Programming With Models: Writing Statistical Algorithms for General Model Structures With NIMBLE
By P. de Valpine, Daniel Turek, Christopher J. Paciorek, Clifford Anderson-Bergman, Duncan Temple Lang, Rastislav Bodík. 2015
1,061 citations recorded by the source corpus.
ABSTRACT We describe NIMBLE, a system for programming statistical algorithms for general model structures within R. NIMBLE is designed to meet three challenges: flexible model specification, a language for programming algorithms that can use different models, and a balance between high-level programmability and execution efficiency. For model specification, NIMBLE extends the BUGS language and creates model objects, which can manipulate variables, calculate log probability values, generate simulations, and query the relationships among variables. For algorithm programming, NIMBLE provides functions that operate with model objects using two stages of evaluation. The first stage allows specialization of a function to a particular model and/or nodes, such as creating a Metropolis-Hastings sampler for a particular block of nodes. The second stage allows repeated execution of computations using the results of the first stage. To achieve efficient second-stage computation, NIMBLE compiles models and functions via C++, using the Eigen library for linear algebra, and provides the user with an interface to compiled objects. The NIMBLE language represents a compilable domain-specific language (DSL) embedded within R. This article provides an overview of the design and rationale for NIMBLE along with illustrative examples including importance sampling, Markov chain Monte Carlo (MCMC) and Monte Carlo expectation maximization (MCEM). Supplementary materials for this article are available online.
Subfields: Artificial Intelligence, Information Systems and Management, Management Science and Operations Research
View Paper: https://www.semanticscholar.org/paper/2f7ccbfed5d3dd2fd2e746b1ab116c28f3ec5223
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In Artificial Intelligence
- Paul-Christian Burkner
- Aki Vehtari
- Andrew Gelman
- Daniel P. Simpson
- Bob Carpenter
- Paul Fearnhead
- Christopher Nemeth
- Yuling Yao
In Information Systems and Management
In Management Science and Operations Research
- Paul-Christian Burkner
- Andrew Gelman
- Daniel P. Simpson
- Christopher Nemeth
- Aki Vehtari
- Paul Fearnhead
Citations in the corpus
- Rank-Normalization, Folding, and Localization: An Improved Rˆ for Assessing Convergence of MCMC (with Discussion) (2019, methodology): “…al. (2013). packages for MCMC sampling such as Stan (Carpenter et al., 2017), JAGS (Plummer, 2003), WinBUGS (Lunn et al., 2000), OpenBUGS (Lunn et al., 2009), PyMC3 (Salvatier et al., 2016), and NIMBLE (de Valpine et al., 2017), which together are estimated to have hundreds of thousands of users.”
- bridgesampling: An R Package for Estimating Normalizing Constants (2017, neutral): “…methods for objects of class mcmc , mcmc.list (Plummer et al. 2006), stanfit ( Stan Development Team 2016a), matrix , rjags (Plummer 2016; Su and Yajima 2015), runjags (Denwood 2016), stanreg ( Stan Development Team 2016b), and for MCMC_refClass objects produced by nimble (de Valpine et al. 2017).”
- Stochastic Gradient Markov Chain Monte Carlo (2019, methodology): “A major success of traditional MCMC algorithms, and their broad appeal in a range of application areas, is partly a result of freely available software, such as WinBUGS (Lunn et al. , 2000), JAGS (Plummer et al. , 2003), NIMBLE (de Valpine et al. , 2017) and STAN (Carpenter et al. , 2017).”