aemcmc

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Ratio distributions are distributions of random variables $Z$ that can be formed as the ratio $Z = X/Y$ of two other random variables $X$ and $Y$. Common examples are Cauchy...

rlouf

Use `at.dot` instead of `@` to support sparse data matrices

1

We should be able use both sparse and dense matrices for `X` in `horseshoe_nbinom_gibbs`. While Aesara is designed so that `at.dot` can be used with both, `@` currently raises an...

rlouf

Examples that come to mind are the Binomial distribution and the Wald distribution (but there are so many others, see the Leemis chart for more examples). We will need to...

rlouf

Add more scale-mixtures

1

We need to add more scale-mixtures.

brandonwillard

`uniform` is already available in Aesara, and `t` will be added in #1211. The change is minimal and consists in adding two `fact`s in `transforms.py`.

rlouf

Implement relations between distributions in the Leemis chart

1

The [Leemis chart](http://www.math.wm.edu/~leemis/chart/UDR/UDR.html) represents relationships between univariate probability distributions that might be useful to simplify model graphs / rewrite model graphs to build better samplers.

rlouf

Expand scale-mixtures

1

After #45, we need to add a scale-mixture-expansion step to the sampler-generating process. We can start by implementing the Polya-gamma expansion for negative-binomials and Bernoulli random variables. This will work...

brandonwillard

We need to implement more conjugate relationships and posteriors.

brandonwillard

Add reversible jump MCMC

1

Reversible Jump MCMC algorithms are a generalization of the Rosenbluth-Metropolis-Hastings algorithm for when the target density does not have a fixed number of dimensions. Since `aesara` can handle variables of...

rlouf

Copying @brandonwillard's message from #4. If we lift random/measurable variables through mixtures, we can enable some important closed-form posterior opportunities. For example: ```python import aesara import aesara.tensor as at srng...

rlouf