predicting protein-ligand binding is a major goal in computational chemistry, but even the prediction of ligand binding modes in proteins poses major challenges. Here, we focus on solving the binding mode prediction problem for rigid fragments. That is, we focus on computing the dominant placement…
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…
Tijana Radivojević, Elena Akhmatskaya
The Hamiltonian Monte Carlo (HMC) method has been recognized as a powerful sampling tool in computational statistics. We show that performance of HMC can be dramatically improved by incorporating importance sampling and an irreversible part of the dynamics into the chain. This is achieved by replacing Hamiltonians in…
Samuel Gill, Nathan M. Lim, Patrick Grinaway, Ariën S. Rustenburg + 4 more
Accurately predicting protein-ligand binding is a major goal in computational chemistry, but even the prediction of ligand binding modes in proteins poses major challenges. Here, we focus on solving the binding mode prediction problem for rigid fragments. That is, we focus on computing the dominant placement…
Samuel Gill, Nathan M. Lim, Patrick Grinaway, Ariën S. Rustenburg + 4 more
Accurately predicting protein-ligand binding is a major goal in computational chemistry, but even the prediction of ligand binding modes in proteins poses major challenges. Here, we focus on solving the binding mode prediction problem for rigid fragments. That is, we focus on computing the dominant placement…
Samuel Gill, Nathan M. Lim, Patrick Grinaway, Ariën S. Rustenburg + 4 more
Accurately predicting protein-ligand binding is a major goal in computational chemistry, but even the prediction of ligand binding modes in proteins poses major challenges. Here, we focus on solving the binding mode prediction problem for rigid fragments. That is, we focus on computing the dominant placement…
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…
Martin Robinson, Alan Bond, Alexandr Simonov, Jie Zhang + 1 more
Recently, we have introduced the use of techniques drawn from Bayesian statistics to recover kinetic and thermodynamic parameters from voltammetric data, and were able to show that the technique of large amplitude ac voltammetry yielded significantly more accurate parameter values than the equivalent dc approach. In…