Description Usage Arguments Value References Examples

A Markov chain Monte Carlo (MCMC) sampler for location-scale regression
models from the `lmls()`

function. The sampler uses Gibbs updates for the
location coefficients and the Riemann manifold Metropolis-adjusted Langevin
algorithm (MMALA) from Girolami and Calderhead (2011) with the Fisher-Rao
metric tensor for the scale coefficients. The priors for the regression
coefficients are assumed to be flat.

To find the optimal step size for the MMALA updates, the dual averaging algorithm from Nesterov (2009) is used during a warm-up phase.

1 | ```
mcmc(m, num_samples = 1000, num_warmup = 1000, target_accept = 0.8)
``` |

`m` |
A location-scale regression model from the |

`num_samples` |
The number of MCMC samples after the warm-up. Defaults to 1000. |

`num_warmup` |
The number of MCMC samples for the warm-up. Defaults to 1000. |

`target_accept` |
The target acceptance rate for the dual averaging algorithm used for the warm-up. Defaults to 0.8. |

An `lmls`

S3 object, see `lmls()`

. The entry `mcmc`

with the matrices
of MCMC samples is added to the object as a list with the names `location`

and `scale`

.

Girolami, M. and Calderhead, B. (2011), Riemann manifold Langevin and Hamiltonian Monte Carlo methods. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 73: 123-214. doi: 10.1111/j.1467-9868.2010.00765.x

Nesterov, Y. (2009), Primal-dual subgradient methods for convex problems. Mathematical Programming, 120: 221–259. doi: 10.1007/s10107-007-0149-x

1 2 3 4 5 |

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.