Zeel B Patel
Zeel B Patel
## Instructions * Follow [the contributing guidelines](https://github.com/probml/pyprobml/blob/master/CONTRIBUTING.md) and specific instructions given over [here](https://github.com/probml/pyprobml/blob/master/notebooks/README.md). ## Dashboards - ### [Book1 Figures](https://github.com/probml/pyprobml/blob/workflow_testing_indicator/dashboard_figures_book1.md) - ### [Book2 Figures](https://github.com/probml/pyprobml/blob/workflow_testing_indicator/dashboard_figures_book2.md) - ### [Mapping of Fig name to...
Solve the current errors in the notebooks mentioned in #739. Currently, 25 notebooks are failing due to various minor issues.
This process can help us determine which notebooks need acceleration/optimization to speed up the workflow. The timing can be added as a third column in [this dashboard](https://github.com/probml/pyprobml/blob/workflow_testing_indicator/README.md).
# Instructions * [Follow these guidelines](https://github.com/probml/pyprobml/blob/master/notebooks/README.md) ## Chapter 18: Gaussian processes | Figure | Script | Notebook | PR | Author | | :- | :- | :- | :-...
# Instructions * [Follow these guidelines](https://github.com/probml/pyprobml/blob/master/notebooks/README.md) ## Chapter 17: Bayesian neural networks | Figure | Script | Notebook | PR | Author | | :- | :- | :- |...
Hi, I was trying to use `tfp.math.minimize_stateless` for one of my applications and found that it automatically optimizes all the arguments passed to the `loss_fun`. For example, here is a...
I'm referring to the marginalization and conditioning figure (3rd figure in the article) here. As per my understanding, $\sigma_{Y|X}^2=\sigma_Y^2-\frac{\sigma_{XY}^2}{\sigma_X^2}$. While I'm trying to change the bivariate distribution of $X$ and...
### Description of the bug Hi @wesselb, when I try to import stheno with tensorflow_probability (reproduced in a fresh Google colab environment), it takes a long time, around 30 seconds....
### Description of the bug Hi @wesselb, I am trying to write some GP code in JAX and accelerate it with `jax.jit`, but it is failing due to a numpy...
# Feature Request **Describe the Feature Request** I think the current implementation does not support heteroscedastic noise variance. https://github.com/thomaspinder/GPJax/blob/db40b9cb20103a5f7104b1ccd0ad12713f44bc06/gpjax/gps.py#L163 It can be tweaked with a few lines to support homoscedastic...