tfp.experimental.mcmc.resample_independent
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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].
Args |
|---|
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').
|
Returns |
|---|
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
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Last updated 2023-11-21 UTC.
[null,null,["Last updated 2023-11-21 UTC."],[],[],null,["# tfp.experimental.mcmc.resample_independent\n\n\u003cbr /\u003e\n\n|------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/tensorflow/probability/blob/v0.23.0/tensorflow_probability/python/experimental/mcmc/weighted_resampling.py#L183-L240) |\n\nCategorical resampler for sequential Monte Carlo. \n\n tfp.experimental.mcmc.resample_independent(\n log_probs, event_size, sample_shape, seed=None, name=None\n )\n\nThe return value from this function is similar to sampling with \n\n expanded_sample_shape = tf.concat([[event_size], sample_shape]), axis=-1)\n tfd.Categorical(logits=log_probs).sample(expanded_sample_shape)`\n\nbut with values sorted along the first axis. It can be considered to be\nsampling events made up of a length-`event_size` vector of draws from\nthe `Categorical` distribution. For large input values this function should\ngive better performance than using `Categorical`.\nThe sortedness is an unintended side effect of the algorithm that is\nharmless in the context of simple SMC algorithms.\n\nThis implementation is based on the algorithms in \\[Maskell et al. (2006)\\]\\[1\\].\nIt is also known as multinomial resampling as described in\n\\[Doucet et al. (2011)\\]\\[2\\].\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|----------------|---------------------------------------------------------------------------------------------------------------------------------|\n| `log_probs` | A tensor-valued batch of discrete log probability distributions. |\n| `event_size` | the dimension of the vector considered a single draw. |\n| `sample_shape` | the `sample_shape` determining the number of draws. |\n| `seed` | PRNG seed; see [`tfp.random.sanitize_seed`](../../../tfp/random/sanitize_seed) for details. Default value: None (i.e. no seed). |\n| `name` | Python `str` name for ops created by this method. Default value: `None` (i.e., `'resample_independent'`). |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ------- ||\n|---------------------|----------------------|\n| `resampled_indices` | a tensor of samples. |\n\n\u003cbr /\u003e\n\n#### References\n\n\\[1\\]: S. Maskell, B. Alun-Jones and M. Macleod. A Single Instruction Multiple\nData Particle Filter.\nIn 2006 IEEE Nonlinear Statistical Signal Processing Workshop.\n\u003chttp://people.ds.cam.ac.uk/fanf2/hermes/doc/antiforgery/stats.pdf\u003e\n\\[2\\]: A. Doucet \\& A. M. Johansen. Tutorial on Particle Filtering and\nSmoothing: Fifteen Years Later\nIn 2011 The Oxford Handbook of Nonlinear Filtering\n[https://www.stats.ox.ac.uk/\\~doucet/doucet_johansen_tutorialPF2011.pdf](https://www.stats.ox.ac.uk/~doucet/doucet_johansen_tutorialPF2011.pdf)"]]
View source on GitHub