All Questions
Tagged with probabilistic-programming bayesian
23
questions
0
votes
0
answers
14
views
Process Modelling with LSTMs vs Probabilistic Programming
I am trying to model an aircraft’s turnaround process from the beginning (in-block) to the end (off-block). The goal is
to gain transparency about the progress of the process / subprocesses and
to ...
- 1
0
votes
0
answers
17
views
How to structure Bayesian model for hiring data based on race, performance, and years of experience
I'm working on an analysis of some HR data that is attempting to answer the following question:
Do applicants of different races have substantially different probabilities of being selected?
For now, ...
- 11
0
votes
0
answers
27
views
Best way to show one Bayesian model is more certain and accurate than another, based on simulated data?
I'm trying to compare performance of two bayesian models $A$ and $B$ on simulated data. It's a recruitment curve fitting problem and I'm interested in how accurate these models are in estimating only ...
- 211
1
vote
0
answers
49
views
Which is the best way to implement variational inference?
To implement variational inference in a Bayesian model, one essentially has the choice between different approaches that differ in their degree of automation and flexibility:
manually deriving update ...
- 11
5
votes
1
answer
457
views
How to interpret rank bar plot of a MCMC trace?
I am learning how to use PyMC for Bayesian inference. I coded up a random intercept $Y = \gamma + \sum_{j=1}^3 \beta_j \mathbb{I}_j + \epsilon$ and looked at the trace plots. Here is a ...
- 8,502
2
votes
0
answers
37
views
In what ways do conjugate priors compose?
A lot of conjugate priors are known for a lot of likelihood distributions (mostly the exponential family). But most Bayesian models in practice don't just consist of one distribution. Usually, you ...
- 210
1
vote
1
answer
596
views
Regarding Gibbs sampling and HMC in fitting Bayesian model, their differences and advantages
I have a question regarding the two MCMC algorithms, Gibbs sampling and Hamiltonian Monte Carlo (HMC) for performing the Bayesian analysis.
If using Gibbs sampling, my understanding is that we need to ...
- 5,162
1
vote
0
answers
66
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 ...
- 1,955
6
votes
1
answer
101
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&...
- 19.4k
3
votes
0
answers
4k
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 ...
2
votes
1
answer
800
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 ...
- 1,057
0
votes
1
answer
35
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 ...
- 235
11
votes
2
answers
3k
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 ...
- 864
1
vote
0
answers
29
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
1
answer
535
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.
...
- 826
5
votes
0
answers
1k
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 ...
- 330
2
votes
0
answers
60
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. ...
- 21
2
votes
1
answer
925
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 ...
- 255
6
votes
1
answer
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 ...
- 162
1
vote
0
answers
179
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.
- 11
2
votes
1
answer
1k
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 ...
- 21
3
votes
1
answer
2k
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 ...
- 33
12
votes
1
answer
1k
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 ...
- 19.4k