Bayesian statistics and modelling
Bayesian statistics is an approach to data analysis based on Bayes' theorem, where
available knowledge about parameters in a statistical model is updated with the information …
available knowledge about parameters in a statistical model is updated with the information …
Bayesian analysis reporting guidelines
JK Kruschke - Nature human behaviour, 2021 - nature.com
Previous surveys of the literature have shown that reports of statistical analyses often lack
important information, causing lack of transparency and failure of reproducibility. Editors and …
important information, causing lack of transparency and failure of reproducibility. Editors and …
[BOOK][B] Violence against women prevalence estimates, 2018: global, regional and national prevalence estimates for intimate partner violence against women and …
World Health Organization - 2021 - books.google.com
This report is based on an analysis of available prevalence data from surveys and studies
conducted between 2000 and 2018, obtained through a systematic and comprehensive …
conducted between 2000 and 2018, obtained through a systematic and comprehensive …
Building eco-surplus culture among urban residents as a novel strategy to improve finance for conservation in protected areas
The rapidly declining biosphere integrity, representing one of the core planetary boundaries,
is alarming. In particular, the global numbers of mammals, birds, fishes, and plants declined …
is alarming. In particular, the global numbers of mammals, birds, fishes, and plants declined …
Rank-normalization, folding, and localization: An improved R ̂ for assessing convergence of MCMC (with discussion)
Abstract Markov chain Monte Carlo is a key computational tool in Bayesian statistics, but it
can be challenging to monitor the convergence of an iterative stochastic algorithm. In this …
can be challenging to monitor the convergence of an iterative stochastic algorithm. In this …
Active learning narrows achievement gaps for underrepresented students in undergraduate science, technology, engineering, and math
EJ Theobald, MJ Hill, E Tran… - Proceedings of the …, 2020 - National Acad Sciences
We tested the hypothesis that underrepresented students in active-learning classrooms
experience narrower achievement gaps than underrepresented students in traditional …
experience narrower achievement gaps than underrepresented students in traditional …
[BOOK][B] Flexible imputation of missing data
S Van Buuren - 2018 - books.google.com
Missing data pose challenges to real-life data analysis. Simple ad-hoc fixes, like deletion or
mean imputation, only work under highly restrictive conditions, which are often not met in …
mean imputation, only work under highly restrictive conditions, which are often not met in …
[BOOK][B] Bayesian cognitive modeling: A practical course
MD Lee, EJ Wagenmakers - 2014 - books.google.com
Bayesian inference has become a standard method of analysis in many fields of science.
Students and researchers in experimental psychology and cognitive science, however, have …
Students and researchers in experimental psychology and cognitive science, however, have …
[HTML][HTML] Introduction to Bayesian Mindsponge Framework analytics: An innovative method for social and psychological research
Abstract The paper introduces Bayesian Mindsponge Framework (BMF) analytics, a new
analytical tool for investigating socio, psychological, and behavioral phenomena. The …
analytical tool for investigating socio, psychological, and behavioral phenomena. The …
Generalized linear mixed models: a practical guide for ecology and evolution
How should ecologists and evolutionary biologists analyze nonnormal data that involve
random effects? Nonnormal data such as counts or proportions often defy classical statistical …
random effects? Nonnormal data such as counts or proportions often defy classical statistical …