Results 31 to 40 of about 18,517 (245)
Statistical Significance Testing for Mixed Priors: A Combined Bayesian and Frequentist Analysis
Entropy, 2022 In many hypothesis testing applications, we have mixed priors, with well-motivated informative priors for some parameters but not for others. The Bayesian methodology uses the Bayes factor and is helpful for the informative priors, as it incorporates ...
Jakob Robnik, Uroš Seljak
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Advanced Intelligent Systems, EarlyView.This study provides an introduction to Bayesian optimisation targeted for experimentalists. It explains core concepts, surrogate modelling, and acquisition strategies, and addresses common real‐world challenges such as noise, constraints, mixed variables, scalability, and automation.
Chuan He +2 more
wiley +1 more source
Annals of Neurology, EarlyView.Objective Age of symptom onset is highly variable in familial frontotemporal lobar degeneration (f‐FTLD). Accurate prediction of onset would inform clinical management and trial enrollment. Prior studies indicate that individualized maps of brain atrophy can predict conversion to dementia in f‐FTLD.
Shubir Dutt +82 more
wiley +1 more source
British Journal of Clinical Pharmacology, EarlyView.Aim Metabolic liver disease, including nonalcoholic fatty liver disease (NAFLD) and nonalcoholic steatohepatitis, is a major cause of chronic liver dysfunction worldwide, creating an urgent need for effective treatments. This systematic literature review (SLR) and network meta‐analysis (NMA) systematically reviews and compares the efficacy and safety ...
Andrej Belančić +8 more
wiley +1 more source
Setting limits and application to Higgs boson search
EPJ Web of Conferences, 2013 This lecture summarizes the basic concept of hypothesis testing, will introduce the concepts of significance and upper limit under the frequentist and Bayesian approaches, and will discuss the benefits and limitations of the most popular approaches ...
Lista Luca
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Comparison of Bayesian and frequentist methods for prevalence estimation under misclassification
BMC Public Health, 2020 Background Various methods exist for statistical inference about a prevalence that consider misclassifications due to an imperfect diagnostic test.
Matthias Flor +4 more
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Bayesian Methodologies with pyhf [PDF]
EPJ Web of Conferencesbayesian_pyhf is a Python package that allows for the parallel Bayesian and frequentist evaluation of multi-channel binned statistical models. The Python library pyhf is used to build such models according to the HistFactory framework and already ...
Feickert Matthew +2 more
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Intrinsic Bayesian estimation of linear time series models
Statistical Theory and Related Fields, 2021 Intrinsic loss functions (such as the Kullback–Leibler divergence, i.e. the entropy loss) have been used extensively in place of conventional loss functions for independent samples. But applications in serially correlated samples are scant.
Shawn Ni, Dongchu Sun
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Results in Physics, 2022 In recent years, statisticians have grown increasingly engaged in research of mixture models, particularly in the previous decade, without adequate consideration of challenge of estimating the parameters of mixture models from a frequentist perspective ...
Showkat Ahmad Lone +3 more
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Gaussian Processes for Predictive QSAR Modeling of Chromatographic Processes
Biotechnology and Bioengineering, EarlyView.ABSTRACT Chromatography is a key unit operation in the biopharmaceutical manufacturing process used for protein purification and polishing. Design and optimization of these processes are resource‐intensive resulting from the complex combinatorial design space.
Harini Narayanan +7 more
wiley +1 more source