probabilistic-programming

NumPyro now has several excellent introductory examples with no direct counterparts in Pyro. Porting one of these to Pyro would be a great way for someone to simultaneously learn more about Bayesian data analysis and make a valuable open source contribution.

If you are reading this and want to give one of them a try, please leave a comment here so that other peo

The currently implemented version of the horseshoe distribution is not the parameterization that most ML papers use. This limits the ease of use of this as, for example, a prior in a tfp.layers.KLDivergenceAddLoss or in tfp.layers.DenseReparameterization. The regularized horseshoe would also be useful as an implemented distribution.

The alternative parameterization is shown here:
https://www.

Ankit Shah and I are trying to use Gen to support a project and would love the addition of a dirichlet distribution

Hi,

I am trying to use random_flax_module on a class that uses flax.linen.BatchNorm that uses mutable parameters. Is there any example on how to use that? Here is my code:

The model:

class NSBlock(nn.Module):

    train: bool
    dim: int
    ks_1: int = 3
    ks_2: int = 3
    dl_1: int = 1
    dl_2: int = 1
    mp_ks: int = 3
    mp_st: int = 1

    @nn.compact
    def

The current example on MDN from Edward tutorials needs small modifications to run on edward2. Documentation covering these modifications will be appreciated.

See #228

Rather than trying to rebuild all functionality from Distributions.jl, we're first focusing on reimplementing logdensity (logpdf in Distributions), and delegating most other functions to the current Distributions implementations.

So for example, we have

distproxy(d::Normal{(:μ, :σ)}) = Dists.Normal(d.μ, d.σ)

This makes some functions in Distributions.jl available through

Hi,

Looks like there is support for lots of common distribution. There are a handful of other distributions which are not presently supported but could (fingers crossed) be easily implemented. Looking at [Stan's Function Reference] I see...

• Beta Binomial
• [Chi-Square](https://mc-stan.org/docs/2

There are a variety of interesting optimisations that can be performed on kernels of the form

k(x, z) = w_1 * k_1(x, z) + w_2 * k_2(x, z) + ... + w_L k_L(x, z)

A naive recursive implementation in terms of the current Sum and Scaled kernels hides opportunities for parallelism in the computation of each term, and the summation over terms.

Notable examples of kernels with th

This way we can do model selection at the ensemble level. Implementation is really simple, all we need is a wrapper to the Sktime mixer but enforcing the model_path parameter to point to the AutoARIMA forecaster.