So many chains for too little iterations.
Check Bob suggestion too.


2014/1/18 Bob O'Hara <bohara@senckenberg.de>

> On 01/18/2014 04:39 AM, 明 wrote:
>
>> Dear all
>>       I have a model with four paramters, I want to estimate the
>> parameter uncertainty, so Bayesian analysis with MCMC method is applied.
>>       But every sigle mcmc chain seems give quite different parameter
>> marginal distributions.
>>       In order to get the true parameter marginal distributions, I do
>> like this:
>>       (1) I take 100 MCMC chain, and each chain has 10000 iterations.
>>       (2) caculate the different parameter marginal distributions
>> according to the frequence of paramter in step 1 sampling.
>>       The result seems reasonable. but is it right?
>>       Looking forward for your reply, Thanks in advance
>>    Han Ming
>>         [[alternative HTML version deleted]]
>>
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>>
> Have you checked for convergence? If the chains are giving different
> marginal distributions, this suggests they haven't converged, so they are
> not sampling from the correct posterior distribution (or, at least, some
> chains aren't).
>
> Bob
>
> --
> Bob O'Hara
>
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