Questions tagged [pymc]
PyMC is a Python library for performing Bayesian inference using MCMC. It is a Python equivalent to JAGS and BUGS.
265 questions
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What does skewed distribution as posterior mean?
I am trying to estimate the values of three unknown parameters in the Ordinary Differential Equation (ODE) using pyMC. Physically, A should be smaller than both B and C. But, given the data and ODE I ...
- 183
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1
answer
85
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Pymc-BART Index error
I am trying to follow an example notebook from here but I am running into some IndexErrors when executing the code.
Here's a Minimal working example:
...
- 598
3
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2
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180
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Fitting OLS with constraint on the betas [duplicate]
Suppose my model looks like $y=\beta_1x_1 + \beta_2x_2 + \beta_3x_3$, but there's a catch: I want to enforce $\beta_1=\beta_3$. How do I do this, in both frequentist and Bayesian setup? Specifically ...
- 509
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47
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PyMC model from aggregated data
I am starting with probabilistic programming and PyMC and seems that stuck with the first step. I am trying to figure out something very simple and then add complexity. Appreciate if you can help me ...
- 121
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Negative Binomial GLM - Overflow in the link function
I am trying to create a GLM to model some count data.
I am using the pymc package in python.
The set up is what I believe to be the usual set up:
$$Y \sim NB(\mu,k) $$
$$E(Y\vert\mathbf{X}) = \mu $$
$$...
- 95
3
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1
answer
104
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Estimating posterior of proportion of positives in population from per-observation probabilities
I have a sample from some population of 0s and 1s and need to estimate the posterior of the proportion of 1s in this population. But the catch is: for each observation in the sample I only have ...
- 131
2
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1
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625
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Why do we need to scale the variables in a Bayesian model?
In a Bayesian MMM model using pymc3 the variables are scaled. It is said that scaling helps in improving the efficiency of the MCMC algorithm. Also, it is stated that setting priors for the non-scaled ...
- 21
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38
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Lots of variability in the effective sample size but stable parameter estimates?
I ran 4 chains with NUTS and made a forest plot, but I cannot show the plot here. In words, what I am seeing is the there is a lot of variability in the effective sample size (ESS) in the chains. ...
- 10.1k
2
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264
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Bayesian meta-analysis: Why and how to weight individual study's contribution to overall effect?
I'm interested in performing a Bayesian meta-analysis, specifically, using a random-effects hierarchical model (as described here). Briefly, in this model we assume that the $k$th study's reported (...
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147
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A Bayesian marginal structural model (IPW) in a single model
Inspired by Richard McElreath's "Full Luxury Bayes" in his Statistical Rethinking course, I wanted to implement a "Full Luxury Bayesian Marginal Structural Model".
Briefly: MSMs ...
- 161
6
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1
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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 ...
- 10.1k
3
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1
answer
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Bayesian regression confidence intervals with Pymc3
This question is based on question 1 of the week 2 Statistical Rethinking problems, i.e. q1 here: https://github.com/rmcelreath/stat_rethinking_2022/blob/main/homework/week02.pdf
I have a pandas data ...
4
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2
answers
742
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Reason behind only using internal knots when defining basis splines
In the spline regression tutorials of pymc and bambi they first define the knots using quantiles, but for building the design matrix they don't use the boundary knots and only keep the internal knots. ...
2
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1
answer
230
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PyMC3 Beta-Binomial fails to converge on actual parameter values
Something is not performing as expected with PyMC. I'm trying a simple Beta-Binomial conjugate prior model, trying to recover known parameters.
Control data
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- 3,970
0
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95
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Need help explaining Bayesian p-value plot
I'm working through Chapter 2 of BMCP and am having trouble understanding why the plot of a Bayesian p-value looks so unexpectedly "spiky" or multimodal. Here's my code
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