Matrix object with each sample (possibly multivariate) as a row.

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).

method

Computes the effective sample size of MCMC chains.<p></p><p>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.</p>

Stochastic Newton Sampler (SNS) is a Metropolis-Hastings-based, Markov Chain Monte Carlo sampler for twice differentiable, log-concave probability densities 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.

ess: Effective Sample Size Calculator

Description

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References