The most basic possible Metropolis-Hastings style MCMC sampler
:param cov:
The covariance matrix to use for the proposal distribution.
:param dim:
Number of dimensions in the parameter space.
:param lnpostfn:
A function that takes a vector in the parameter space as input and
returns the natural logarithm of the posterior probability for that
position.
:param args: (optional)
A list of extra arguments for ``lnpostfn``. ``lnpostfn`` will be
called with the sequence ``lnpostfn(p, *args)``.
Definition at line 19 of file mh.py.
| def emcee.mh.MHSampler.sample |
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self, |
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p0, |
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lnprob = None, |
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randomstate = None, |
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thin = 1, |
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storechain = True, |
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iterations = 1 |
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Advances the chain ``iterations`` steps as an iterator
:param p0:
The initial position vector.
:param lnprob0: (optional)
The log posterior probability at position ``p0``. If ``lnprob``
is not provided, the initial value is calculated.
:param rstate0: (optional)
The state of the random number generator. See the
:func:`random_state` property for details.
:param iterations: (optional)
The number of steps to run.
:param thin: (optional)
If you only want to store and yield every ``thin`` samples in the
chain, set thin to an integer greater than 1.
:param storechain: (optional)
By default, the sampler stores (in memory) the positions and
log-probabilities of the samples in the chain. If you are
using another method to store the samples to a file or if you
don't need to analyse the samples after the fact (for burn-in
for example) set ``storechain`` to ``False``.
At each iteration, this generator yields:
* ``pos`` — The current positions of the chain in the parameter
space.
* ``lnprob`` — The value of the log posterior at ``pos`` .
* ``rstate`` — The current state of the random number generator.
Definition at line 49 of file mh.py.
1.8.1.2