Problem model definition PyMC3

Questions
6

In pm.DensityDist, observed are the parameters and data you pass to the custom log-likelihood function.

If you want to make it more clear, you can also rewrite the logp function like this:

# Log likelihood function
def logp_function(parameters):
    def logp(observed):
        some_scaler = some_function(parameters, observed)
        return some_scaler

    return logp # returns a function

In your case, it would be:

def likelihoodMCMC(m, q, sigmay):
    def logp(value):
        return tt.exp(0.5*tt.sum(
            tt.log(1/(2*np.pi*(sigmay**2 + value[3]**2 + (m*value[2])**2))) -
            (value[1] - m*value[0] - q)**2/(sigmay**2 + value[3]**2 + (m*value[2])**2)))
    return logp

where value = (logEiso, logEpeak_i, logErrEiso, logErrEpeak_i)

Then you can call it as:

with pm.Model() as model:
    ...
    obs_peaks = pm.DensityDist('obs_peak', likelihoodMCMC(m,q,sigmay), 
               observed=(logEiso, logEpeak_i, logErrEiso, logErrEpeak_i))
1 Like