effective_sample_size

Computes the effective sample size from historical data given a
discounting parameter.

Implements conjugate power priors for efficient Bayesian analysis
of normal data. Power priors allow principled incorporation of historical
information while controlling the degree of borrowing through a discounting
parameter (Ibrahim and Chen (2000) <doi:10.1214/ss/1009212519>). This
package provides closed-form conjugate representations for both univariate
and multivariate normal data using Normal-Inverse-Chi-squared and
Normal-Inverse-Wishart distributions, eliminating the need for MCMC sampling.
The conjugate framework builds upon standard Bayesian methods described in
Gelman et al. (2013, ISBN:978-1439840955).

Yusuke Yamaguchi

powerprior

Conjugate Power Priors for Bayesian Analysis of Normal Data

Yifei Huang

effective_sample_size function

<dl><dt>m</dt>
<dd>Sample size of historical data</dd>
<dt>a0</dt>
<dd>Discounting parameter</dd></dl>

Arguments

Calculate Effective Sample Size — effective_sample_size

<dl>

<dt>m</dt>
<dd>Sample size of historical data</dd>


<dt>a0</dt>
<dd>Discounting parameter</dd>

</dl>

effective_sample_size: Calculate Effective Sample Size

Description

Usage

Value

Arguments

Examples