27 papers · ranked by Valyu relevance
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…
Gael M. Martin, David T. Frazier, Worapree Maneesoonthorn, Rubén Loaiza‐Maya + 5 more
'Rubén Loaiza‐Maya' 'Florian Huber' 'Gary Koop' 'John M. Maheu' 'Didier Nibbering' 'Anastasios Panagiotelis'] The Bayesian statistical paradigm provides a principled and coherent approach to probabilistic forecasting. Uncertainty about all unknowns that characterize any forecasting problem – model, parameters, latent…
Leonhard Held, Robert Matthews, Manuela Ott, Samuel Pawel
It is now widely accepted that the standard inferential toolkit used by the scientific research community-null-hypothesis significance testing (NHST)-is not fit for purpose. Yet despite the threat posed to the scientific enterprise, there is no agreement concerning alternative approaches for evidence assessment. This…
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…
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…
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…
Christian P. Robert
The concept of hypothesis testing is somewhat inseparable from statistics as principled hypothesis testing is unfeasible outside a statistical framework, while testing may be the most ubiquitous and long-standing manifestation of statistical practice, if not in theoretical statistics. The implementation of this goal is…
Michele Introna, Johannes P. van den Berg, Douglas J. Eleveld, Michel M. R. F. Struys
'Michel M. R. F. Struys'] This narrative review intends to provide the anesthesiologist with the basic knowledge of the Bayesian concepts and should be considered as a tutorial for anesthesiologists in the concept of Bayesian statistics. The Bayesian approach represents the mathematical formulation of the idea that we…
Callum Taylor, Kathryn Puxty, Tara Quasim, Martin Shaw
Bayesian analysis is being used with increasing frequency in critical care research and brings advantages and disadvantages compared to traditional Frequentist techniques. This study overviews this methodology and explains the terminology encountered when appraising this literature. Setting different priors can impact…
Udi Alter, Miranda A. Too, Robert A. Cribbie
Bayesian statistics has gained substantial popularity in the social sciences, particularly in psychology. Despite its growing prominence in the psychological literature, many researchers remain unacquainted with Bayesian methods and their advantages. This tutorial addresses the needs of curious applied psychology…
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…
Niu Du, Yang Chen, Yu Qian
Curve fitting is a key statistical method in the pharmaceutical industry for modeling the relationship between drug effects and dose levels. Traditional regression-based curve fitting is computationally efficient but sensitive to technical errors, particularly with small sample sizes. In practice, budget constraints…
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…
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.…
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…
Simon Martina-Perez, Heba Sailem, Ruth E. Baker
Bayesian methods are routinely used to combine experimental data with detailed mathematical models to obtain insights into physical phenomena. However, the computational cost of Bayesian computation with detailed models has been a notorious problem. Moreover, while high-throughput data presents opportunities to…
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…
Jim E. Griffin
selection using Cartesian credible sets Authors: ['Jim E. Griffin'] Modern regression applications can involve hundreds or thousands of variables which motivates the use of variable selection methods. Bayesian variable selection defines a posterior distribution on the possible subsets of the variables (which are…
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…
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…
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…
Christopher Brydges, Xiaoyu Che, W. Ian Lipkin, Oliver Fiehn
Univariate analyses of metabolomics data currently follow a frequentist approach, using p-values to reject a null-hypothesis. However, the usability of p-values is plagued by many misconceptions and inherent pitfalls. We here propose the use of Bayesian statistics to quantify evidence supporting different hypotheses…
Authors not listed
Occupational chemical hazards pose profound risks to chemists in laboratory and industrial settings, encompassing acute and chronic exposures that imperil sensory organs (e.g., ocular, auditory, olfactory, dermal) and vital physiological systems. This manuscript delineates a multifaceted, innovative protocol suite…
Keiji Ota, Laurence T Maloney
Bayesian decision theory (BDT) is frequently used to model normative performance in perceptual, motor, and cognitive decision tasks where the outcome of each trial is a reward or penalty that depends on the subject’s actions. The resulting normative models specify how decision makers should encode and use information…
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…