sns: Stochastic Newton Sampler (SNS)
Stochastic Newton Sampler (SNS) is a Metropolis-Hastings-based, Markov Chain Monte Carlo sampler for twice differentiable, log-concave probability density functions (PDFs) where the proposal density function is a multivariate Gaussian resulting from a second-order Taylor-series expansion of log-density around the current point. The mean of the Gaussian proposal is the full Newton-Raphson step from the current point. A Boolean flag allows for switching from SNS to Newton-Raphson optimization (by choosing the mean of proposal function as next point). This can be used during burn-in to get close to the mode of the PDF (which is unique due to concavity). For high-dimensional densities, mixing can be improved via 'state space partitioning' strategy, in which SNS is applied to disjoint subsets of state space, wrapped in a Gibbs cycle. Numerical differentiation is available when analytical expressions for gradient and Hessian are not available. Facilities for validation and numerical differentiation of log-density are provided.
- Alireza S. Mahani, Asad Hasan, Marshall Jiang, Mansour T.A. Sharabiani
- Date of publication
- 2016-10-25 10:31:12
- Alireza Mahani <firstname.lastname@example.org>
- GPL (>= 2)
- Effective Sample Size Calculator
- Plotting "sns" Objects
- Sample-based prediction using "sns" Objects
- Stochastic Newton Sampler (SNS)
- Utility function for validating log-density
- Utility function for augmentation of a log-density function...
- Utility Functions for Creating and Validating State Space...
- Drawing multiple samples using Stochastic Newton Sampler
- Summarizing "sns" Objects
Files in this package