Questions tagged [probabilistic-programming]

26 questions with no upvoted or accepted answers
Filter by
Sorted by
Tagged with
6
votes
0answers
412 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 ...
5
votes
0answers
992 views

Probability distribution over classes as labels in classification task

Classical classification problem has next formulation. Given a set of $n$ attributes, a set of $k$ classes and a set of labelled training instances: $(i_i, l_j),...,(i_j, l_j)$, where $ i = (v_1, v_2,...
4
votes
0answers
74 views

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. ...
4
votes
2answers
50 views

Using Bayesian Graphical Models to reconstruct duplicated damaged data

I am a computer science student specialised in machine learning. Recently I fell in love with Probabilistic Graphical Models (and probabilistic programming) because of the flexibility to focus on ...
3
votes
0answers
543 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 ...
2
votes
1answer
449 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 ...
1
vote
0answers
12 views

If given a table of samples from a multivariate distribution, can you get multivariate marginals by just extracting the columns you are interested in?

If we let $p(x)$ be the joint probability density where of r.v. $x$ where $x \in \mathbb{R}^{10}$ I am interested in looking at the joint marginal $p_{x_3, x_4, x_5}(x_3, x_4, x_5)$ I know that ...
1
vote
0answers
2k 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 ...
1
vote
0answers
92 views

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 ...
1
vote
0answers
32 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 ...
1
vote
0answers
303 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?
1
vote
0answers
27 views

“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 ...
1
vote
0answers
53 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. ...
1
vote
1answer
57 views

Switchpoint test with many customers

Im just reading the wunderful book Probabilistic Programming and Bayesian Methods for Hackers. In the very first chapter they inspect whether the habits of one single customer change over time, for ...
1
vote
0answers
57 views

What data generating process can't be expressed as a probabilistic graphical model?

I've been reading a bit on probabilistic programming, and one of the main claims is that it is more expressive than graphical models. As the representational capacity of PPLs is anything that can be ...
1
vote
0answers
157 views

How to calculate the posterior probabilty of Gaussian Mixture Component

If the mean vector and the Covariance matrix of a Gaussian Mixture model are known, how could I calculate the posterior probability of each of the Gaussian Component in the mixture.
1
vote
1answer
116 views

Need direction in regards to probability based random value generation

I have a daily stream discharge data set spanning 10 years whose histogram looks like this: I would like to use it to set the distribution for a randomly chosen discharge for a given timestep. That ...
1
vote
0answers
179 views

Bound on the expectancy of the maximum level in skip list

Let $M$ be a random variable for the maximum level of skip list, $M$ is a positive integer, $k$ is an integer from 0 to $\infty$, and $$ \Pr(M>k) = 1 - (1-p^k)^n \leq np^k $$ In the article Skip ...
0
votes
0answers
14 views

Efficient Calculation of Moments from arbitrary continuous probability distribution

I want to numerically calculate moments/centralized moments from a continuous probability distribution. The continuous probability distribution is arbitrary which is characterized by a function f(p). ...
0
votes
0answers
18 views

What is the benefit of adding control flow to probabilistic programming?

I was watching an interesting video on the Pyro package in Pytorch for probabilistic programming. One of the things that they ...
0
votes
0answers
8 views

Unsupervised learning over background knowledge

I have a question related to logic programming. Say you have a database of facts like $\{Tall(Jordan), Smokes(Jordan), Tall(Pat), ¬Smokes(Pat)\}$ I want a learning algorithm that will derive from ...
0
votes
0answers
4 views

What are the limmitations of reparametrization gradients for discrete random variables? (Gumbel-softmax)

We know that one approach for re-parametrizing gradients for variational inference is taking the Gumbel-softmax estimator proposed in [1] and [2]. In [3], that is a talk on SVI, D. Blei at around 29:...
0
votes
0answers
10 views

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 ...
0
votes
0answers
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, ...
0
votes
0answers
19 views

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 ...
0
votes
2answers
282 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 ...