# Questions tagged [probabilistic-programming]

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### Plotting a probability density based on the ratio of two random variables in PyMC3

I am trying to graphically represent the ratio of two probability distributions: $$f(x) = \frac{1}{\sqrt{2\pi}} \exp{(\frac{(-x)^2}{2})}$$ $$g(x) = \frac{1}{2} \exp{(-|x|)}$$ I'm a bit confused about ...
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### Statistical relationship between the stages of a stochastic optimization problem

What exactly do the "stages" of a stochastic program say about the statistical relationship between the problem variables? From what I understand, the stages imply both an "ordering" and "grouping" ...
28 views

### REINFORCE for VAE Implementation Question

I want to compute the VAE loss through REINFORCE since my model's decoder is a deterministic program and is non-differentiable. The only REINFORCE implementation for VAE I was able to find used the ...
7 views

### Adaptive MCMC when variables exist only conditionally?

I'm looking at models that make the existence of one variable depend on another variable. For example, n ~ geometric(0.5) x ~ iid(n,normal(0,1)) Here, ...
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### Probabilistic Program based grammar

I want to use the method proposed in the Generative Grading: Neural Approximate Parsing for Automated Student Feedback . Based on my understanding the first step for using this method is writing a PPG ...
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### Program analysis of a coin toss simulator

Here's a problem I'm trying to solve (it's a problem from the CS109 course, assignment 2): Suppose we want to write an algorithm fairRandom for randomly ...
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### 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 ...
865 views

### Variance of evidence lower bound(ELBO) loss function

When using Bayesian optimisation in a neural network our loss function is equal to: Here the first term is the KL divergence between the approximate and true posteriors. The second term is the ...
257 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 ...
73 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 ...
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### How to get quantiles/probabilities of time series forecasts?

my problem is as follows : I am creating demand forecasts for some goods with different methods (ARIMA, ETS,..) The issue is that I would like to forecast the probabilities of those forecasts since ...
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### 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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### What property must a RNG have to be used in Monte Carlo simulations?

For example, every cryptographically secure pseudo-random number generator (CSPRNG) is required to satisfy the next-bit test and withstand "state compromise extensions". This makes me wonder what ...
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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 ...
186 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 ...
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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 ...
335 views

### Plate notation for a hierarchical regression model (bayesian)

I've been recently studying hierarchical bayesian regressio (with pymc3), and I was wondering, how does the following example: http://twiecki.github.io/blog/2014/03/17/bayesian-glms-3/ look like ...
2k 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 ...
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### Predicting a Twitter user from individual tweets probabilities

Let's say I have three tweets and those three tweets are all from either Mary or John. There is no possibility for mixed result. ...
27 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 ...
314 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 ...
260 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 ...
300 views

### How Deep Probabilistic (Graphical) Models differs from the usual Probabilistic Graphical Models?

How Deep Probabilistic (Graphical) Models differs from the usual Probabilistic Graphical Models? What are the main applications and state of art results of Deep Probabilistic Models?
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### “Blocking” effects in probabilistic programs

I'm trying to estimate a regression where: I can only see the sex of a subset of the population I do know the total population size I'd like to know how many events are driven my men vs women, using ...
230 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 ...
142 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 ...
2k views

### 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 ...
415 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. ...
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### What are some methods for learning from data in probabilistic graphical models especially Bayesian Networks?

While most books and papers on Probability Graphical Models (PGMs) describe a nice representational method for Bayesian Networks BNs (and/or Dynamic Bayesian Networks DBNs), where a Joint Probability ...
504 views

### Is probabilistic modeling the same thing as Bayesian modeling?

When titles, books, and posts refer to probabilistic modeling, coupled with it I usually see the word "Bayesian" near by and all around. If we were to think of this as a ven diagram, are they the same ...
674 views

### Sample space and outcome of birthday problem

Suppose, calculating the probability of having at least two peoples same birthday from 25 people. What is the sample space and outcome of the experiment? As far as I pondered, S = 365^25 and outcome ...
52 views

### Bayesian/Probabilistic Programming with PyMC: Am I doing this right?

I've been playing around with Bayesian / Probabilistic Programming with PyMC and others. I can't find a ton of great practical examples on the web so I created my own problem and tried to solve it. ...
266 views

### Is it possible to model using Bayesian Network (probabilistic graphical model) if you have no prior knowledge of the data?

Is it possible to model using Bayesian Network (probabilistic graphical model) if you have no idea at all of the interaction of the variables in the data? From my reading, I find that Bayesian network ...
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### Posterior autocorrelation in Pymc. How to interpret it?

I started learning Bayesian inference by reading "Probabilistic Programming and Bayesian Methods for Hackers". I found something that is not really clear for me in the third chapter. Lets look at the ...
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### Need help on how to interpret Gamma Probability density function of gamma(2, 1/10) [duplicate]

The below sample graph was created using R, for an example I came across while reading (on Distribution of time until customer x arrives): ...
1k views

### Why does my posterior distribution have probability values greater than 1? [duplicate]

I'm attempting to learn Bayesian modelling with PyMC, so I have been going through Cam Pilon Davidson's Probabilistic Programming for Hackers. I literally copied his code from chapter 1 and used my ...