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Two day workshop: Flexible programming of MCMC and other methods for hierarchical and Bayesian models

We’ll be giving a two day workshop at the 43rd Annual Summer Institute of Applied Statistics at Brigham Young University (BYU) in Utah, June 19-20, 2018.

Abstract is below, and registration and logistics information can be found here.

This workshop provides a hands-on introduction to using, programming, and sharing Bayesian and hierarchical modeling algorithms using NIMBLE (r-nimble.org). In addition to learning the NIMBLE system, users will develop hands-on experience with various computational methods. NIMBLE is an R-based system that allows one to fit models specified using BUGS/JAGS syntax but with much more flexibility in defining the statistical model and the algorithm to be used on the model. Users operate from within R, but NIMBLE generates C++ code for models and algorithms for fast computation. I will open with an overview of creating a hierarchical model and fitting the model using a basic MCMC, similarly to how one can use WinBUGS, JAGS, and Stan. I will then discuss how NIMBLE allows the user to modify the MCMC – changing samplers and specifying blocking of parameters. Next I will show how to extend the BUGS syntax with user-defined distributions and functions that provide flexibility in specifying a statistical model of interest. With this background we can then explore the NIMBLE programming system, which allows one to write new algorithms not already provided by NIMBLE, including new MCMC samplers, using a subset of the R language. I will then provide examples of non-MCMC algorithms that have been programmed in NIMBLE and how algorithms can be combined together, using the example of a particle filter embedded within an MCMC. We will see new functionality in NIMBLE that allows one to fit Bayesian nonparametric models and spatial models. I will close with a discussion of how NIMBLE enables sharing of new methods and reproducibility of research. The workshop will include a number of breakout periods for participants to use and program MCMC and other methods, either on example problems or problems provided by participants. In addition, participants will see NIMBLE’s flexibility in action in several real problems.

Version 0.6-10 of NIMBLE released

We’ve released the newest version of NIMBLE on CRAN and on our website. Version 0.6-10 primarily contains updates to the NIMBLE internals that may speed up building and compilation of models and algorithms, as well as a few bug fixes.

Changes include:

  • some steps of model and algorithm building and compilation are faster;
  • compiled execution with multivariate distributions or function arguments may be faster;
  • data can now be provided as a numeric data frame rather than a matrix;
  • to run WAIC, a user now must set ‘enableWAIC’ to TRUE, either in NIMBLE’s options or as an argument to buildMCMC();
  • if ‘enableWAIC’ is TRUE, buildMCMC() will now check to make sure that the nodes monitored by the MCMC algorithm will lead to a valid WAIC calculation; and
  • the use of identityMatrix() is deprecated in favor of diag().

Please see the NEWS file in the installed package for more details

NIMBLE webinar Friday April 13

We’ll be presenting a webinar on NIMBLE, hosted by the Eastern North America Region of the International Biometric Society. Details are as follows.
Programming with hierarchical statistical models: An introduction to the BUGS-compatible NIMBLE system for MCMC and more
Friday, April 13, 2018
11:00 a.m. – 1:00 p.m. EST
Must register before April 12. You can register here. (You’ll need to create an account on the ENAR website and there is a modest fee – from $25 for ENAR student members up through $85 for non-IBS members.)
This webinar will introduce attendees to the NIMBLE system for programming with hierarchical models in R. NIMBLE (r-nimble.org) is a system for flexible programming and dissemination of algorithms that builds on the BUGS language for declaring hierarchical models. NIMBLE provides analysts with a flexible system for using MCMC, sequential Monte Carlo and other techniques on user-specified models. It provides developers and methodologists with the ability to write algorithms in an R-like syntax that can be easily disseminated to users. C++ versions of models and algorithms are created for speed, but these are manipulated from R without any need for analysts or algorithm developers to program in C++. 

While analysts can use NIMBLE as a drop-in replacement for WinBUGS or JAGS, NIMBLE provides greatly enhanced functionality in a number of ways. The webinar will first show how to specify a hierarchical statistical model using BUGS syntax (including user-defined function and distributions) and fit that model using MCMC (including user customization for better performance). We will demonstrate the use of NIMBLE for biostatistical methods such as semiparametric random effects models and clustering models. We will close with a discussion of how to use the system to write algorithms for use with hierarchical models, including building and disseminating your own methods.

Presenter:
Chris Paciorek
Adjunct Professor, Statistical Computing Consultant
Department of Statistics, University of California, Berkeley

Version 0.6-9 of NIMBLE released

We’ve released the newest version of NIMBLE on CRAN and on our website. Version 0.6-9 is primarily a maintenance release with various bug fixes and fixes for CRAN packaging issues.

New features include:

  • dimensions in a model will now be determined from either ‘inits’ or ‘data’ if not otherwise available;
  • one can now specify “nBootReps = NA” in the runCrossValidate() function, which will prevent the Monte Carlo error from being calculated;
  • runCrossValidate() now returns the averaged loss over all k folds, instead of the summed loss;
  • We’ve added the besselK function to the NIMBLE language;
  • and a variety of bug fixes.

Please see the NEWS file in the installed package for more details

NIMBLE has a post-doc or software developer position open

The NIMBLE statistical software project at the University of California, Berkeley is looking for a post-doc or statistical software developer. NIMBLE is a tool for writing hierarchical statistical models and algorithms from R, with compilation via code-generated C++. Major methods currently include MCMC and sequential Monte Carlo, which users can customize and extend. More information can be found at https://R-nimble.org. Currently we seek someone with experience in computational statistical methods such as MCMC and excellent software development skills in R and C++. This could be someone with a Ph.D. in Statistics, Computer Science, or an applied statistical field in which they have done relevant work. Alternatively it could be someone with relevant experience in computational statistics and software engineering. The scope of work can include both core development of NIMBLE and development and application of innovative methods using NIMBLE, with specific focus depending on the background of the successful candidate. Applicants must have either a Ph.D. in a relevant field or have a proven record of relevant work. Please send cover letter, CV, and the names and contact information for three references to nimble.stats@gmail.com. Applications will be considered on a rolling basis starting 30 January, 2018.

Version 0.6-8 of NIMBLE released

We’ve released the newest version of NIMBLE on CRAN and on our website a week ago. Version 0.6-8 has a few new features, and more are on the way in the next few months.

New features include:

  • the proper Gaussian CAR (conditional autoregressive) model can now be used in BUGS code as dcar_proper, which behaves similarly to BUGS’ car.proper distribution;
  • a new nimbleMCMC function that provides one-line invocation of NIMBLE’s MCMC engine, akin to usage of JAGS and WinBUGS through R;
  • a new runCrossValidate function that will conduct k-fold cross-validation of NIMBLE models fit by MCMC;
  • dynamic indexing in BUGS code is now allowed by default;
  • and a variety of bug fixes and efficiency improvements.

Please see the NEWS file in the installed package for more details.

Version 0.6-6 of NIMBLE released!

We’ve just released the newest version of NIMBLE on CRAN and on our website. Version 0.6-6 has some important new features, and more are on the way in the next few months.

New features include:

  • dynamic indexes are now allowed in BUGS code — indexes of a variable no longer need to be constants but can be other nodes or functions of other nodes; for this release this is a beta feature that needs to be enabled with nimbleOptions(allowDynamicIndexing = TRUE);
  • the intrinsic Gaussian CAR (conditional autoregressive) model can now be used in BUGS code as dcar_normal, which behaves similarly to BUGS’ car.normal distribution;
  • optim is now part of the NIMBLE language and can be used in nimbleFunctions;
  • it is possible to call out to external compiled code or back to R functions from a nimbleFunction using nimbleExternalCall() and nimbleRcall() (this is an experimental feature);
  • the WAIC model selection criterion can be calculated using the calculateWAIC() method for MCMC objects;
  • the bootstrap and auxiliary particle filters can now return their ESS values;
  • and a variety of bug fixes.

Please see the NEWS file in the installed package for more details.

Finally, we’re deep in the midst of development work to enable automatic differentiation, Tensorflow as an alternative back-end computational engine, additional spatial models, and Bayesian nonparametrics.

 

Version 0.6-5 of NIMBLE released!

We’ve just released the newest version of NIMBLE on CRAN and on our website. Version 0.6-5 is mostly devoted to bug fixes and packaging fixes for CRAN, but there is some new functionality:

  • addition of the functions  c(), seq(), rep(), `:`, diag() for use in BUGS code;
  • addition of two improper distributions (dflat and dhalfflat) as well as the inverse-Wishart distribution;
  • the ability to estimate the asymptotic covariance of the estimates in NIMBLE’s MCEM algorithm;
  • the ability to use nimbleLists in any nimbleFunction, newly including nimbleFunctions without setup code;
  • and a variety of bug fixes and better error trapping.

Please see the NEWS file in the installed package for more details.

Better block sampling in MCMC with the Automated Factor Slice Sampler


One nice feature of NIMBLE’s MCMC system is that a user can easily write new samplers from R, combine them with NIMBLE’s samplers, and have them automatically compiled to C++ via the NIMBLE compiler. We’ve observed that block sampling using a simple adaptive multivariate random walk Metropolis-Hastings sampler doesn’t always work well in practice, so we decided to implement the Automated Factor Slice sampler (AFSS) of Tibbits, Groendyke, Haran, and Liechty (2014) and see how it does on a (somewhat artificial) example with severe posterior correlation problems.

Roughly speaking, the AFSS works by conducting univariate slice sampling in directions determined by the eigenvectors of the marginal posterior covariance matrix for blocks of parameters in a model. So far, we’ve found the AFSS often outperforms random walk block sampling. To compare performance, we look at MCMC efficiency, which we define for each parameter as effective sample size (ESS) divided by computation time. We define overall MCMC efficiency as the minimum MCMC efficiency of all the parameters, because one needs all parameters to be well mixed.

We’ll demonstrate the performance of the AFSS on the correlated state space model described in Turek, de
Valpine, Paciorek, Anderson-Bergman, and others (2017)
.

Model Creation

Assume x_{i} is the latent state and y_{i} is the observation at time i for i=1,\ldots,100. We define the state space model as

 x_{i} \sim N(a \cdot x_{i-1} + b, \sigma_{PN})
 y_{i} \sim N(x_{i}, \sigma_{OE})

for i = 2, \ldots, 100, with initial states

 x_{1} \sim N(\frac{b}{1-a}, \frac{\sigma_{PN}}{\sqrt{1-a^2}})
 y_{1} \sim N(x_{1}, \sigma_{OE})

and prior distributions

 a \sim Unif(-0.999, 0.999)
 b \sim N(0, 1000)
 \sigma_{PN} \sim Unif(0, 1)
 \sigma_{OE} \sim Unif(0, 1)

where N(\mu, \sigma) denotes a normal distribution with mean \mu and standard deviation \sigma.

A file named model_SSMcorrelated.RData with the BUGS model code, data, constants, and initial values for our model can be downloaded here.

## load the nimble library and set seed
library('nimble')
set.seed(1)
load('model_SSMcorrelated.RData')
## build and compile the model
stateSpaceModel <- nimbleModel(code = code,
                              data = data,
                              constants = constants,
                              inits = inits,
                              check = FALSE)

C_stateSpaceModel <- compileNimble(stateSpaceModel)

Comparing two MCMC Samplers

We next compare the performance of two MCMC samplers on the state space model described above. The first sampler we consider is NIMBLE’s RW_block sampler, a Metropolis-Hastings sampler with a multivariate normal proposal distribution. This sampler has an adaptive routine that modifies the proposal covariance to look like the empirical covariance of the posterior samples of the parameters. However, as we shall see below, this proposal covariance adaptation does not lead to efficient sampling for our state space model.

We first build and compile the MCMC algorithm.

RW_mcmcConfig <- configureMCMC(stateSpaceModel)
RW_mcmcConfig$removeSamplers(c('a', 'b', 'sigOE', 'sigPN'))
RW_mcmcConfig$addSampler(target = c('a', 'b', 'sigOE', 'sigPN'), type = 'RW_block')
RW_mcmc <- buildMCMC(RW_mcmcConfig)
C_RW_mcmc <- compileNimble(RW_mcmc, project = stateSpaceModel)

We next run the compiled MCMC algorithm for 10,000 iterations, recording the overall MCMC efficiency from the posterior output. The overall efficiency here is defined as min(\frac{ESS}{T}), where ESS denotes the effective sample size, and T the total run-time of the sampling algorithm. The minimum is taken over all parameters that were sampled. We repeat this process 5 times to get a very rough idea of the average minimum efficiency for this combination of model and sampler.

RW_minEfficiency <- numeric(5)
for(i in 1:5){
  runTime <- system.time(C_RW_mcmc$run(50000, progressBar = FALSE))['elapsed']
  RW_mcmcOutput <- as.mcmc(as.matrix(C_RW_mcmc$mvSamples))
  RW_minEfficiency[i] <- min(effectiveSize(RW_mcmcOutput)/runTime)
}
summary(RW_minEfficiency)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.3323  0.4800  0.5505  0.7567  0.7341  1.6869

Examining a trace plot of the output below, we see that the $a$ and $b$ parameters are mixing especially poorly.

plot(RW_mcmcOutput, density = FALSE)
plot of chunk plot-mcmc

Plotting the posterior samples of a against those of b reveals a strong negative correlation. This presents a problem for the Metropolis-Hastings sampler — we have found that adaptive algorithms used to tune the proposal covariance are often slow to reach a covariance that performs well for blocks of strongly correlated parameters.

plot.default(RW_mcmcOutput[,'a'], RW_mcmcOutput[,'b'])
plot of chunk plot-corr
cor(RW_mcmcOutput[,'a'], RW_mcmcOutput[,'b'])
## [1] -0.9201277

In such situations with strong posterior correlation, we’ve found the AFSS to often run much more efficiently, so we next build and compile an MCMC algorithm using the AFSS sampler. Our hope is that the AFSS sampler will be better able to to produce efficient samples in the face of high posterior correlation.

AFSS_mcmcConfig <- configureMCMC(stateSpaceModel)
AFSS_mcmcConfig$removeSamplers(c('a', 'b', 'sigOE', 'sigPN'))
AFSS_mcmcConfig$addSampler(target = c('a', 'b', 'sigOE', 'sigPN'), type = 'AF_slice')
AFSS_mcmc<- buildMCMC(AFSS_mcmcConfig)
C_AFSS_mcmc <- compileNimble(AFSS_mcmc, project = stateSpaceModel, resetFunctions = TRUE)

We again run the AFSS MCMC algorithm 5 times, each with 10,000 MCMC iterations.

AFSS_minEfficiency <- numeric(5)
for(i in 1:5){
  runTime <- system.time(C_AFSS_mcmc$run(50000, progressBar = FALSE))['elapsed']
  AFSS_mcmcOutput <- as.mcmc(as.matrix(C_AFSS_mcmc$mvSamples))
  AFSS_minEfficiency[i] <- min(effectiveSize(AFSS_mcmcOutput)/runTime)
}
summary(AFSS_minEfficiency)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   9.467   9.686  10.549  10.889  10.724  14.020

Note that the minimum overall efficiency of the AFSS sampler is approximately 28 times that of the RW_block sampler. Additionally, trace plots from the output of the AFSS sampler show that the a and b parameters are mixing much more effectively than they were under the RW_block sampler.

plot(AFSS_mcmcOutput, density = FALSE)
plot of chunk plot-mcmc-2

Tibbits, M. M, C. Groendyke, M. Haran, et al.
(2014).
“Automated factor slice sampling”.
In: Journal of Computational and Graphical Statistics 23.2, pp. 543–563.

Turek, D, P. de
Valpine, C. J. Paciorek, et al.

(2017).
“Automated parameter blocking for efficient Markov chain Monte Carlo sampling”.
In: Bayesian Analysis 12.2, pp. 465–490.

 

Thanks to r-bloggers (and their feed).

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