description: Tune the number of steps and scaling of one mutation.
View source on GitHub |
Tune the number of steps and scaling of one mutation.
tfp.experimental.mcmc.simple_heuristic_tuning(
num_steps, log_scalings, log_accept_prob, optimal_accept=0.234,
target_accept_prob=0.99, name=None
)
This is a simple heuristic for tuning the number of steps of the next mutation, as well as the scaling of a transition kernel (e.g., step size in HMC, scale of a Normal proposal in RWMH) using the acceptance probability from the previous mutation stage in SMC.
Args | |
---|---|
num_steps
|
The initial number of steps for the next mutation, to be tune. |
log_scalings
|
The log of the scale of the proposal kernel |
log_accept_prob
|
The log of the acceptance ratio from the last mutation. |
optimal_accept
|
Optimal acceptance ratio for a Transitional Kernel. Default value is 0.234 (Optimal for Random Walk Metropolis kernel). |
target_accept_prob
|
Target acceptance probability at the end of one mutation step. Default value: 0.99 |
name
|
Python str name prefixed to Ops created by this function.
Default value: None .
|
Returns | |
---|---|
num_steps
|
The number of steps for the next mutation. |
new_log_scalings
|
The log of the scale of the proposal kernel for the next mutation. |