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Any distribution available in `edward.stats` should have a corresponding distribution object that is also available in `edward.models`.
Discrete
- [x] Bernoulli
- [ ] Binomial
- [x] Multinomial (assu…
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Creating an issue to track `stan_betareg`, as we don't actually have one open for this yet. Current status is that it's almost ready on the `feature/betareg` branch thanks to @imadmali.
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Thank you for the wonderful package. I'm running into the following issue though: model.fit() complains that there is a missing attribute in pymc3 (dot):
```
data = pd.DataFrame({ 'one' : pd.Series([…
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Is stan ever going to support sampling discrete parameters?
Now we have to try to marginalize the parameter and rewrite the code. it's complex and prone to errors.
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Admit generic ensemble sampling within Stan's sampler engine.
Allows running multiple chains internally that can communicate with each other, possibly useful during adaptation. Allows ensemble sampl…
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```
{-# OPTIONS_GHC -Wall #-}
{-# LANGUAGE TypeFamilies #-}
{-# LANGUAGE BangPatterns #-}
module Main where
import Control.Monad.Bayes.LogDomain
import Control.Monad.Bayes.Primitive
i…
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Discrete
- [x] Bernoulli
- [x] Binomial
- [x] Multinomial
- [x] Poisson
- [x] Geometric
- [x] Negative Binomial
Continuous
- [x] Uniform
- [x] Multivariate Normal
- [x] Student-t (with mean and scale…
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I am pretty much a statistics and probability newb, so If there is a better way to solve this problem which doesn't involve this approach, I am all ears.
I have a fairly large data set ( > 500k rows…
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Should this always be the case? For uniformly distributed random number in [0,1) I would expect the least significant bits to have some skew. But is it right that the final bit is always zero?
```
…
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We want to support modeling languages which are either popular or are useful for certain tasks over the alternatives we support. With that in mind, [pymc3 seems appealing](http://pymc-devs.github.io/p…