Adds Annealed Importance Sampling #550
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## develop #550 +/- ##
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- Coverage 99.08% 98.68% -0.41%
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Files 52 56 +4
Lines 3605 3941 +336
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+ Hits 3572 3889 +317
- Misses 33 52 +19 ☔ View full report in Codecov by Sentry. |
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the algorithm doesn't seem correct to me, but I could be miss-reading it. In addition, I would recommend writing a test for the algorithm, using a known distribution. I'd use the same unimodel distribution used in the paper, so you can compare with its results.
| # Main sampling loop | ||
| for j in range(1, self._num_beta): | ||
| # Compute jth transition with current sample | ||
| log_density_current[j] = self.transition_distribution(current, j) |
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you are doing f_j(x_j) / f_{j-1}(x_j), should be f_j(x_j) / f_{j+1}(x_j) from eqn 11
| log_density_current = np.zeros(self._num_beta) | ||
| log_density_current[0] = current_f | ||
| log_density_previous = np.zeros(self._num_beta) | ||
| log_density_previous[0] = current_f |
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same here, log_density_previous[0] should be f_{1}(x_0) from eqn 11
| proposed_f = self.transition_distribution(proposed, j) | ||
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| # Metropolis acceptance | ||
| acceptance_log_prob = proposed_f - current_f |
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for the gaussian test case they use in the paper, a much more complicated transition T_j is used, a sequence of 3 metropolis tests repeated 5-10 times. I'm not sure if all that is neccessary however
Description
Adds an annealed importance sampling class for marginal likelihood computation.
Issue reference
Fixes #549
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