tfp.experimental.mcmc.resample_independent

Categorical resampler for sequential Monte Carlo.

tfp.experimental.mcmc.resample_independent(
    log_probs, event_size, sample_shape, seed=None, name=None
)

The return value from this function is similar to sampling with

expanded_sample_shape = tf.concat([[event_size], sample_shape]), axis=-1)
tfd.Categorical(logits=log_probs).sample(expanded_sample_shape)`

but with values sorted along the first axis. It can be considered to be sampling events made up of a length-event_size vector of draws from the Categorical distribution. For large input values this function should give better performance than using Categorical. The sortedness is an unintended side effect of the algorithm that is harmless in the context of simple SMC algorithms.

This implementation is based on the algorithms in [Maskell et al. (2006)][1]. It is also known as multinomial resampling as described in [Doucet et al. (2011)][2].

log_probs A tensor-valued batch of discrete log probability distributions.
event_size the dimension of the vector considered a single draw.
sample_shape the sample_shape determining the number of draws.
seed PRNG seed; see tfp.random.sanitize_seed for details. Default value: None (i.e. no seed).
name Python str name for ops created by this method. Default value: None (i.e., 'resample_independent').

resampled_indices a tensor of samples.

References

[1]: S. Maskell, B. Alun-Jones and M. Macleod. A Single Instruction Multiple Data Particle Filter. In 2006 IEEE Nonlinear Statistical Signal Processing Workshop. http://people.ds.cam.ac.uk/fanf2/hermes/doc/antiforgery/stats.pdf [2]: A. Doucet & A. M. Johansen. Tutorial on Particle Filtering and Smoothing: Fifteen Years Later In 2011 The Oxford Handbook of Nonlinear Filtering https://www.stats.ox.ac.uk/~doucet/doucet_johansen_tutorialPF2011.pdf