sns: Stochastic Newton Sampler (SNS)
Version 1.1.2

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.

Package details

AuthorAlireza S. Mahani, Asad Hasan, Marshall Jiang, Mansour T.A. Sharabiani
Date of publication2016-10-25 10:31:12
MaintainerAlireza Mahani <>
LicenseGPL (>= 2)
Package repositoryView on CRAN
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sns documentation built on May 29, 2017, 1:43 p.m.