R/RcppExports.R
nd_nhpp_fixed_fit.RdEstimate the nonhomgogenous poisson process intensity function from grouped data with fixed concentration parameters
nd_nhpp_fixed_fit( r, n_j, d, L, K, J, mu_0, kappa_0, nu_0, sigma_0, alpha, rho, iter_max, warm_up, thin, seed, chain, num_posterior_samples )
| r | vector of distances associatd with different BEFs |
|---|---|
| n_j | matrix of integers denoting the start and length of each observations associated BEF distances |
| d | a 1D grid of positive real values over which the differing intensities are evaluated |
| L | component truncation number |
| K | intensity cluster truncation number |
| J | number of rows in r matrix; number of groups |
| mu_0 | normal base measure prior mean |
| kappa_0 | normal base measure prior variance scale |
| nu_0 | inverse chi sqaure base measure prior degrees of freedom |
| sigma_0 | inverse chi square base measure prior scale |
| alpha | upper level concentration parameter (fixed) |
| rho | lower level concentration parameter (fixed) |
| iter_max | total number of iterations for which to run sampler |
| warm_up | number of iterations for which to burn-in or "warm-up" sampler |
| thin | number of iterations to thin by |
| seed | integer with which to initialize random number generator |
| chain | integer chain label |
| num_posterior_samples | the total number of posterior samples after burn in |
Gelman, A., Carlin J., Stern H. and Rubin D. (2004). Bayesian Data Analysis. Cambridge University Press, Chapman & Hall/CRC.
the normal-inverse Chi-square conjugate parameterization in the reference