Computes the effective sample size of MCMC chains, using the algorithm in Section 2.3 of the paper by Madeline Thompson. The algorithm is taken from earlier work on `Initial Sequence Estimators' by multiple authors.

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. Note: Formerly available versions of the MfUSampler can be obtained from the archive <https://cran.r-project.org/src/contrib/Archive/MfUSampler/>.

Alireza Mahani

Stochastic Newton Sampler (SNS)

ess function

<dl> <dt>x</dt>
<dd>Matrix object with each sample (possibly multivariate) as a row. Effective sample size calculation is done independently for each column of <code>x</code>.</dd> <dt>method</dt>
<dd>Method of calculating effective size. Current options are "coda" which calls <code>effectiveSize</code> function in <code>coda</code> package, and "ise" which uses the 'Initial Sequence Estimators' method described in Section 2.3 of Thompson (2010).</dd></dl>

Arguments

Alireza S. Mahani, Asad Hasan, Marshall Jiang, Mansour T.A. Sharabiani

Author

Effective Sample Size Calculator — ess

<dl>

 <dt>x</dt>
<dd>Matrix object with each sample (possibly multivariate) as a row. Effective sample size calculation is done independently for each column of <code>x</code>.</dd>

 <dt>method</dt>
<dd>Method of calculating effective size. Current options are "coda" which calls <code>effectiveSize</code> function in <code>coda</code> package, and "ise" which uses the 'Initial Sequence Estimators' method described in Section 2.3 of Thompson (2010).</dd>

</dl>

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ess: Effective Sample Size Calculator

Description

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