Results 21 to 30 of about 24,269,673 (319)

BEAST 2.5: An advanced software platform for Bayesian evolutionary analysis

open access: yesbioRxiv, 2018
Elaboration of Bayesian phylogenetic inference methods has continued at pace in recent years with major new advances in nearly all aspects of the joint modelling of evolutionary data.
R. Bouckaert   +24 more
semanticscholar   +1 more source

Bayesian Analysis of Tests with Unknown Specificity and Sensitivity

open access: yesmedRxiv, 2020
When testing for a rare disease, prevalence estimates can be highly sensitive to uncertainty in the specificity and sensitivity of the test. Bayesian inference is a natural way to propagate these uncertainties, with hierarchical modelling capturing ...
A. Gelman, B. Carpenter
semanticscholar   +1 more source

Spatial patterns and determinants of fertility levels among women of childbearing age in Nigeria

open access: yesSouth African Family Practice, 2017
Background: Despite aggressive measures to control the population in Nigeria, the population of Nigeria still remains worrisome. Increased birth rates have significantly contributed to Nigeria being referred to as the most populous country in Africa ...
Oluwayemisi O Alaba   +2 more
doaj   +1 more source

Chronological relationship between the fortified settlement of Kamennyi Ambar and the Kamennyi Ambar-5 cemetery in the Southern Trans-Urals: capabilities of the Bayesian statistics [PDF]

open access: yesВестник археологии, антропологии и этнографии, 2021
By means of the Bayesian analysis of radiocarbon dates, a comparison of chronologies of the Kamennyi Ambar settlement and the cemetery of Kamennyi Ambar-5 of the Late Bronze Age Syntashta-Petrovka period has been carried out.
Chechushkov I.V., Epimakhov A.V.
doaj   +1 more source

A posterior probability approach for gene regulatory network inference in genetic perturbation data

open access: yesMathematical Biosciences and Engineering, 2016
Inferring gene regulatory networks is an important problem in systems biology. However, these networks can be hard to infer from experimental data because of the inherent variability in biological data as well as the large number of genes involved. We
William Chad Young   +2 more
doaj   +1 more source

Indexing Inefficacy of Efforts to Stop Escalation of COVID Mortality

open access: yesMathematics, 2022
Background: COVID-19 efforts were often ineffective in controlling the spread of the pandemic. Thus, identifying ineffective controls during a pandemic is vital.
Ramalingam Shanmugam   +3 more
doaj   +1 more source

A Bayesian Approach to Modeling Risk of Hospital Admissions Associated With Schizophrenia Accounting for Underdiagnosis of the Disorder in Administrative Records

open access: yesComputational Psychiatry, 2018
Schizophrenia is a debilitating serious mental illness characterized by a complex array of symptoms with varying severity and duration. Patients may seek treatment only intermittently, contributing to challenges diagnosing the disorder.
Eileen M. Stock   +4 more
doaj   +1 more source

The JASP guidelines for conducting and reporting a Bayesian analysis

open access: yesPsychonomic Bulletin & Review, 2019
Despite the increasing popularity of Bayesian inference in empirical research, few practical guidelines provide detailed recommendations for how to apply Bayesian procedures and interpret the results.
Johnny B van Doorn   +19 more
semanticscholar   +1 more source

scCODA is a Bayesian model for compositional single-cell data analysis

open access: yesNature Communications, 2021
Compositional changes of cell types are main drivers of biological processes. Their detection through single-cell experiments is difficult due to the compositionality of the data and low sample sizes.
M. Büttner   +4 more
semanticscholar   +1 more source

Bayesian correction for covariate measurement error: a frequentist evaluation and comparison with regression calibration [PDF]

open access: yes, 2016
Bayesian approaches for handling covariate measurement error are well established, and yet arguably are still relatively little used by researchers. For some this is likely due to unfamiliarity or disagreement with the Bayesian inferential paradigm.
Bartlett, Jonathan W., Keogh, Ruth H.
core   +2 more sources

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