-
Since `bayestestR` can really be used with any distribution of data, I think it would be good to have a vignette about using it with non-Bayeisan distributions - for example how to use `bayestestR` wi…
-
Hi Josh,
I've been moving toward MCMC results being in the form of an iterator instead of an array, and encouraging others in this direction as well. This convenience and flexibility in a lot of di…
-
https://github.com/JasperSnoek/spearmint
http://people.seas.harvard.edu/~jsnoek/software.html
http://fastml.com/tuning-hyperparams-automatically-with-spearmint/
http://www.johnmyleswhite.com/notebo…
-
Like we have [Stan](http://mc-stan.org/) for Python, R as HMC requires some tuning, so if we use [NUTS](http://andrewgelman.com/2011/11/30/stan-uses-nuts/) (Stan's sampler) which is more computationa…
-
- [ ] Implement a Bayesian network.
- [ ] Test the bayesian network (by comparing in an easy task).
-
Hello, after reading your library, I would say it is a great job for uncertainty calibration, but I was wondering if the model is limited that in bayes regression model, input and output must have the…
-
We can add a few example Turing models or Bayesian workflow following this: https://prefix.dev/
yebai updated
1 month ago
-
File: https://github.com/pymc-devs/pymc-examples/blob/main/examples/variational_inference/bayesian_neural_network_advi.ipynb
Reviewers:
> The sections below may still be pending. If so, the issue is …
-
Evaluate improvement in code efficiency for the following
- [ ] pyMC3
- [ ] cov_exp_quad function in Stan
- [ ] multi_gp function in Stan
-
In the docs, it says that at some point, HyperOpt will also include support for Bayesian optimization through either Gaussian processes or regression trees; any idea on when that could be implemented?…