Questions tagged [probabilistic-programming]
The probabilistic-programming tag has no usage guidance.
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1answer
23 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
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1answer
47 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 ...
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0answers
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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 ...
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1answer
42 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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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 ...
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1answer
52 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
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1answer
190 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
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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 ...
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0answers
60 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.
...
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0answers
24 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
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1answer
267 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
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1answer
206 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
...
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0answers
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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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0answers
26 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
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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
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1answer
116 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
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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
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1answer
313 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.
...
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1answer
110 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 ...
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0answers
382 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
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1answer
544 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 ...
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0answers
49 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
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2answers
204 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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0answers
767 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
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1answer
241 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
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1answer
243 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
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1answer
53 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 ...
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0answers
54 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
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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 ...
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0answers
139 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.
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2answers
46 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
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0answers
404 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
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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
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1answer
390 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 ...
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0answers
167 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
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1answer
676 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 ...
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0answers
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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):
...
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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 ...
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1answer
597 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 ...
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2answers
605 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 ...
