leapfrog {hmclearn}

Leapfrog Algorithm for Hamiltonian Monte Carlo

Description

Runs a single iteration of the leapfrog algorithm. Typically called directly from hmc

Usage

leapfrog(
  theta_lf,
  r,
  epsilon,
  glogPOSTERIOR,
  Minv,
  constrain,
  lastSTEP = FALSE,
  ...
)

Arguments

theta_lf

starting parameter vector

r

starting momentum vector

epsilon

Step-size parameter for leapfrog

glogPOSTERIOR

Function to calculate and return the gradient of the log posterior given a vector of values of theta

Minv

Inverse Mass matrix

constrain

Optional vector of which parameters in theta accept positive values only. Default is that all parameters accept all real numbers

lastSTEP

Boolean indicating whether to calculate the last half-step of the momentum update

...

Additional parameters passed to glogPOSTERIOR

Value

List containing two elements: theta.new the ending value of theta and r.new the ending value of the momentum

References

Neal, Radford. 2011. MCMC Using Hamiltonian Dynamics. In Handbook of Markov Chain Monte Carlo, edited by Steve Brooks, Andrew Gelman, Galin L. Jones, and Xiao-Li Meng, 116–62. Chapman; Hall/CRC.

Examples

set.seed(321)
X <- cbind(1, rnorm(10))
y <- rnorm(10)
p <- runif(3) - 0.5
leapfrog(rep(0,3), p, 0.01, g_linear_posterior,
         diag(3), FALSE, X=X, y=y)