diagnostic

chain

dependence


The proposed burn-in is calculated by dividing the Markov chains into <code>m</code> blocks and calculate the 95% credibility intervals and the respective mean.
Starting in the first one, the block is taken as burn-in as long as the mean of the current block is not in the credibility interval of the following block or vice versa.
The thinning rate is proposed by the first lag which leads to a chain autocorrelation less than <code>dependence</code>.
It is not easy to automate these choices, so it is highly recommended to verify the chains manually.


Bayesian estimation and prediction for stochastic processes based
on the Euler approximation. Considered processes are: jump diffusion,
(mixed) diffusion models, hidden (mixed) diffusion models, non-homogeneous
Poisson processes (NHPP), (mixed) regression models for comparison and a
regression model including a NHPP.

diagnostic: Calculation of a proposal for burn-in phase and thinning rate

Description

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