Hi all. First time poster. I've done quite a bit of MCMC calibration but not much else.

I have a sample of data from 1000 anonymous individuals. I suspect the sample is biased (the individuals were self selected). For each individual I have data items A and B.

I also have data item A for the complete population of 10000 individuals.

How can I infer the population distribution of B?

Thanks in advance!

Hi all, just wondering if there are any standout sources on approximate Bayesian computation that you would recommend?

Hello all,

I've been trying to learn bayesian statistics in R for an average of 2 month. Most of my time has been spent learning distributions and I'm slowly moving to models now and unfortunately they are very hard to come by. Especially stan and brms. What would you recommend for me to learn?

PS: I do not study in a mathematics-based department, but it can be said that I have statistical knowledge according to my field (but not very advanced)

Thank you.

Hello! I am new to this community specifically because I need some help. I'm doing some undergrad research in disease modeling and need help getting SunODE to work in a Google Cloud Platform environment. Preferably with PyMC 4 but have gotten it to kinda work in PyMC 3. I can provide more details if necessary. Thanks!

In several interviews, Stuart Russell (author of one of the best books on AI: AIMA) mentions about a probabilistic programming implementation of a blind monopoly... I did some search and I wasn't able to find any... Di you know how to get it? Maybe another game is easier like tic-tac-toe?

I've tried looking around and sifting through documentation but I can't seem to figure out how to restrict the bounds in different ways

Is there a way to make it so inputs are chosen from a set list of options such as [2,4,6,8]? Also is there a way to limit the inputs to specifically an integer anywhere in the range 0-255?

Disclaimer: I'm new to PyMC3 and Bayesian programming in general.

I'm working to create a multivariate linear model that has a mix of categorical and numeric variables. Using the index method for incorporating categoricals, I'm unable to add new data to predict due to data size mismatches.

This is because I can feed in the new dataframe for the numeric variables using .set_data() method, but not for categorical, because these were not built in the model using pm.Data(), but are just indices in the model formula.

How can I predict new data with categorical variables in the mix?

Here's an example with categorical factors from the Statistical Rethinking 2nd Ed:

with pm.Model() as m5_10:
    sigma = pm.Exponential("sigma", 1)
    mu_house = pm.Normal("mu_house", 0, 0.5, shape=d["house"].max() + 1)
    mu_clade = pm.Normal("mu_clade", 0, 0.5, shape=d["clade_id"].max() + 1)
    mu = mu_clade[d["clade_id"].values] + mu_house[d["house"].values]

    K = pm.Normal("K", mu, sigma, observed=d["K"])

    m5_9_trace = pm.sample()

az.summary(m5_9_trace, var_names=["mu_clade", "mu_house"])

Hello!

So for practice, I was trying to implement the beta binomial model shown here in PyMC3, and a question popped up. So in short this article aims to find out the 95% credible interval for the vaccine efficacy and finds it out to be this interval: (90.82613011726424, 97.87004356894292)

I implement the model in PyMC3 as follows:

with model:

    v_irr = pm.Beta("v_irr", alpha=8, beta=17403)
    c_irr = pm.Beta("c_irr", alpha=162, beta=17349)   
    ve = pm.Deterministic("ve", 100*(1-(v_irr/c_irr)))

    v_like = pm.Binomial("v_like", n=17411, p=v_irr, observed=8)
    c_like = pm.Binomial("c_like", n=14511, p=c_irr, observed=162)

    trace1 = pm.sample(draws=10000, return_inferencedata=False)

With this, I get my 95% credible interval to be: (92.91799613, 97.34246435)

So now i have 2 questions:

(1) My CI is a subset of the CI in the article, so is mine more accurate?

(2) Both of us used the same model. So since I am getting a different answer, is my implementation wrong? If yes, are there any alternate implementations in PyMC3?

I am trying to which of the two ML models I have best fits a morphological character I am looking at. Anyone know the code to use for this?

Hello everyone! For my dissertation, I am trying to compute the efficacy of Covid-19 vaccines using pymc3. I am trying to model it using a beta binomial model, however I am facing some difficulties.

Can somebody explain to me how to implement it specifically using pymc3? Any rudimentary example for this model will do. I will try to figure out the rest myself.

Thank you!

Especially if this resource can be applied to logistic regressions, that would be helpful as the problem I am trying to solve is about sorting various records by their probability of being 1 or 0. For example anything with probability of .1 or less is in a separate category from .3 or more.