Learn About Our Meetup

5000+ Members



Join our meetup, learn, connect, share, and get to know your Toronto AI community. 



Browse through the latest deep learning, ai, machine learning postings from Indeed for the GTA.



Are you looking to sponsor space, be a speaker, or volunteer, feel free to give us a shout.

[Discussion] How to estimate conditional probability (cdf) of multivariate dataset?


I am sharing the problem I face in Matlab but if you have a solution for this problem even in Python then I would very very happy.

I was able to estimate conditional probability (CDF) for a dataset that has two features (X_1 and Y) i.e., P(X_1|Y) using a Matlab function called “quantilePredict”. It works great. However, when I consider three features X_1, X_2 and Y. Then how can I find the P(X_1,X_2|Y) without the assumption of conditional independence?

How to capture the covariance as well as the CDF while considering quantiles but not mean of the data? Worst case I am fine with how to capture the covariance as well as CDF with mean of the data?

TreeBagger is trained (f) by giving “Y” as input and X_1 as output i.e., X_1 = f(Y). We then use the Treebagger model to predict responses for “quantilePredict” but in multivariate case, the Treebagger cannot fit the data where the input is “Y” and output has “X_1, X_2” i.e., Y = f(X_1,X_2) (this idea/pov is probably wrong and naive) ?.

submitted by /u/askquestion001
[link] [comments]

Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.