SSS_hierarchical_prior

Compute marginal posterior probabilities (slab probabilities) that data points have
 non-zero mean for the hierarchical prior.

Contains fast functions to calculate the exact Bayes posterior
for the Sparse Normal Sequence Model, implementing the algorithms
described in Van Erven and Szabo (2021,
<doi:10.1214/20-BA1227>). For general hierarchical
priors, sample sizes up to 10,000 are feasible within half an hour
on a standard laptop. For beta-binomial spike-and-slab priors, a
faster algorithm is provided, which can handle sample sizes of
100,000 in half an hour. In the implementation, special care has
been taken to assure numerical stability of the methods even for
such large sample sizes.

Tim van Erven

SequenceSpikeSlab

Exact Bayesian Model Selection Methods for the Sparse Normal
Sequence Model

Steven de Rooij

Botond Szabo

SSS_hierarchical_prior function

<dl><dt>log_phi_psi</dt>
<dd>List {logphi, logpsi} containing two vectors of the same length n
that represent a preprocessed version of the data. logphi and logpsi should contain
the logs of the phi and psi densities of the data points, as produced for instance
by <code>SSS_log_phi_psi_Laplace</code> or <code>SSS_log_phi_psi_Cauchy</code></dd>
<dt>logprior</dt>
<dd>vector of length n+1 with components logprior[p]=log(pi_n(p)) for
\(p=0,...,n\)</dd>
<dt>show_progress</dt>
<dd>Boolean that indicates whether to show a progress bar</dd></dl>

Arguments

Compute marginal posterior probabilities (slab probabilities) that data points have
  non-zero mean for the hierarchical prior. — SSS_hierarchical_prior

<dl>

<dt>log_phi_psi</dt>
<dd>List {logphi, logpsi} containing two vectors of the same length n
that represent a preprocessed version of the data. logphi and logpsi should contain
the logs of the phi and psi densities of the data points, as produced for instance
by <code>SSS_log_phi_psi_Laplace</code> or <code>SSS_log_phi_psi_Cauchy</code></dd>


<dt>logprior</dt>
<dd>vector of length n+1 with components logprior[p]=log(pi_n(p)) for
\(p=0,...,n\)</dd>


<dt>show_progress</dt>
<dd>Boolean that indicates whether to show a progress bar</dd>

</dl>

SSS_hierarchical_prior: Compute marginal posterior probabilities (slab probabilities) that data points have non-zero mean for the hierarchical prior.

Description

Usage

Value

Arguments