Questions tagged [probabilistic-programming]
The probabilistic-programming tag has no usage guidance.
61
questions
0
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1answer
17 views
Is there a way to supress p-values in tbl_regression function in R? [closed]
I want to make tables including exponentiated parameter coefficients with CIs, but not with p-values.
Right now i am using :
...
0
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0answers
21 views
How do I fit a multiple linear regression where I know the relationship between some of the coefficients
I want to fit a linear regression with multiple independent variables. Say that I know that the coefficient of one variable is twice as large as the coefficient of another variable, or that one ...
1
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0answers
37 views
What does it mean to create predictions on predicted probabilities?
Background:
I have a particular dataset of presence and absence of a specific bird. This data went through various means of simplification for machine learning techniques, for example random forests ...
0
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0answers
23 views
Does PyMC3's Timeseries API allow for time varying parameters of any model that would require fixed values under a frequentist approach?
Before diving into PyMC3, I would like to know if it can solve my problem.
I'm dealing with time series modeling and my problem is that my time series exhibit frequent structural breaks that make ...
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0answers
21 views
How do you write factor operations for rule-based conditional probability distributions?
Supposing I have rule-based conditional probability distributions (CPDs), $\{P(X|\text{Pa}_{X}), \cdots\}$, in a graphical model each represented as a set of rules $\mathscr{R}$ such that:
For each ...
2
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1answer
109 views
How to implement conditional probability distribution on set-valued Random variables?
A Random Set is a set-valued RV, i.e. a map $X:\Omega\to\mathcal{C}$ from a probability space $(\Omega,\Lambda,P)$ to the family of measurable closed sets $\mathcal{C}$ on a $\sigma-$algebra $\Lambda$ ...
0
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0answers
26 views
Bayesian regression learning (RVM) on weighted data (data with different importance/exposure)
I am working with an extension of the relevance vector machine (RVM) by Tipping (2001), and want to model some data which requires handling of an exposure column (different importance).
Is someone ...
1
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0answers
39 views
Bayesian meta-analysis of multiple ranked lists?
Let's say I go around and ask a bunch of my friends to rank 30 movies. Each one returns me a list. Now the obvious treatment is to average the rankings, but I'm wondering if anyone has seen a more ...
0
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0answers
16 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
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0answers
26 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
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1answer
35 views
Are nodes outside the markov blanket unconditionally independent?
Apologies if my question is deeply flawed, I've been working through a lot of material in the past few weeks and have a few blind spots here and there.
On one level my question is this - given a ...
0
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0answers
9 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 ...
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0answers
17 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 ...
0
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0answers
19 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:...
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0answers
19 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 ...
5
votes
1answer
79 views
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&...
0
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1answer
100 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 ...
3
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1answer
69 views
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 ...
1
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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
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1answer
561 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
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1answer
82 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 ...
2
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0answers
160 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
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1answer
28 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
132 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
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1answer
164 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 ...
1
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1answer
359 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
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1answer
234 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 ...
3
votes
1answer
498 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 ...
10
votes
2answers
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 ...
4
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0answers
79 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.
...
1
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0answers
34 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
1answer
356 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 ...
7
votes
1answer
367 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
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0answers
310 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
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0answers
28 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
247 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 ...
1
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1answer
153 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 ...
25
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1answer
3k 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
484 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
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1answer
123 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
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0answers
602 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
902 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
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. ...
0
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2answers
300 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
1k 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
318 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
472 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
59 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
59 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 ...
6
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 ...
