I recently asked a question about how to condition a normally distributed variable on bernoulli parents. After looking at the code, and after some discussion, I understood what was going on. In my setting, the dependent variable either takes the form of a particular distribution given the parents, …

Hey, I think I’ve made a lot of progress on my problem. Do you have any examples, you can point me to about inferring the mixture weights? Do you mean doing something like this: #Mixture weights pOns = pymc3.Dirichlet('pOns', numpy.array(on_alphas)) on = pymc3.Categorical('on' pOns) But for the …

You should have a look at marginalized mixture model - whenever you have discrete variables in your model, you should first try to find way to marginalized it :slight_smile: Frequently Asked Questions is a good place to start.

Ok, I will look into this! Thank you

@junpenglao , I came up with this. Please let me know what you think. tri_name_giv_tri_on_dist = numpy.array([.4, .4, .2]) tri_name_giv_tri_not_on_dist = numpy.array([.2,.3, .5]) block_name_giv_block_on_dist = numpy.array([.3, .3, .4]) block_name_giv_block_not_on_dist = numpy.array([.1, .3, .6]) N…

You are getting there! You should rewrite the following into a continuous variable, either use the parameters (i.e., pOn, pTri_given_not_on + on * tri_delta_on here) directly, or wrap it into a Beta distribution: on = pymc3.Bernoulli('on', pOn) triangle = pymc3.Bernoulli('triangle', pTri_give…

@junpenglao , So, you’re saying I should do something like this? # Internal x,y,z position variable to be transformed pos = pymc3.Normal('pos', 0., 1., shape=3) # Internal NA random variable NA = pymc3.Deterministic('NA', NA_ENCODING) # On pOn = pymc3.Beta('on', alpha=on_coun…

Nope - I mean try to avoid something like on = pymc3.Bernoulli('on', pOn) as it is an unobserved discrete variable.

So, I’m not sure what to do about marginalization. In my setting, I will want to infer the values of these hidden variables. If they’re marginalized out like that, I won’t be able to do this, right?

You can infer the continuous mixture weight instead - if you want explicit latent label you can sample from the mixture weight with a categorical, or applying argmax.

Condition Categorical Variable on Bernoulli Parents

Questions

dmenager May 4, 2019, 1:28am 8

Ah, Ok, I see… You are suggesting that I marginalize it out, right?

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Condition Categorical Variable on Bernoulli Parents

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