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Hey @sunxd3 -- we realized that working with flattened_values as state for our samplers is going to result in loss of precision for models with mixed states. For example, when we have ints and floats, flattened_values is concretely typed Vector{Float64} so the ints are cast as floats. And that ends up causing issues when we want to dispatch for coordinate-wise sampling. Therefore, it would be nice to just work with the evaluation_env inside the model as the proper state. But then we don't have a way of computing tempered_evaluate!!(model).
Going further, it seems that (in the long run) it would make your life easier to have a unique "barebones_tempered_evaluate!!" such that all the special cases -- with/without tempering, with/without rng, and with/without flattened values -- are handled via light pre-processing of inputs. Do you think that would be possible?
The text was updated successfully, but these errors were encountered:
Hey @sunxd3 -- we realized that working with
flattened_values
as state for our samplers is going to result in loss of precision for models with mixed states. For example, when we have ints and floats,flattened_values
is concretely typed Vector{Float64} so the ints are cast as floats. And that ends up causing issues when we want to dispatch for coordinate-wise sampling. Therefore, it would be nice to just work with theevaluation_env
inside the model as the proper state. But then we don't have a way of computingtempered_evaluate!!(model)
.Going further, it seems that (in the long run) it would make your life easier to have a unique "
barebones_tempered_evaluate!!
" such that all the special cases -- with/without tempering, with/without rng, and with/without flattened values -- are handled via light pre-processing of inputs. Do you think that would be possible?The text was updated successfully, but these errors were encountered: