Description Usage Arguments Value Examples

Markov chain Monte Carlo (Gibbs) method to sample from the conditional distributions of gamma and tau

1 2 |

`s` |
Observation locations |

`dpc_grid` |
Discrete Process Convolution grid |

`y` |
Observation values |

`nburn` |
Number of EM iterations (burn-in) use dto estimate kernel parameters (rho) |

`nsample` |
Number of MCMC iterations |

`priors` |
Priors for gamma and tau |

`printEvery` |
Console notifications every X iterations |

`seed` |
Seed for reproducible chains |

List with sampled values of gamma and tau, as well as optimal estimates for rho

1 2 3 4 5 6 7 | ```
s = data.frame(lat = rep(0, 3), lon = c(-1:1))
y = c(1,1,0)
dpc_grid = get_grid(c(-1,1), c(0,0), spacing = 2)
priors = get_priors(dpc_grid)
iso_kernel_matrix = get_kernel_matrix(s, dpc_grid)
fit = get_mcmc(s, dpc_grid, y, 10, 1000, priors, 100, 1)
cbind(get_estimates(s, dpc_grid, fit), obs = y)
``` |

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