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Hi all,
While playing around with Edwards and Stan, I try to port a Fully-treated Bayesian PCA from [Stan](https://www.cs.helsinki.fi/u/sakaya/tutorial/code/pca.R) to Edward. It is only different f…
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### User Story
As a Fabric8-analytics IDE user I should be able to get recommendations for the NPM ecosystem within the time frame committed in the SLA.
### Acceptance Criteria
Confirmation…
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Hi,
I wanted to see if it's possible with the current pymc design to separate observations of random variables's values in a probabilistic model from the definition of the model itself. If not, I thi…
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to foster community involvement - some richer sample code beyond MNIST should be tackled.
Generative Adversarial Networks is a hot topic amongst ML - and some sample code using swift should help enco…
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I am new to both pyro and probabilistic programming but I tried to do my homework before I raise this issue, please bear with me if it is real basic.
I was going through the Bayesian regression tuto…
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Dear Todd,
I have just run a non-parametric with 10000 permutations in SPM and the same non-parametric procedure with the same permutation on the data and I get different results. Is there an expla…
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https://github.com/mit-probabilistic-computing-project/ppaml-cps/blob/master/cp4/p1_regression/regression.py#L148 is the only place that (appears to?) relies on a non-persistent inference trace.
The …
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It seems that any hyperparameters you feed kernels on initialization are immediately enclosed in a `Param`object. So anything that's not a Numpy array will be rejected.
How would we handle kernels…
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Curious why all the params are auto-magically global with a shared namespace. Seems like a very different design choice than the nice cleanly scoped pytorch variables.
srush updated
6 years ago
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Continue to #702, the following steps are necessary to make GP module more accessible.
- [x] Support multidimensional output (currently, the output `y` is assumed to be 1D). This should not be comp…