[SciPy-User] random variable for truncated multivariate normal and t distributions

[Sturla Molden]

Den 02.06.2011 18:54, skrev josef.pktd at gmail.com:
>
> If these are the alternatives, then I will stick with rejection sampling.
> I'm not starting to learn the implementation details of simulating
> with MCMC, Metropolis-Hastings or Gibbs, and leave it to the pymc
> developers and to Wes.

Metropolis-Hastings is a form of rejection sampling.

It's just a way to reduce the number of rejections, particularly when the sample space is large.




> rtmvnorm has a big Warning label about the Gibbs sampler, although,
> for MonteCarlo integration, any serial correlation in the sampler
> won't be very relevant.

You will get serial correlation with MCMC, but remember they are still 
samples from the stationary distribution of the Markov chain. You can 
still use these samples to compute mean, standard deviation, KDE, 
numerical integrals, etc.


Sturla