27 papers · ranked by Valyu relevance
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…
Carlo A. Furia, Richard Torkar, Robert Feldt
Statistical analysis is the tool of choice to turn data into information, and then information into empirical knowledge. The process that goes from data to knowledge is, however, long, uncertain, and riddled with pitfalls. To be valid, it should be supported by detailed, rigorous guidelines, which help ferret out…
Helen Evelyn Malone, Imelda Coyne
2.1### Bayesian Analysis Incorporates Prior Information The incorporation of prior information is a fundamental component of Bayesian analysis and is represented in the software as the prior probability distribution (Quintana and Williams ). It is the distribution of credible effect sizes before experimental data…
Zhengxiao Wei, Aijun Yang, Leno Rocha, Michelle F. Miranda + 2 more
'Farouk S. Nathoo' 'Miguel Rubi'] We discuss hypothesis testing and compare different theories in light of observed or experimental data as fundamental endeavors in the sciences. Issues associated with the p-value approach and null hypothesis significance testing are reviewed, and the Bayesian alternative based on the…
Sarah Schreiber, Danielle Hewitt, Ben Seymour, Wako Yoshida
Bayesian statistics offers a flexible framework that supports iterative updating of hypotheses and the incorporation of prior information, amongst other advantages. Although well established for retrospective analysis, the application of Bayesian methods to prospective analysis is less well developed, especially when…
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…
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…
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…
Sebastian Sosa, Mary Brooke McElreath, Cody Ross
Bayesian modeling is a cornerstone of modern ecological and evolutionary research, offering the flexibility to account for hierarchical structures, imperfect detection, and spatial dependencies. However, as ecological datasets grow in scale and complexity—from high-resolution telemetry to phylogenomics—researchers…
Nina Baldy, Meysam Hashemi, Nicolas Simon, Viktor K. Jirsa
Alcohol use disorder (AUD) also called alcohol dependence is a major public health problem, which affects almost 10% of the world’s population. Baclofen as a selective GABA_B_ receptor agonist has emerged as a promising drug for the treatment of AUD, however, its optimal dosage varies according to individuals, and its…
Holly A. Huber, Senta K. Georgia, Stacey D. Finley
Bayesian inference produces a posterior distribution for the parameters and predictions from a mathematical model that can be used to guide the formation of hypotheses, thereby increasing our understanding of a biological process. Previous approaches to such Bayesian hypothesis formation are largely qualitative and…
Richard A. Chechile, Daniel H. Barch Jr.
Nonparametric (or distribution-free) statistics have been widely used in psychological research because behavioral data can be messy and inconsistent with the Gaussian model for measurement error. Distribution-free procedures only use categorical or rank information, so they avoid the problems of outliers and…
Peyton M. Mueller, Abel Torres-Espín, Cole Vonder Haar
The field of neurotrauma is grappling with the effects of the recently identified replication crisis. As such, care must be taken to identify and perform the most appropriate statistical analyses. This will prevent misuse of research resources and ensure that conclusions are reasonable and within the scope of the data.…
Jorge N. Tendeiro, Henk A. L. Kiers
In classical statistics, there is a close link between null hypothesis significance testing (NHST) and parameter estimation via confidence intervals. However, for the Bayesian counterpart, a link between null hypothesis Bayesian testing (NHBT) and Bayesian estimation via a posterior distribution is less…
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…
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…
Klaus Mosegaard
A well known, but mostly neglected, difficulty is connected to the notion of conditional probability densities. Borel, and later Kolmogorov's (1933/1956), found that the traditional definition of conditional densities is incomplete: In different parameterizations it leads to different results. We will show an example…
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…
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…
Nickolas Gantzler, Aryan Deshwal, Janardhan Rao Doppa, Cory Simon
Our objective is to search a large candidate set of covalent organic frameworks (COFs) for the one with the largest equilibrium adsorptive selectivity for xenon (Xe) over krypton (Kr) at room temperature. To predict the Xe/Kr selectivity of a COF structure, we have access to two molecular simulation techniques: (1) a…
Chin-Hsuan Sophie Lin, Trang Thuy Do, Lee Unsworth, Marta I. Garrido
Numerous studies have found that the Bayesian framework, which formulates the optimal integration of the knowledge of the world (i.e. prior) and current sensory evidence (i.e. likelihood), captures human behaviours sufficiently well. However, there are debates regarding whether humans use precise but cognitively…
Russell J. Bowater
An overview is presented of a general theory of statistical inference that is referred to as the fiducial-Bayes fusion. This theory combines organic fiducial inference and Bayesian inference. The aim is that the reader is given a clear summary of the conceptual framework of the fiducial-Bayes fusion as well as pointers…
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
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…
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
We developed OpenStats, a user-friendly web application that brings the power of the R language to researchers through a high-level interface and broad support for statistical methods such as t-tests and ANOVA. OpenStats was integrated into our electronic lab notebook Chemotion ELN via its third-party API, enabling…