Fitting Bayesian item response models in Stata and Stan

Grant, Robert; Furr, Daniel C.; Carpenter, Bob; Gelman, Andrew

doi:10.1177/1536867x1701700206

Cited by 15 publications

(12 citation statements)

References 8 publications

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“…Grant et al . ), but come with caveats when trying to extrapolate. For example, our simulated models might not reflect nuances in real data, or might not be representative of typical models in other subfields of ecology.…”

Section: Discussionmentioning

confidence: 99%

Faster estimation of Bayesian models in ecology using Hamiltonian Monte Carlo

2016

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Summary1. Bayesian inference is a powerful tool to better understand ecological processes across varied subfields in ecology, and is often implemented in generic and flexible software packages such as the widely used BUGS family (BUGS, WinBUGS, OpenBUGS and JAGS). However, some models have prohibitively long run times when implemented in BUGS. A relatively new software platform called Stan uses Hamiltonian Monte Carlo (HMC), a family of Markov chain Monte Carlo (MCMC) algorithms which promise improved efficiency and faster inference relative to those used by BUGS. Stan is gaining traction in many fields as an alternative to BUGS, but adoption has been slow in ecology, likely due in part to the complex nature of HMC. 2. Here, we provide an intuitive illustration of the principles of HMC on a set of simple models. We then compared the relative efficiency of BUGS and Stan using population ecology models that vary in size and complexity. For hierarchical models, we also investigated the effect of an alternative parameterization of random effects, known as non-centering. 3.. For small, simple models there is little practical difference between the two platforms, but Stan outperforms BUGS as model size and complexity grows. Stan also performs well for hierarchical models, but is more sensitive to model parameterization than BUGS. Stan may also be more robust to biased inference caused by pathologies, because it produces diagnostic warnings where BUGS provides none. Disadvantages of Stan include an inability to use discrete parameters, more complex diagnostics and a greater requirement for hands-on tuning. 4. Given these results, Stan is a valuable tool for many ecologists utilizing Bayesian inference, particularly for problems where BUGS is prohibitively slow. As such, Stan can extend the boundaries of feasible models for applied problems, leading to better understanding of ecological processes. Fields that would likely benefit include estimation of individual and population growth rates, meta-analyses and cross-system comparisons and spatiotemporal models.

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Section: Discussionmentioning

confidence: 99%

Faster estimation of Bayesian models in ecology using Hamiltonian Monte Carlo

2016

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“…Recent studies introduced the NUTS algorithm (Hoffman and Gelman, ) which is an extension of the Hamiltonian Monte Carlo (HMC) algorithm (Duane, Kennedy, Pendleton, & Roweth, ; Neal, , ) as a replacement for the MH or GS methods. Hoffman and Gelman (), Granty, Furrz, Carpenter, & Gelman (), and Nugroho and Morimoto () claim that the HMC and NUTS algorithms provide more accurate results compared to other MCMC methods in different statistical models. In this paper, we explore the use of the NUTS algorithm in the context of CDMs.…”

Section: Introductionmentioning

confidence: 99%

Estimating the DINA model parameters using the No‐U‐Turn Sampler

et al. 2017

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The deterministic inputs, noisy, "and" gate (DINA) model is a popular cognitive diagnosis model (CDM) in psychology and psychometrics used to identify test takers' profiles with respect to a set of latent attributes or skills. In this work, we propose an estimation method for the DINA model with the No-U-Turn Sampler (NUTS) algorithm, an extension to Hamiltonian Monte Carlo (HMC) method. We conduct a simulation study in order to evaluate the parameter recovery and efficiency of this new Markov chain Monte Carlo method and to compare it with two other Bayesian methods, the Metropolis Hastings and Gibbs sampling algorithms, and with a frequentist method, using the Expectation-Maximization (EM) algorithm. The results indicated that NUTS algorithm employed in the DINA model properly recovers all parameters and is accurate for all simulated scenarios. We apply this methodology in the mental health area in order to develop a new method of classification for respondents to the Beck Depression Inventory. The implementation of this method for the DINA model applied to other psychological tests has the potential to improve the medical diagnostic process.

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“…These extend beyond the current (Stata 14.2) capability of bayesmh, which is explicitly for regression. In our companion article (Grant et al 2017), we describe the functionality of Stan and advantages of its algorithm. In this article, we give a brief overview of Hamiltonian Monte Carlo in intuitive terms, set out the syntax of the commands, and present a worked example.…”

Section: Introductionmentioning

confidence: 99%

Introducing the StataStan Interface for Fast, Complex Bayesian Modeling Using Stan

Grant

Carpenter

Furr

et al. 2017

The Stata Journal: Promoting communications on statistics and S

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In this article, we present StataStan, an interface that allows simulation-based Bayesian inference in Stata via calls to Stan, the flexible, open-source Bayesian inference engine. Stan is written in C++, and Stata users can use the commands stan and windowsmonitor to run Stan programs from within Stata. We provide a brief overview of Bayesian algorithms, details of the commands available from Statistical Software Components, considerations for users who are new to Stan, and a simple example. Stan uses a different algorithm than bayesmh, BUGS, JAGS, SAS, and MLwiN. This algorithm provides considerable improvements in efficiency and speed. In a companion article, we give an extended comparison of StataStan and bayesmh in the context of item response theory models.

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Fitting Bayesian item response models in Stata and Stan

Cited by 15 publications

References 8 publications

Faster estimation of Bayesian models in ecology using Hamiltonian Monte Carlo

Faster estimation of Bayesian models in ecology using Hamiltonian Monte Carlo

Estimating the DINA model parameters using the No‐U‐Turn Sampler

Introducing the StataStan Interface for Fast, Complex Bayesian Modeling Using Stan

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