Just Another Gibbs Sampler (JAGS)

Depaoli, Sarah; Clifton, James P.; Cobb, Patrice R.

doi:10.3102/1076998616664876

Cited by 77 publications

(38 citation statements)

References 18 publications

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“…There are a lot of IRT specific software packages available, in particular in the programming language R (R Core Team 2019), for example, mirt (Chalmers 2012), sirt (Robitzsch 2019), or TAM (Robitzsch et al 2019; see Bürkner 2019 for a detailed comparison). In addition to these more specialized packages, general purpose probabilistic programming languages can be used to specify and fit Bayesian IRT models, for example, BUGS (Lunn et al 2009; see also Curtis 2010), JAGS (Plummer 2013; see also Depaoli et al 2016;Zhan et al 2019), or Stan (Carpenter et al 2017; see also Allison and Au 2018; Luo and Jiao 2018). In this paper, I use the brms package (Bürkner 2017(Bürkner , 2018, a higher level interface to Stan, which is not focused specifically on IRT models but more generally on (Bayesian) regression models.…”

Section: Irt Models As Regression Modelsmentioning

confidence: 99%

Analysing Standard Progressive Matrices (SPM-LS) with Bayesian Item Response Models

Bürkner

2020

J. Intell.

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Raven’s Standard Progressive Matrices (SPM) test and related matrix-based tests are widely applied measures of cognitive ability. Using Bayesian Item Response Theory (IRT) models, I reanalyzed data of an SPM short form proposed by Myszkowski and Storme (2018) and, at the same time, illustrate the application of these models. Results indicate that a three-parameter logistic (3PL) model is sufficient to describe participants dichotomous responses (correct vs. incorrect) while persons’ ability parameters are quite robust across IRT models of varying complexity. These conclusions are in line with the original results of Myszkowski and Storme (2018). Using Bayesian as opposed to frequentist IRT models offered advantages in the estimation of more complex (i.e., 3–4PL) IRT models and provided more sensible and robust uncertainty estimates.

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Section: Irt Models As Regression Modelsmentioning

confidence: 99%

Analysing Standard Progressive Matrices (SPM-LS) with Bayesian Item Response Models

Bürkner

2020

J. Intell.

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“…Nowadays, Markov-Chain-Monte-Carlo (MCMC) methods are used (Gelfand and Smith, 1990;Tierney, 1994;Chib and Greenberg, 1995), which circumvent the computation of these often-intractable integrals by sampling directly from the nonnormalized posterior. Implementations of MCMC samplers exist as either standalone versions (Lunn et al, 2009;Gelman et al, 2015;Depaoli et al, 2016) or they are implemented for popular languages like R (Martin et al, 2011;Lindgren and Rue, 2015;Denwood, 2016) and Python (Patil et al, 2010;Foreman-Mackey et al, 2013).…”

Section: Challenges Of Bayesianismmentioning

confidence: 99%

What We Talk About When We Talk About Uncertainty. Toward a Unified, Data-Driven Framework for Uncertainty Characterization in Hydrogeology

2019

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“…In particular, we fit values of the information bonus A, spatial bias B, variance of internal noise σ 2 int , and variance of external noise, σ 2 ext for each participant in each horizon. Model fitting was performed using the MATJAGS and JAGS software (Depaoli et al, 2016 with full details given in the Methods.…”

Section: Model Fittingmentioning

confidence: 99%

“…Model parameters, priors, hyperparameters and hyperpriors.The model was fit to the data using Markov Chain Monte Carlo approach implemented in the JAGS package(Depaoli et al, 2016) via the MATJAGS interface (psiexp.ss.uci.edu/research/programs data/jags).…”

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confidence: 99%

Separating random and deterministic sources of computational noise in explore-exploit decisions

Wang¹,

Rc²

2018

Preprint

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Human decision making is inherently variable. While this variability is often seen as a sign of suboptimality in human behavior, recent work suggests that randomness can actually be adaptive. An example arises when we must choose between exploring unknown options or exploiting options we know well. A little randomness in these 'explore-exploit' decisions is remarkably effective as it encourages us to explore options we might otherwise ignore. Moreover, people actually use such 'random exploration' in practice, increasing their behavioral variability when it is more valuable to explore. Despite this progress, the nature of adaptive 'decision noise' for exploration is unknown -specifically whether it is generated internally, from stochastic processes in the brain, or externally, from stochastic stimuli in the world. Here we show that, while both internal and external noise drive variability in behavior, the noise driving random exploration is predominantly internal. This suggests that random exploration depends on adaptive noise processes in the brain which are subject to cognitive control.

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Just Another Gibbs Sampler (JAGS)

Cited by 77 publications

References 18 publications

Analysing Standard Progressive Matrices (SPM-LS) with Bayesian Item Response Models

Analysing Standard Progressive Matrices (SPM-LS) with Bayesian Item Response Models

What We Talk About When We Talk About Uncertainty. Toward a Unified, Data-Driven Framework for Uncertainty Characterization in Hydrogeology

Separating random and deterministic sources of computational noise in explore-exploit decisions

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