# Metropolis : Set first sample value instead of randomly generate an arbitary value

According to Metropolis-Hasting algorithm, the first sample is an arbitrary value generated randomly at the Initialization step. ( http://en.wikipedia.org/wiki/Metropolis%E2%80%93Hastings_algorithm ) If we know the range of values that is closer to the parameters to-be-estimated is known, this information can be used to reduce the number of iterations.

Thus, why not set the first sample at the Initialization step to the value that are within the range ? How to provide the first sample value to the pymc.MCMC() object instead of allowing pymc to randomly generate an arbitrary first sample value ?

I'm currently using pymc2.2 and failed to install pymc3 to my Windows platform.

• In PyMC2, you can use the value parameter of the pm.Stochastic class to set initial values, for example, X = pm.Normal('X', 0, 1, value=.1). – Abraham D Flaxman May 28 '15 at 23:23