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Apologies if I missed this somewhere but what is the difference between these two probabilities?
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I can not understand the deduction process of Maximum likelihood under Chapter 5. Loss functions. Is there any math materials can help me to understand it better?
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Hi. I have doubts on how the code calculates NHP's log-likelihood, which is specified by equation (8) in the NHP paper:
![Screenshot 2024-09-18 at 14 54 42](https://github.com/user-attachments/assets…
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What is reported in github is not pseudo-log-likelihood:
https://github.com/ntranoslab/esm-variants/blob/main/esm_variants_utils.py#L90
Pseudo-likelihood requires masking each position one-by-one,…
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See https://github.com/arviz-devs/arviz/issues/2367 for example. We should look into what makes it be slow and try to fix it, it would also be nice to add a progressbar so it is less surprising when t…
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Hi,
I had the problem that my estimator got into NaN values after some steps. Sometimes from the second step, and sometimes it trains normally for longer (>1000 steps), depending on different setu…
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Hi, thanks for your great work!
I notice there is a ```discretized_gaussian_log_likelihood``` function to estimate the log-likelihood of the reconstructed representation from $x_1$. As the VAE has …
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The code in `_glm_predictive_samples` always applies `torch.softmax` to the results under classification.
For numerical stability supporting `torch.log_softmax` here would be helpful. Similarly, it…
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# Documentation/tutorial notebooks
As of `2.9.1`, the `log_marginal_likelihood` is deprecated.
See the docs [here](https://gpflow.github.io/GPflow/2.9.1/api/gpflow/models/gpr/index.html#gpflow.mo…
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# 🐛 Bug
`botorch.models.utils.gpytorch_modules` implements a few utility functions that return kernels and likelihoods. Those functions should enforce constraints on `kernel.lengthscale` and `likel…