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Hey @rsullivan-lord,
I was able to get PyABC set up and working. It looks like it's able to get posteriors that make sense, which is good! Instead of predicting for the whole data set (i.e. 1980 to…
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Hello,
My research needs to change the prior distribution parameters to train a Bayesian Neural Network model. More specifically, I want to change the prior Gaussian distribution mu and sigma.
…
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I thank reviewer 3 for raising this issue (rephrased by AHL):
The normalised curvature matrix [used to find the optimal value of alpha] is constructed numerically from chi2 using a forward Euler me…
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Thanks for an excellent and well thought out framework.
It looks like from a model evaluation perspective - freeze/unfreeze have no role to play . To predict on a held out set it is enough to set t…
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I'm working with a model that has a DenseVariational layer defined as follows (lifted from examples found online):
```
def posterior_mean_field(kernel_size, bias_size=0, dtype=None):
n = kernel…
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Collection of questions regarding the first milestone.
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#### issue
+ Update the function's description:
- Add more information about ridge plots.
- Change the term 'traces' for 'models.'
- The forest plot's description should specify that…
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[Rezende and Mohamed 2015](https://arxiv.org/abs/1505.05770) is a classic paper on using normalizing flows, rather than e.g. mean-field approximations, for variational inference. Normalizing flows are…
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In 5SSD0, we have a simple binary classification model:
```
@model function linear_classification(y,X)
θ ~ MvNormalMeanCovariance(zeros(D), diageye(D))
for i in eachindex(y)
…
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Hi, thanks so much for the package, it's a huge help! I am cross-posting this issue on [the stan forums](https://discourse.mc-stan.org/t/posterior-predictive-check-with-two-grouping-variables/25222) i…