  We construct a Bayesian inference deep learning machine for parameter
estimation of gravitational wave events of binaries of black hole coalescence.
The structure of our deep Bayesian machine adopts the conditional variational
autoencoder scheme by conditioning on both the gravitational wave strains and
the variations of the amplitude spectral density (ASD) of the detector noise.
We show that our deep Bayesian machine is capable of yielding posteriors
compatible with the ones from the nested sampling method and better than the
one without conditioning on the ASD. Our result implies that the process of
parameter estimation can be accelerated significantly by deep learning even
with large ASD drifting/variation. We also apply our deep Bayesian machine to
the LIGO/Virgo O3 events, the result is compatible with the one by the
traditional Bayesian inference method for the gravitational wave events with
signal-to-noise ratios higher than typical threshold value. We also discuss
some possible ways for future improvement.
