Stochastic Newton Sampler (SNS) is a MetropolisHastingsbased, Markov Chain Monte Carlo sampler for twice differentiable, logconcave probability density functions (PDFs) where the proposal density function is a multivariate Gaussian resulting from a secondorder Taylorseries expansion of logdensity around the current point. The mean of the Gaussian proposal is the full NewtonRaphson step from the current point. A Boolean flag allows for switching from SNS to NewtonRaphson optimization (by choosing the mean of proposal function as next point). This can be used during burnin to get close to the mode of the PDF (which is unique due to concavity). For highdimensional 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 logdensity are provided.
Package details 


Author  Alireza S. Mahani, Asad Hasan, Marshall Jiang, Mansour T.A. Sharabiani 
Date of publication  20161025 10:31:12 
Maintainer  Alireza Mahani <alireza.s.mahani@gmail.com> 
License  GPL (>= 2) 
Version  1.1.2 
Package repository  View on CRAN 
Installation 
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