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      The JASP guidelines for conducting and reporting a Bayesian analysis

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          Abstract

          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. Here we offer specific guidelines for four different stages of Bayesian statistical reasoning in a research setting: planning the analysis, executing the analysis, interpreting the results, and reporting the results. The guidelines for each stage are illustrated with a running example. Although the guidelines are geared towards analyses performed with the open-source statistical software JASP, most guidelines extend to Bayesian inference in general.

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          Most cited references54

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          brms: An R Package for Bayesian Multilevel Models Using Stan

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              Stan: A Probabilistic Programming Language

              Stan is a probabilistic programming language for specifying statistical models. A Stan program imperatively defines a log probability function over parameters conditioned on specified data and constants. As of version 2.14.0, Stan provides full Bayesian inference for continuous-variable models through Markov chain Monte Carlo methods such as the No-U-Turn sampler, an adaptive form of Hamiltonian Monte Carlo sampling. Penalized maximum likelihood estimates are calculated using optimization methods such as the limited memory Broyden-Fletcher-Goldfarb-Shanno algorithm. Stan is also a platform for computing log densities and their gradients and Hessians, which can be used in alternative algorithms such as variational Bayes, expectation propagation, and marginal inference using approximate integration. To this end, Stan is set up so that the densities, gradients, and Hessians, along with intermediate quantities of the algorithm such as acceptance probabilities, are easily accessible. Stan can be called from the command line using the cmdstan package, through R using the rstan package, and through Python using the pystan package. All three interfaces support sampling and optimization-based inference with diagnostics and posterior analysis. rstan and pystan also provide access to log probabilities, gradients, Hessians, parameter transforms, and specialized plotting.
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                Author and article information

                Journal
                Psychonomic Bulletin & Review
                Psychon Bull Rev
                Springer Science and Business Media LLC
                1069-9384
                1531-5320
                October 09 2020
                Article
                10.3758/s13423-020-01798-5
                63c366c6-e61e-4c12-812f-92ca4cb8c0f1
                © 2020

                https://creativecommons.org/licenses/by/4.0

                https://creativecommons.org/licenses/by/4.0

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