29 papers · ranked by Valyu relevance
Jun Zhu, Jianfei Chen, Wenbo Hu, Bo Zhang
—The explosive growth in data volume and the availability of cheap computing resources have sparked increasing interest in Big learning, an emerging subfield that studies scalable machine learning algorithms, systems, and applications with Big Data. Bayesian methods represent one important class of statistical methods…
Sanjib Sharma
Markov Chain Monte Carlo based Bayesian data analysis has now become the method of choice for analyzing and interpreting data in almost all disciplines of science. In astronomy, over the last decade, we have also seen a steady increase in the number of papers that employ Monte Carlo based Bayesian analysis. New…
Branimir K. Hackenberger
According to different scientific literature databases (eg, ScienceDirect, Web of Science), each year more and more scientific articles use Bayesian methods for data processing ([F1]). Does this mean that Bayesian statistics is better than frequentist statistics? What can we achieve with Bayesian methods but not with…
Christopher Yau, Kieran Campbell
Bayesian statistical learning provides a coherent probabilistic framework for modelling uncertainty in systems. This review describes the theoretical foundations underlying Bayesian statistics and outlines the computational frameworks for implementing Bayesian inference in practice. We then describe the use of Bayesian…
Erling W. Rognli, Rune Zahl‐Olsen, Sondre Sverd Rekdal, Asle Hoffart + 1 more
'Thomas Bjerregaard Bertelsen'] Bayesian statistical approaches offer nuanced, detailed, and intuitive analyses, even with small sample sizes. Although these qualities are highly relevant for researchers in child and adolescent mental health, Bayesian methods are still quite rarely employed. This editorial perspective…
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…
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…
Xuan Guo, Bing Liu, Le Chen, Guantao Chen + 2 more
This paper aims to review state-of-the-art Bayesian-inference-based methods applied to functional magnetic resonance imaging (fMRI) data. Particularly, we focus on one specific long-standing challenge in the computational modeling of fMRI datasets: how to effectively explore typical functional interactions from fMRI…
Natalia Levshina
In recent years, Bayesian statistics has gained traction across a wide range of scientific disciplines. This paper explores the growing application of Bayesian methods within the field of linguistics and considers their future potential. A survey of articles from different linguistics journals indicates that Bayesian…
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…
Camilla van Geen, Raphael T. Gerraty
Reinforcement learning models have been used extensively to capture learning and decision-making processes in humans and other organisms. One essential goal of these computational models is the generalization to new sets of observations. Extracting parameters that can reliably predict out-of-sample data can be…
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…
Shuji Shinohara, Nobuhito Manome, Kouta Suzuki, Ung-il Chung + 4 more
In this study, we start by proposing a causal induction model that incorporates symmetry bias. This model has two parameters that control the strength of symmetry bias and includes conditional probability and conventional models of causal induction as special cases. It can reproduce causal induction of human judgment…
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…
Samidha Shetty, Gordon Brittan Jr., Prasanta S. Bandyopadhyay, Dawn E. Holmes
'Dawn E. Holmes'] Empirical Bayes-based Methods (EBM) is an increasingly popular form of Objective Bayesianism (OB). It is identified in particular with the statistician Bradley Efron. The main aims of this paper are, first, to describe and illustrate its main features and, second, to locate its role by comparing it…
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…
Clare D. Harris, Elise G. Rowe, Roshini Randeniya, Marta I. Garrido
Predictive coding postulates that we make (top-down) predictions about the world and that we continuously compare incoming (bottom-up) sensory information with these predictions, in order to update our models and perception so as to better reflect reality. That is, our so-called “Bayesian brains” continuously create…
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…
Rens van de Schoot, Naomi Schalken, Miranda Olff
2.1.### Flexible hypothesis testing The first reason why Bayesian statistics is used is that it provides a flexible alternative to null hypothesis significance testing. van Veen, Engelhard, and van den Hout ([CIT0036]) tested several predictions from working memory theory that could explain the efficacy of eye movement…
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…
Jose D. Perezgonzalez
Bayesian Statistics for Beginners. A Step-by-Step Approach (Donovan and Mickey, [2]) is, perhaps, the “truest-to-title” book I have read on Bayesian inference and statistics, insofar (a) it is written for novices to probability, inference, the scientific method, and Bayesian methodology, (b) it introduces those four…
Sanjay Chaudhuri, Yin Teng
In recent times empirical likelihood has been widely applied under Bayesian framework. Markov chain Monte Carlo (MCMC) methods are frequently employed to sample from the posterior distribution of the parameters of interest. However, complex, especially non-convex nature of the likelihood support erects enormous…
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…
Ya’acov Ritov
We argue that the Bayesian paradigm, of a prior which represents the beliefs of the statistician before observing the data, is not feasible in ultra-high dimensional models. We claim that natural priors that represent the a priori beliefs fail in unpredictable ways under values of the parameters that cannot be honestly…
David J. Hand
The papers in this collection are superb illustrations of the power of modern Bayesian methods. They give examples of problems which are well suited to being tackled using such methods, but one must not lose sight of the merits of having multiple different strategies and tools in one's inferential armoury.
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…
Sterling Baird, Jason R. Hall, Taylor D. Sparks
Would you rather search for a line inside a cube or a point inside a square? Physics-based simulations and wet-lab experiments often have symmetries (degeneracies) that allow reducing problem dimensionality or search space, but constraining these degeneracies is often unsupported or difficult to implement in many…
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…