24 papers · ranked by Valyu relevance
Zoubin Ghahramani
Modelling is fundamental to many fields of science and engineering. A model can be thought of as a representation of possible data one could predict from a system. The probabilistic approach to modelling uses probability theory to express all aspects of uncertainty in the model. The probabilistic approach is synonymous…
Charles C. Margossian, Yi Zhang, William R. Gillespie
Stan is an open-source probabilistic programing language, primarily designed to do Bayesian data analysis. Its main inference algorithm is an adaptive Hamiltonian Monte Carlo sampler, supported by state-of-the-art gradient computation. Stan's strengths include efficient computation, an expressive language that offers a…
Juan Sosa, Carlos Martínez, Cruz, Danna
This paper offers a comprehensive introduction to Bayesian inference, combining historical context, theoretical foundations, and core analytical examples. Beginning with Bayes' theorem and the philosophical distinctions between Bayesian and frequentist approaches, we develop the inferential framework for estimation…
Rick Farouni
In general, we will be limiting our focus mainly to the the multivariate statistics setting in which we treat an observation as a multivariate random vector yn = (yn,1, . . . , yn,P ) consisting of P features and the data as a set of N observations y = {y1, · · · , yn, · · · , yN }. Accordingly, we can think of the…
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…
Chong Wang, David M. Blei
Robust Bayesian models are appealing alternatives to standard models, providing protection from data that contains outliers or other departures from the model assumptions. Historically, robust models were mostly developed on a case-by-case basis; examples include robust linear regression, robust mixture models, and…
Teddy Groves, Nicholas Luke Cowie, Lars Keld Nielsen
Facilitates Quantitative Modeling of Cell Metabolism Authors: ['Teddy Groves' 'Nicholas Luke Cowie' 'Lars Keld Nielsen'] This paper presents Maud, a command-line application that implements Bayesian statistical inference for kinetic models of biochemical metabolic reaction networks. Maud takes into account quantitative…
Yu Lin Hsu, Chu Chuan Jeng, Pavithra Sripathanallur Murali, Mohammadreza Torkjazi + 3 more
'Mohammadreza Torkjazi' 'J. S. West' 'Michaela Zuber' 'Vadim Sokolov'] This paper presents an overview of some of the concepts of Bayesian Learning. The number of scientific and industrial applications of Bayesian learning has been growing in size rapidly over the last few decades (Damien et al. 2013). This process has…
Juan Sosa, Lina Buitrago
We provide four case studies that use Bayesian machinery to making inductive reasoning. Our main motivation relies in offering several instances where the Bayesian approach to data analysis is exploited at its best to perform complex tasks, such as description, testing, estimation, and prediction. This work is not…
Brandon S Coventry, Edward L Bartlett
Typical statistical practices in the biological sciences have been increasingly called into question due to difficulties in replication of an increasing number of studies, many of which are confounded by the relative difficulty of null significance hypothesis testing designs and interpretation of p-values. Bayesian…
Matti T. J. Heino, Matti Vuorre, Nelli Hankonen
Introduction Evaluating effects of behavior change interventions is a central interest in health psychology and behavioral medicine. Researchers in these fields routinely use frequentist statistical methods to evaluate the extent to which these interventions impact behavior and the hypothesized mediating processes in…
Oliver M. Crook, Chun-wa Chung, Charlotte M. Deane
Opportunities for Bayesian Statistics in Proteomics Authors: ['Oliver M. Crook' 'Chun-wa Chung' 'Charlotte M. Deane'] Proteomics is a data-rich science with complex experimental designs and an intricate measurement process. To obtain insights from the large data sets produced, statistical methods, including machine…
Olivier Gimenez, Andy Royle, Marc Kéry, Chloé R. Nater + 1 more
Working with Bayesian statistics is an iterative process consisting of multiple rounds of building, assessing, and revising models (Tip #3). Model assessment typically has several steps too, and most focus on either MCMC performance or model goodness-of-fit (GOF; Tip #5). Obtaining reliable inferences from a Bayesian…
Chenxi Sui, Ziyang Jiang, Genesis Higueros, David Carlson + 1 more
High-performance batteries are poised for electrification of vehicles and therefore mitigate greenhouse gas emissions, which, in turn, promote a sustainable future. However, the design of optimized batteries is challenging due to the nonlinear governing physics and electrochemistry. Recent advancements have…
Colin D. Kinz-Thompson, Korak Kumar Ray, Ruben L. Gonzalez
Biophysics experiments performed at single-molecule resolution contain exceptional insight into the structural details and dynamic behavior of biological systems. However, extracting this information from the corresponding experimental data unequivocally requires applying a biophysical model. Here, we discuss how to…
Shuji Shinohara, Nobuhito Manome, Kouta Suzuki, Ung-il Chung + 5 more
Bayesian inference is a process of narrowing down hypotheses (causes) to one that best explains observational data (effects). To accurately estimate a cause, a considerable amount of data is required to be observed for as long as possible. However, the object of inference is not always constant. In this case, a method…
Jure Demšar, Grega Repovš, Erik Štrumbelj
Research in psychology generates complex data and often requires unique statistical analyses. These tasks are often very specific, so appropriate statistical models and methods cannot be found in accessible Bayesian tools. As a result, the use of Bayesian methods is limited to researchers and students that have the…
Jan Boelts, Jan-Matthis Lueckmann, Richard Gao, Jakob H. Macke
Identifying parameters of computational models that capture experimental data is a central task in cognitive neuroscience. Bayesian statistical inference aims to not only find a single configuration of best-fitting parameters, but to recover all model parameters that are consistent with the data and prior knowledge.…
Lucian Chan, Geoffrey Hutchison, Garrett Morris
Generating low-energy molecular conformers is a key task for many areas of computational chemistry, molecular modeling and cheminformatics. Most current conformer generation methods primarily focus on generating geometrically diverse conformers rather than finding the most probable or energetically lowest minima. Here…
Lucian Chan, Geoffrey Hutchison, Garrett Morris
Generating low-energy molecular conformers is a key task for many areas of computational chemistry, molecular modeling and cheminformatics. Most current conformer generation methods primarily focus on generating geometrically diverse conformers rather than finding the most probable or energetically lowest minima. Here…
Joseph M. Northrup, Brian D. Gerber
Understanding patterns of species occurrence and the processes underlying these patterns is fundamental to the study of ecology. One of the more commonly used approaches to investigate species occurrence patterns is occupancy modeling, which can account for imperfect detection of a species during surveys. In recent…
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…
Authors not listed
Solving optimization problems, especially for nonlinear and constrained systems, is a challenge. Decades of specialized algorithms have been developed for general and special cases of root finding, minimization (including constraints), for parameter estimation, and mapping connected spaces. These approaches typically…
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…