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Make a deterministic swap proposal function, alternating even/odd swaps.
tfp.mcmc.even_odd_swap_proposal_fn(
swap_frequency, name=None
)
This proposal function swaps deterministically swap_frequency fraction of
the time, alternating even and odd parity.
This was shown in [2] to mix better than random schemes.
Contrast this with default_swap_proposal_fn, which swaps randomly with
probability prob_swap.
swap_fn = even_odd_swap_proposal_fn(swap_frequency=1)
even_odd_swap_proposal_fn(num_replica=4, step_count=0)
==> [1, 0, 3, 2] # Swap 0 <--> 1 and 2 <--> 3, even parity.
even_odd_swap_proposal_fn(num_replica=4, step_count=1)
==> [0, 2, 1, 3] # Swap 1 <--> 2, odd parity.
Args | |
|---|---|
swap_frequency
|
Scalar Tensor in [0, 1] giving the frequency of swaps.
Swaps will occur, with alternating parity, every N steps, where
N = 1 / swap_frequency.
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name
|
Python str name given to ops created by this function.
Default value: 'even_odd_swaps'.
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Returns | |
|---|---|
default_swap_proposal_fn_
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Python callable which take a number of
replicas (a Python integer), and integer Tensor batch_shape, a
step_count, a seed, and returns swaps, a shape
[num_replica] + batch_shape Tensor, where axis 0 indexes
"one-time swaps", i.e., such that (if rank(swaps) == 1,
range(num_replicas) == tf.gather(swaps, swaps).
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References
[1]: S. Syed, A. Bouchard-Cote G. Deligiannidis, A. Doucet Non-Reversible Parallel Tempering: a Scalable Highly Parallel MCMC Scheme https://arxiv.org/abs/1905.02939