Programming With Models: Writing Statistical Algorithms for General Model Structures With NIMBLE
University of California, Berkeley · University of California, Davis
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…
Citation impact
- FWCI
- 33.57
- Percentile
- 100%
- References
- 36
Authors
6- PDPerry de ValpineCorresponding
University of California, Berkeley
- DTDaniel Turek
University of California, Berkeley
- CJChristopher J. Paciorek
University of California, Berkeley
- CAClifford Anderson-Bergman
University of California, Berkeley
- DTDuncan Temple Lang
University of California, Davis
Topics & keywords
- Computation
- Markov chain Monte Carlo
- Programming paradigm
- Monte Carlo method
- Language model
- Statistical model
- Block (permutation group theory)
- Computational statistics