my goal : I’d like to use a custom distribution as a mixture component. This is my ‘basic’ code. It works ok to build a log-normal mixture model that fit (more or less) my data : l_nbr = 3 # number of components l_mu = pm.Halfnormal('l_mu'),sd=1,shape=l_nbr) l_sd = m.HalfNormal('l_sd_1',mu=l_sd_p…

I dont think that’s possible as the components in a mixture distribution must be a density function. You will have to write a lognormal_plus_offset logp function in this case.

ok, as a first try, I will only consider to write my ‘own’ logp function for the log-normal mixture (I’ll try to add the offset when this step will work) : d = pm.Lognormal.dist(mu=l_mu,sd=l_sd,shape=l_nbr) # only used to access the log-normal logp function l_comp = pm.DensityDist('l_comp',d.logp,s…

I think the issue is that pm.DensityDist needs an observed value. Look at this discussion: [image] How to set up a custom likelihood function for two variables Questions In my case, likelihood function was defined as f(theta_1, theta_2), the expression is already known not…

DensityDist works also without observed https://github.com/pymc-devs/pymc3/blob/master/pymc3/examples/custom_dists.py What I meant in the other post was if the model is not evaluated on some observed it will just sample from the prior.

Thanks every one for your remarks. You helped me build my own distribution with the ‘DensityDist’ ! So now I am able to build log-normal distribution with an offset. I am very happy about that, but I still have a problem obtaining the PPC (Predictive Posterior Check) This code provides logp and r…

In the random method definition, you are adding a tensor to which gives the error. Maybe try: def my_rand(dist, point, size=None): def foo(point, size=None): offset_val = point['offset'] r = dist.random(point=point, size=size) return r+offset_val return foo

Thanks this helped me solving the problem ! But now I must confess that I doubt about ‘my_logp’ function (in previous message)… Is it ok ? How can I check it ?

ok, I now I understand how to build a ‘DensityDist’ to build one distribution (à log-normal) with an offset… This are logp & random functions : def my_logp(dist,offset): def foo(x): return dist.logp(x-offset) return foo def my_rand(dist,size=None): def foo(point,size=None): …

How to use a Deterministic in a mixture?

Questions

narendramukherjee June 11, 2018, 3:09am 4

I think the issue is that pm.DensityDist needs an observed value. Look at this discussion:

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How to use a Deterministic in a mixture?

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