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Ran 13 Feb 2018 05:06PM UTC

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Committed 13 Feb 2018 04:32PM UTC coverage: 40.289% (+0.03%) from 40.256%

Job # PYTHON_VERSION=3.6 FLOATX='float64' TESTCMD="--durations=10 --cov-append pymc3/tests/test_distributions_random.py pymc3/tests/test_shared.py pymc3/tests/test_smc.py pymc3/tests/test_sampling.py"

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push

travis-ci

Committed by

ColCarroll

Commit Message

Fix effective sample size estimation (#2854)

* Fix effective sample size estimation

Close #2810
Reference implementation in PyStan: https://github.com/stan-dev/pystan/pull/415
- improve autocorr and autocov function, use fft convolve for autocorrelation computation

* Improve effective sample size implementation

Effective sample size computed using autocov and Geyer's initial monotone sequence criterion for vhat.

* Improve effective sample size implementation

Effective sample size computed using autocov and Geyer's initial monotone sequence criterion for vhat.

Co-authored-by: Junpeng Lao <junpeng.lao@unifr.ch>
Co-authored-by: Sharan Yalburgi <sharanyalburgi@gmail.com>

* Turn off automatic convergent check for short chain

* update release note

* Turn off convergent check in benchmark model

To have more accurate computation of the things we want to benchmark. 
effective_n computation is slower than before for large number of samples (at 10000 sample it is around 10x slower).
There should be a large increase in effective_n benchmark.

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