Comments (2)
alhauser
commented on May 4, 2024
Currently, it is not possible to change this proportion.
This could be enabled by adding a new entry to the model.args argument of CausalImpact(), and passing it down to GetPosteriorSamples() where the burn-in rate of 0.1 is currently hardcoded. This would require downstream changes in tests and documentation, too.
We currently don't have plans to add this option as the default burn-in rate typically works well, but feel free to create a pull request if you want to implement that functionality.
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steve-the-bayesian
commented on May 4, 2024
Note that the burn-in rate for bsts has not been a strict 10% of draws for
a long time. Bsts looks at the log-likelihood associated with each
parameter draw. Bsts looks at the last 10% of draws (assuming convergence
has been achieved by then) to find an upper quantile of the log-likelihood
distribution. Burn-in is declared the first time the MCMC sample path of
log-likelihood exceeds this level.
In non-Gaussian models bsts does not compute log likelihood (because it is
not available in closed form), so it falls back to eliminating a fixed
percentage of draws.
…On Thu, Jul 15, 2021 at 9:14 AM Alain Hauser ***@***.***> wrote:
Currently, it is not possible to change this proportion.
This could be enabled by adding a new entry to the model.args argument of
CausalImpact(), and passing it down to GetPosteriorSamples() where the
burn-in rate of 0.1 is currently hardcoded
<https://github.com/google/CausalImpact/blob/7e0f59f944b22bfb3186a726215f56919d25e37f/R/impact_inference.R#L31>.
This would require downstream changes in tests and documentation, too.
We currently don't have plans to add this option as the default burn-in
rate typically works well, but feel free to create a pull request if you
want to implement that functionality.
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