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Hello there,
I have a simple model and several issues. I am unclear if they are related or not.
The first involves group effects
```
model = bmb.Model(
"gold_label ~ (predictor_a | pred…
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The idea of posteriori is basically advanced napkin math, so you don't want to get bogged down with false precision. That said,
1. maybe you have data
2. maybe you _have_ fit some sort of model, eith…
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Hello authors,
Thank you for your excellent work.
When I tried to create the conda environment according to the instructions, I encountered this issue.
```
Collecting package metadata (curren…
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Hi @marius311,
Great work on MUSE! I am wondering if you are interested in contributing MUSE to [BlackJAX](https://github.com/blackjax-devs/blackjax).
Some benefits:
- Easy discoverable for broad…
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### Discussed in https://github.com/pymc-labs/pymc-marketing/discussions/837
Originally posted by **AlfredoJF** July 17, 2024
Hi,
Is it possible to implement callbacks in pymc-marketing? If…
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It is often useful / sufficient for posterior predictives (examples for purposes of plotting) to be based on less than all samples from a given call to `.fit()`.
It seems like currently one cannot…
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See :
```
EXPERIMENT
Source filename: /home/bas/Git/choderalab/host-guest/SAMPL4-CB7/itc/data/02042015/20150204a15.itc
Number of injections: 11
Target temperature: 298.1 K
Equilibration time before …
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Hi, firstly thanks for a great package!
I _think_ arviz doesn't currently support posterior predictive checks for discrete data - is that correct? Assuming that is correct - is there any interest i…
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```python
import numpy as np
import aesara.tensor as at
x = np.full((2, 3, 3), np.eye(3))
np.linalg.cholesky(x) # broadcast operation fine
at.linalg.cholesky(x) # AssertionError x.ndim == 2
…
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### Describe the issue:
I get failures near the beginning of sampling when running models on Linux. They are coming from the multiprocessing library. I can usually work around them by simply restar…