RcppSMC

Background

Sequential Monte Carlo methods, including particle filters, are a powerful simulation-based technique for approximating probability distributions and associated normalizing constants. See, e.g., Particle Filtering Tutorial for one recent introduction to these methods.

There is a perception that such methods are difficult to implement and good software support is needed.

Related work

The majority of software support for such methods is in the form of libraries for lower-level languages (smctc,vsmc) or standalone programs (libBI,biips).

RcppSMC is an attempt to leverage the power of RcppSMC and smctc to combine the flexible, friendly interface of R with the speed and power of C++ in this context.

Details of your coding project

There is considerable flexibility. The intention is that the project will begin with familiarization with SMC and RcppSMC, before the student goes on to complete some simple tasks to gain confidence and experience with the existing software before moving on to produce substantial enhancements and extensions to the existing skeleton.

Some more concrete items:

• update the packaging / interface, i.e. use Rcpp Attributes

• switch to (Rcpp)Armadillo vectors internally (both for efficiency reasons and their expressiveness / readability)

• investigate vectorizing algorithm internals

• modify underlying C++ code to allow normalizing constant estimates to be obtained more easily (a prototypical implementation is available) as these are of widespread interest in model selection, particle MCMC and other contexts and are not readily available with the current implementation

• add additional prototypical algorithm examples, perhaps for more advanced particle filters, model selection or particle MCMC.

Expected impact

A successful project will lead to a more stable, more feature complete package allowing relatively easy development of fast implementation of SMC algorithms within R. This could dramatically increase the use of such methods and make R more attractive to developers working with SMC-algorithms and have impact both within academia and more widely.

Mentors

Dirk Edelbuettel and Adam Johansen

Tests

Several tests that potential students can do to demonstrate their capabilities for this particular project. Ask some hard questions that will give you insight about how the students write code to solve problems. You'll see that the harder the questions that you ask, the easier it will be for you to choose between the students that apply for your project! Please modify the suggestions below to make them specific for your project.

• Easy: download Rcpp and apply the example particle filters to simulated data.
• Medium: implement a Kalman filter in R.
• Hard: implement a Kalman filter in Rcpp and package it.

Solutions of tests

Students, please post a link to your test results here.