I want to add some noise to the variable \alpha like this: \alpha=\alpha*exp(\varepsilon) where \varepsilon~Normal(0,\sigma) is a separate, independent noise term (non-trainable) and \sigma~HalfNormal(sigma=1) (trainable). The distribution of random variables in pymc is trainable as default, bu…

You need to implement a custom step sampler that always updates the value of the variable with a new draw from the prior, regardless of the model logp. This thread may be useful: Custom sampling step - #32 by ricardoV94

How to get a non-trainable random variable?

Questions v3

wmylxmj April 12, 2023, 10:49am 3

Thank you so much! it works.

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How to get a non-trainable random variable?

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