Questions tagged [probabilistic-programming]

Filter by
Sorted by
Tagged with
1
vote
1answer
86 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 ...
0
votes
1answer
73 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 ...
0
votes
1answer
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 ...
1
vote
0answers
29 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 ...
0
votes
1answer
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 ...
2
votes
1answer
53 views

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 ...
0
votes
0answers
16 views

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 ...
0
votes
1answer
89 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 ...
0
votes
0answers
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 ...
0
votes
1answer
78 views

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 ...
2
votes
1answer
228 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 ...
7
votes
2answers
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 ...
4
votes
0answers
63 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. ...
0
votes
0answers
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 ...
0
votes
1answer
285 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 ...
6
votes
1answer
217 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 ...
1
vote
0answers
278 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 ...
7
votes
3answers
224 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 ...
0
votes
1answer
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 ...
22
votes
1answer
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 ...
0
votes
1answer
360 views

ADVI Best Practices

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. ...
1
vote
1answer
113 views

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 ...
3
votes
0answers
440 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
578 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 ...
1
vote
0answers
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. ...
0
votes
2answers
222 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 ...
5
votes
0answers
809 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,...
1
vote
1answer
263 views

What makes parallel/distributed probabilistic inference difficult to implement?

My knowledge of probabilistic inference is severely limited, so coming from a Computer Science background I'm trying to understand what makes probabilistic inference difficult to implement in a ...
2
votes
1answer
261 views

Calculating acceptance rate in Monte Carlo Markov Chain while doing Bayesian analyis

I am doing Bayesian analysis using a Monte Carlo Markov Chain of length 10000 and burn-in length 1000. I consider my chain as converged when the acceptance rate is equal to 23% and the chain mixing ...
1
vote
1answer
55 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
55 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 ...
5
votes
1answer
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 ...
1
vote
0answers
146 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.
4
votes
2answers
48 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 ...
6
votes
0answers
406 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 ...
1
vote
1answer
107 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 ...
2
votes
1answer
404 views

Probabilistic Logical Graphical models like Markov Logic networks etc

I can't quite get a grasp of how and where these Probabilistic Logical Graphical Models (or PLGM or Statistical Relational Learning Models) score better than ordinary Probabilistic Graphical ...
1
vote
0answers
169 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 ...
2
votes
1answer
716 views

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 ...
0
votes
0answers
16 views

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): ...
3
votes
1answer
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 ...
8
votes
1answer
629 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 ...
1
vote
2answers
620 views

Any good book for learning probability programming

Are there any good books for me to learn probability programming? For example, I am new to Latent Dirichlet allocation (LDA) and Gibbs sampling. I have read some books about the techniques, but it ...