R package 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.
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References in zbMATH (referenced in 4 articles , 1 standard article )
Showing results 1 to 4 of 4.
- Mauff, Katya; Erler, Nicole S.; Kardys, Isabella; Rizopoulos, Dimitris: Pairwise estimation of multivariate longitudinal outcomes in a Bayesian setting with extensions to the joint model (2021)
- Alireza S. Mahani, Asad Hasan, Marshall Jiang, Mansour T. A. Sharabiani: Stochastic Newton Sampler: The R Package sns (2016) not zbMATH
- Mahani, Alireza S.; Sharabiani, Mansour T. A.: SIMD parallel MCMC sampling with applications for big-data Bayesian analytics (2015)
- Alireza S. Mahani, Mansour T.A. Sharabiani: Multivariate-from-Univariate MCMC Sampler: R Package MfUSampler (2014) arXiv