Description Usage Arguments Details See Also Examples

Create, test or show objects of class "bayesparams".

1 2 3 4 5 6 7 8 | ```
bayesparams(prop.a = 0.02, prop.b = 0.02,
prior.mu = c(0, 10), prior.nu = c(2, 1/2), prior.eta = c(2, 2),
trunc = 100, comp.saved = 15, maxit = 30000,
burn = 5000, thin = 1,
adapt = 5000, batch.size = 125,
mode = 1)
is.bayesparams(x)
``` |

```
prop.a,
prop.b
``` |
standard deviation for the Gaussian proposal of the Heffernan–Tawn parameters. |

`prior.mu` |
mean and standard deviation of the Gaussian prior for the components' means. |

`prior.nu` |
shape and rate of the inverse gamma prior for the components' variances. |

`prior.eta` |
shape and scale of the gamma prior for the precision parameter of the Dirichlet process. |

`trunc` |
integer; value of the truncation for the approximation of the infinite sum in the stick-breaking representation. |

`comp.saved` |
number of first components to be saved and returned. |

`maxit` |
maximum number of iterations. |

`burn` |
number of first iterations to discard. |

`thin` |
positive integer; spacing between iterations to be saved. Default is 1, i.e., all iterations are saved. |

`adapt` |
integer; number of iterations during which an adaption algorithm is applied to the proposal variances of |

`batch.size` |
size of batches used in the adaption algorithm. It has no effect if |

`mode` |
verbosity; 0 for debug mode, 1 (default) for standard output, and 2 for silent. |

`x` |
an arbitrary |

`prop.a`

is a vector of length 5 with the standard deviations for each region of the RAMA for the (Gaussian) proposal for *α*. If a scalar is given, 5 identical values are assumed.

`prop.b`

is a vector of length 3 with the standard deviations for each region of the RAMA for the (Gaussian) proposal for *β*. If a scalar is provided, 3 identical values are assumed.

`comp.saved`

has no impact on the calculations: its only purpose is to prevent from storing huge amounts of empty components.

The regional adaption scheme targets a *0.44* acceptance probability. It splits *[-1;1]* in *5* regions for *α* and *[0;1]* in *3* regions for *β*. The decision to increase/decrease the proposal standard deviation is based on the past `batch.size`

MCMC iterations, so too low values yield inefficient adaption, while too large values yield slow adaption.

Default values for the hyperparameters are chosen to get reasonably uninformative priors.

1 2 3 | ```
is.bayesparams(bayesparams()) # TRUE
## use defaults, change max number of iteration of MCMC
par <- bayesparams(maxit=1e5)
``` |

tsxtreme documentation built on May 30, 2017, 3:32 a.m.

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