Hi, I am working on a model update involving pymc. I have a function, say func, which an given array, produces a float or -inf. Func used to be wrapped within a pymc.stochastic decorator before it was deprecated. I understand that func serves to confirm whether a draw during sampling of a tensorvariable satisfies requirements so that only draws that do are retained in the posterior. Now, I am trying to use the pymc.DensityDist or pymc.Potential but because a TensorVariable (not an array) is passed to func, it is unable to perform inequality checks. I have used the eval method of the tensorvariable but that has also failed to work. Please help
@@stanleynwanekezie5355 The best place to ask is the pymc discourse. All the core devs are there and your question I believe has been asked already so there may already be a solution!
Dude your video is amazing. So my clarity and simplicity around complex topic. I really like how you keep re explaining basic terms as you cover them because it really helps following through into more advanced areas
Wow I really hope you dive into pymc3, I always had difficulty on understanding programming the priors. You're the best! This video is great. Sending love from South Korea :)
SUPER good video, I have been practicing on jupyter notebooks for data science and it has taken time but these videos make me feel like im standing on the shoulders of giants. THANKS
Very nice video, well-organized and neatly explained concepts. One thing I would like to see at the end is some discussion on how would you use the posterior distributions, given that the true values are not obvious to infer from those at all :)
Nicely done! Very clear, concise yet informative. One thing missing from intro tutorials like this one is a real world example, with a a bigger dataset and more variables. Can pymc3 run on gpus, or computer clusters, etc or should I look elsewhere (pyro, tensor flow.probability)? Just giving an idea for future videos! Keep up the good work
Great video, very well explained!! I would love to see you 💪 on another equation and distribution, something just a little harder like Ytrue = X1^3 - X2^2 - X3 Y = Ytrue + binomial error distribution Or something weird like that lol Thanks for another great lesson!
Confidence intervals don't actually give you the probability of a parameter being inside them. They instead tell you that, over repeated sampling, say 95% of the time the true value will be contained in those intervals. Bayesian approach gives you intervals with the probability of a parameter being inside them
Can somebody pls tell where did we use the observed values of y in determining the posterior distribution? Is it perhaps used in determining just the normalizing factor in the Bayes theorem...?
Great video. I'm just shocked about the runtime. 2 whole minutes. So, the mcmc needs to run a few thousand iterations, where for each iteration it should sample a few random numbers from standard distributions. I would have expected this to be done in milliseconds. What am I missing?
Could we not simply use p-value to gauge our confidence of our point estimate? Or is there additional benefit from being able to see the full distribution?
Hi ritvikmath, great videos, this one , the gibbs, metrolopis, the ridge/lasso!! Do you have any suggestions to understand how to best do a prediction in the bayesian way? And to get a credible set of the prediction, we have credible set for each of the parameters, if I use their means I can make predictions , if I were to use a bit lower than their mean I can produce a sightly different value , so there many possible prediction ranges for a given x input. I would appreciate any suggestions, thanks !!!! I could not find a good resource for that.
Hi, I am working on a model update involving pymc. I have a function, say func, which an given array, produces a float or -inf. Func used to be wrapped within a pymc.stochastic decorator before it was deprecated. I understand that func serves to confirm whether a draw during sampling of a tensorvariable satisfies requirements so that only draws that do are retained in the posterior. Now, I am trying to use the pymc.DensityDist or pymc.Potential but because a TensorVariable (not an array) is passed to func, it is unable to perform inequality checks. I have used the eval method of the tensorvariable but that has also failed to work. Please help.
