Normalized Power Prior Bayesian Analysis
MCMC Sampling for Poisson Population using Normalized Power Prior with...
MCMC Sampling for Poisson Population of multiple historical data using...
MCMC Sampling for Poisson Population of multiple ordered historical da...
MCMC Sampling for Poisson Population of multiple ordered historical da...
MCMC Sampling for Bernoulli Population using Normalized Power Prior
MCMC Sampling for Bernoulli Population with Multiple Historical Data u...
MCMC Sampling for Bernoulli Population of multiple historical data usi...
MCMC Sampling for Bernoulli Population using Normalized Power Prior
MCMC Sampling for Bernoulli Population of multiple ordered historical ...
MCMC Sampling for Bernoulli Population of multiple ordered historical ...
A Function to Calculate Based on Laplace Approximation
MCMC Sampling for Linear Regression Model of multiple historical data ...
MCMC Sampling for Linear Regression Model of multiple historical data ...
MCMC Sampling for Normal Linear Model using Normalized Power Prior
MCMC Sampling for Linear Regression Model of multiple historical data ...
MCMC Sampling for Linear Regression Model of multiple historical data ...
A Function to Interpolate Based on Its Values on Select...
A Function to Calculate on Selected Knots
A Function to Calculate Log-likelihood of the Historical Data, Given M...
A Function to Calculate Log-likelihood of the Historical Data, Given A...
Calculate Posterior Mode of the Power Parameter in Normalized Power Pr...
Calculate Posterior Mode of the Power Parameter in Normalized Power Pr...
Calculate Posterior Mode of the Power Parameter in Normalized Power Pr...
Calculate Posterior Mode of the Power Parameter in Normalized Power Pr...
Calculate Posterior Mode of the Power Parameter in Normalized Power Pr...
MCMC Sampling for Multinomial Population using Normalized Power Prior
MCMC Sampling for Normal Population using Normalized Power Prior
Posterior sampling in several commonly used distributions using normalized power prior as described in Duan, Ye and Smith (2006) <doi:10.1002/env.752> and Ibrahim et.al. (2015) <doi:10.1002/sim.6728>. Sampling of the power parameter is achieved via either independence Metropolis-Hastings or random walk Metropolis-Hastings based on transformation.