NormalNormalPosterior {bpp} | R Documentation |
Normal-Normal Posterior in conjugate normal model, for known sigma
Description
Compute the posterior distribution in a conjugate normal model for known variance: Let be a sample from a
distribution, with
assumed known. We assume a prior distribution on
, namely
. The posterior distribution is then
with
and
These formulas are available e.g. in Held (2014, p. 182).
Usage
NormalNormalPosterior(datamean, sigma, n, nu, tau)
Arguments
datamean |
Mean of the data. |
sigma |
(Known) standard deviation of the data. |
n |
Number of observations. |
nu |
Prior mean. |
tau |
Prior standard deviation. |
Value
A list with the entries:
postmean |
Posterior mean. |
postsigma |
Posterior standard deviation. |
Author(s)
Kaspar Rufibach (maintainer)
kaspar.rufibach@roche.com
References
Held, L., Sabanes-Bove, D. (2014). Applied Statistical Inference. Springer.
Examples
## data:
n <- 25
sd0 <- 3
x <- rnorm(n, mean = 2, sd = sd0)
## prior:
nu <- 0
tau <- 2
## posterior:
NormalNormalPosterior(datamean = mean(x), sigma = sd0,
n = n, nu = nu, tau = tau)
[Package bpp version 1.0.4 Index]