26 papers · ranked by Valyu relevance
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…
Gael M. Martin, David T. Frazier, Christian P. Robert
> Abstract. This paper takes the reader on a journey through the history of Bayesian computation, from the 18th century to the present day. Beginning with the one-dimensional integral first confronted by Bayes in 1763, we highlight the key contributions of: Laplace, Metropolis (and, importantly, his coauthors!)…
Max Hinne
Bayesian inference is becoming an increasingly popular framework for statistics in the behavioral sciences. However, its application is hampered by its computational intractability - almost all Bayesian analyses require a form of approximation. While some of these approximate inference algorithms, such as Markov chain…
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…
Carolina Luque, Juan José Sánchez Sosa
In this manuscript, we discuss the substantial importance of Bayesian reasoning in Social Science research. Particularly, we focus on foundational elements to fit models under the Bayesian paradigm. We aim to offer a frame of reference for a broad audience, not necessarily with specialized knowledge in Bayesian…
Rohitash Chandra, Royce Chen, Joshua A. Simmons
Bayesian inference provides a methodology for parameter estimation and uncertainty quantification in machine learning and deep learning methods. Variational inference and Markov Chain Monte-Carlo (MCMC) sampling methods are used to implement Bayesian inference. In the past three decades, MCMC sampling methods have…
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…
Xingyao Xiao, Sophia Rabe-Hesketh, Anders Skrondal
This article addresses problematic behaviors of Markov chain Monte Carlo (MCMC) methods for finite mixture models due to what we call degenerate nonidentifiability. We discuss the reasons for these behaviors, propose diagnostics to detect them, and show through simulations that using more informative priors than the…
Paul Fearnhead, Christopher Nemeth, Chris J. Oates, Chris Sherlock
| Preface | | | 1 | | --- | --- | --- | --- | | 1 | Background | | 4 | | 1.1 | Monte Carlo Methods | | 5 | | | 1.1.1 What is Monte Carlo Integration? | | 5 | | | 1.1.2 Importance Sampling | | 7 | | | 1.1.3 Monte Carlo or Quadrature? | | 7 | | | 1.1.4 Control Variates | | 9 | | | 1.1.5 | Monte Carlo Integration and…
E. G. Cooch, D. I. MacKenzie, J. A. Royle
Data augmentation is now a standard device across capture–recapture and occupancy analysis: adding a fixed number M of all-zero encounter histories replaces a model of unknown dimension with one of fixed dimension. Although M is often treated as a computational tuning choice, it also specifies a finite superpopulation…
Xiangning Xue, Hao Wang, Zihan Zhu, Guan Yu + 3 more
Transcriptomic circadian analysis of human post-mortem brain provides a unique opportunity to characterize in vivo molecular circadian rhythms across brain regions implicated in aging and psychiatric disorders. A primary goal in such analyses is the detection of circadian biomarkers. However, this task is complicated…
Junyang Wang, Kolyan Ray, Pablo Brito‐Parada, Yves Plancherel + 5 more
'Tom Bide' 'Joseph Mankelow' 'John Morley' 'Julia A. Stegemann' 'Rupert Myers'] Title: Abstract Material flow analysis (MFA) is used to quantify and understand the life cycles of materials from production to end of use, which enables environmental, social, and economic impacts and interventions. MFA is challenging as…
van Leeuwen, Florian D., van Erp, Sara
Markov Chain Monte Carlo (MCMC) sampling is computationally expensive, especially for complex models. Alternative methods make simplifying assumptions about the posterior to reduce computational burden, but their impact on predictive performance remains unclear. This paper compares MCMC and non-MCMC methods for…
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…
Singh, Gurprit, Jakob, Wenzel
Generative artificial intelligence (AI) has made unprecedented advances in vision language models over the past two years. These advances are largely due to diffusion-based generative models, which are very stable and simple to train. These diffusion models are tasked to learn the underlying unknown distribution of the…
M Amankwah, A Bersani, D Calvetti, G Davico + 2 more
The human musculoskeletal system is characterized by redundancy in the sense that the number of muscles exceeds the number of degrees of freedom of the musculoskeletal system. In practice, this means that a given motor task can be performed by activating the muscles in infinitely many different ways. This redundancy is…
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…
Authors not listed
The political, social and economic consequences of climate change drastically influence the requirements of modern energy systems and its components. This includes not only energy production but also concepts and innovations for its storage, especially in magnitudes of gigawatt hours. Carnot batteries, which convert…
Xinzhu Liang, Sanjaya Lohani, Joseph M. Lukens, Brian T. Kirby + 2 more
'Thomas A. Searles' 'Kody J. H. Law'] In the general framework of Bayesian inference, the target distribution can only be evaluated up-to a constant of proportionality. Classical consistent Bayesian methods such as sequential Monte Carlo (SMC) and Markov chain Monte Carlo (MCMC) have unbounded time complexity…
Nina Baldy, Marmaduke Woodman, Viktor Jirsa, Meysam Hashemi
Understanding the intricate dynamics of brain activities necessitates models that incorporate causality and nonlinearity. Dynamic Causal Modelling (DCM) presents a statistical framework that embraces causal relationships among brain regions and their responses to experimental manipulations, such as stimulation. In this…
Authors not listed
The rapid growth of worldwide computing power has transformed in silico chemistry into a discipline that is integrated into the daily work of many chemists. Nowadays, researchers find it increasingly straightforward to predict a wide range of molecular properties and chemi- cal processes at reasonable computational…
Sterling Baird, Jason R. Hall, Taylor D. Sparks
Would you rather search for a line inside a cube or a point inside a square? This type of solution degeneracy often exists in physics-based simulations and wet-lab experiments, but constraining these degeneracies is often unsupported or difficult to implement in many optimization packages, requiring additional time and…
Yifan Wu, Aron Walsh, Alex Ganose
What is the minimum number of experiments, or calculations, required to find an optimal solution? Relevant chemical problems range from identifying a compound with target functionality within a given phase space to controlling materials synthesis and device fabrication conditions. A common feature in this application…
Authors not listed
Incorporating prior domain knowledge into Bayesian optimization (BO) remains difficult for statistical methods, which also typically suffer from limited interpretability. Large language models (LLMs) offer complementary strengths in reasoning and knowledge integration, but it remains unclear when and how they improve…
Robert Arbon, Yanchen Zhu, Antonia S. J. S. Mey
Markov state models (MSM) are a popular statistical method for analyzing the conformational dynamics of proteins, including protein folding. With all statistical and machine learning (ML) models choices must be made about the modeling pipeline that cannot be directly learned from the data. These choices, or…
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…