Comments (2)
not sure i follow but yes that's what i'd expect the code to be doing
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Thank you for prompt response. To clarify, I would like to build upon your work and sample from the posterior distribution P(parameter | data). I proceed as follows:
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create a base model and a swag model separately. When training the base model, at the end of each epoch, I call swag_model.collect_model(base_model)
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after training, I proceed as follows:
call: base_model.sample() // if I am not mistaken, it sets the parameters of the swag model to 'w' where w ~ P(param | data)
call: base_model.compute_log_prob() // returns an estimate of log(P(w | data))
Would you please confirm / correct my understanding of the code.
Thanks a lot in advance.
from swa_gaussian.
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