When reading and watching video regarding building and using Bayes Nets, the examples typically use binary outcomes for the nodes.

'Probability of it raining', 'Probability of x disease', ect...

With those types of nodes it is easy to derive the conditional probability table. However, what do you do when you have continuous variables with many conditional variables or a more complicated binary model?

Would you use standard regression techniques like OLS and Logistic regression to estimated the nodes of a Bayes Net?

Or what other methods might one use?

  • $\begingroup$ I would clarify what you mean by "would you use [techniques] to estimated the nodes of a Bayes Net?" Do you want to learn the structure of a bayes network given continuous variable outcomes? $\endgroup$
    – Vimal
    Commented Jul 13, 2015 at 15:40
  • $\begingroup$ I suppose I don't know the answer to that question. I know that I am clearly missing something. I want to know how to estimate the probability of an outcome given multiple variables affecting it. I prefer the continuous case. I guess my first thought was the techniques mentioned in my question, but I believe this is a naive thought. $\endgroup$
    – Michael
    Commented Jul 13, 2015 at 16:17
  • $\begingroup$ To clarify, you would like to know how to find the probability distribution for a node given its parents connections, correct? It sounds like your first step will be choosing the form of the probability distribution; maybe a multivariate Gaussian or some other multivariate distribution. Then, you could use a Bayesian approach to find the most likely parameters for that distribution given observed data and the observed data for the parent nodes. $\endgroup$ Commented Jul 13, 2015 at 18:27

1 Answer 1


Would you use standard regression techniques like OLS and Logistic regression to estimated the nodes of a Bayes Net?

If I understand your question correctly, you would like to model $P({\rm outcome} | {\rm features})$, where features are random variables in the continuous domain and outcome is probability of 0 or 1. Assume you are given training samples $(x_i, y_i)$, where $x_i$ are your features (real valued) and $y_i$ are the outcomes (binary valued, 0 or 1).

You can model $P(Y|X)$ using a family of approaches. Logistic Regression (a single-layer neural network) basically models $P(Y|X) = sigmoid(A \cdot X + b)$, where $A, b$ are your parameters (feature weights and bias, respectively). That is, it assumes that the log-odds ($\log(p/(1-p))$) is a linear function of your features. You can read up more on https://en.wikipedia.org/wiki/Logistic_regression.

If you train the above function with a specific loss function (the cross-entropy loss, which you can read on the Wiki page), the minimiser will approximate the true posterior probability $P(Y|X)$.


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