Simple Prior Functions

Functions for generating prior functions for use with mcmc, etc.

make.prior.exponential(r)
make.prior.uniform(lower, upper, log=TRUE)

Arguments

r

Scalar or vector of rate parameters

lower

Lower bound of the parameter

upper

Upper bound of the parameter

log

Logical: should the prior be on a log basis?

Details

The exponential prior probability distribution has probability density $$\sum_i r_i e^{-r_i x_i}$$ where the \(i\) denotes the \(i\)th parameter. If r is a scalar, then the same rate is used for all parameters.

These functions each return a function that may be used as the prior argument to mcmc().

Author

Richard G. FitzJohn