Questions tagged [probabilistic-programming]

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Hamiltonian Monte Carlo vs. Sequential Monte Carlo

I am trying to get a feel for the relative merits and drawbacks, as well as different application domains of these two MCMC schemes. When would you use which and why? When might one fail but the ...
636 views

Hierarchical Bayesian modeling of incidence rates

Kevin Murphy's book discusses a classical Hierarchical Bayesian problem (originally discussed in Johnson and Albert, 1999, p24): Suppose that we are trying to ...
1k views

Probabilistic programming vs “traditional” ML

I was browsing the github repo for Pymc and found this notebook: Variational Inference: Bayesian Neural Networks The author extols the virtues of bayesian/probabilistic programming but then goes on ...
225 views

Are there any seemingly simple probability question that are actually intractable?

Are there any good examples of a seemingly simple probability problem, which is actually intractable? I am trying to motivate the use of simulation, and would like to come with an example of when it ...
221 views

Why is MCMC not reliable when compared to stochastic gradient descent?

I came across the following quote on enter link description here if we made MCMC as reliable as stochastic gradient descent now is for deep networks, that could mean a resurgence of more explicit ...
408 views

Comparing Factorie and Figaro languages for Statistical Relational Learning

I am looking to implement statistical relational learning, preferably in a modern programming language, and came across Factorie and Figaro for Scala. But most resources online that compare these are ...
2k views

PyMC3 Implementation of Probabilistic Matrix Factorization (PMF): MAP produces all 0s

I've started working with pymc3 over the past few days, and after getting a feel for the basics, I've tried implementing the Probabilistic Matrix Factorization model. For validation, I use a subset ...
823 views

289 views

What is Probabilistic Programming Used for?

I am hearing a lot about probabilistic programming. Turns out its just a way to specify probabilistic graphical models. Like Tensorflow is to neural networks. So, why use it? Do you know of any ...
372 views

I tried replicating the stochastic vol example in the pymc3 documentation, but using a larger dataset. NUTS was taking too long, so I tried ADVI. ...
69 views

Unique game problem (ML, DP, PP etc) [closed]

Looking for a solution to my below game problem. I believe it to be some sort of dynamic programming, machine learning, or probabilistic programming challenge, but am unsure... This is my original ...
24 views

applied papers on probabilistic generative models and inference engines

I am looking for applications papers where people choose some task on which they will do Bayesian inferencing and graphical modeling, and then build an inference engine to infer latent parameters. And ...
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How to validate your loss function when it is not a simple regression or classification?

Assuming I have loss function f(y_pred,y_target) that I will use to train my neural network. In this case the loss function is a regression, and let's say it should ...
99 views

Examples of applications of Markov random fields to data with a small number of variables

I am learning about some of the common applications of Markov random fields (a.k.a. undirected graphical models) to data science. A common feature of many applications I have read about is that the ...
123 views

multi-stage stochastic programming interpretation

In multi-stage stochastic programming a two-stage approach is sequentially repeated. In general a two-stage model contains first-stage decisions (e.g. production quantity) and second-stage decisions ...
11 views

Time series - probabilistic graph model

I am very new to Graph models, this is my first attempt. I have good knowledge of time series analysis. I am looking to build a graph model for a set of time series data - daily stock prices for say ...
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What is an Intuitive explanation of Gibbs sampling and what is an intuitive explanation of Dirichlet prior? [closed]

I am working on a Topic modelling project. I would like to understand the Intuition behind Gibbs sampling and Dirichlet prior. Any explanation is appreciated.
101 views

MAP of Gaussian Process Classification in Tensorflow Probability

I'm attempting to implement Gaussian Process Classification learning in tensorflow-probability, but my estimator turns out to be very biased toward zero. As opposed ...
26 views

finding the 'best' set

I came across this simple looking but puzzling question recently. There is a set of N tuples given [(a1,b1), ..., (aN, bN)], where a are real numbers and b are positive real numbers. We need to choose ...
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What is the connection between Bayesian Networks and the models done in Probabilistic Programming?

For what I’ve read about them, they are very similar. Both model probabilistic models in which the nodes are random variables and the edges are dependencies among them. However, the literature I’ve ...
26 views

Unbiasedness of confounding variable models

This is a question based on work in this paper: https://dl4physicalsciences.github.io/files/nips_dlps_2017_14.pdf I am interested in causality and representations using probabilistic graphical ...