Use PyMC3 like emcee

use_pymc3_like_emcee.py

##########################################
#emcee example:
##########################################
import numpy as np
import emcee

# Number of samples for each chain.
N_samp = 1000

def log_prob(x):
    if x < 0.1 or x > 100:
        return -np.inf
    
    return -2.35 * np.log(x)

ndim, nwalkers = 1, 4
ivar = 1. / np.random.rand(ndim)
p0 = np.random.rand(nwalkers, ndim) + 0.2

sampler = emcee.EnsembleSampler(nwalkers, ndim, log_prob)
state = sampler.run_mcmc(p0, 1000)
sampler.reset()
sampler.run_mcmc(state, N_samp)
mc_output = sampler.flatchain[:, 0]



##########################################
#PyMC3 example:
##########################################
import numpy as np
import pymc3 as pm
# Writing our likelihood using tt.log(), etc., allows theano to compute derivatives for HMC
import theano.tensor as tt

# Number of samples for each chain. #chains defaults to #cores.
N_samp = 1000

with pm.Model() as model:
    #Bound our sampled parameter between 0.1 and 100
    baseM = pm.Uniform('baseM', lower=0.1, upper=100)
    #This is p(M):
    L = pm.DensityDist(
        'like',                        ############################################
        lambda _M: -2.35*tt.log(_M),   #    Declare your log-likelihood here!     #
        observed={'_M': baseM}         ############################################
    )
    
    start = model.test_point
    h = pm.find_hessian(start)
    step = pm.HamiltonianMC(model.vars, h)
    trace = pm.sample(N_samp, tune=1000, init=None, step=step)
    
mc_output = trace.get_values(baseM)
#mc_output will have your posterior samples just like sampler.flatchain in emcee