Implement NanMaskedNormal, NanMaskedMultivariateNormal #3116
Conversation
|
Nice, I remember that this is requested by many forum users. |
| result = value.new_zeros(n) | ||
|
|
||
| # Evaluate ok elements. | ||
| for pattern in sorted(set(map(tuple, ok.tolist()))): |
There was a problem hiding this comment.
oh i thought you were computing one big marginalized covariance with 0s/1s where appropriate so that everything could be vectorized (no for loop)
There was a problem hiding this comment.
😄 that's beyond my linear algebra skills / patience. In practice I'm working with 3 columns so there are at most 7 patterns.
| ok_value = value[row_mask][:, col_mask] | ||
| ok_loc = loc[row_mask][:, col_mask] | ||
| ok_cov = cov[row_mask][:, col_mask][:, :, col_mask] | ||
| marginal = MultivariateNormal(ok_loc, ok_cov, validate_args=False) |
There was a problem hiding this comment.
do these invocation not need covariance_matrix=?
There was a problem hiding this comment.
i guess one nice thing about this pattern is that you don't need to worry about factors of log 2pi explicitly...
There was a problem hiding this comment.
covariance_matrix is the default first argument, so no kwarg is necessary.
* Implement NanMaskedNormal, NanMaskedMultivariateNormal * Fix test * Add test for fully-unobserved data
This implements two distributions to serve as likelihoods for partially observed data, where unobserved elements are specified as NAN values. This is new functionality beyond
pyro.mask()andDistribution.mask()in that it allows NAN values within an event of MultivariateNormal; in this case we can analytically marginalize out the missing value. TheNanMaskedNormalis similar toNormal.mask(...), but I've included it for easier compatibility with the nontrivialNanMaskedMultivariateNormal.My motivating example is a Bayesian multivariate linear regression model with learned multivariate noise distribution and partially observed response as specified in a pandas dataframe. Each of the response columns is differently partially observed.
Tested
NanMaskedNormalNanMaskedMultivariateNormalNanMaskedMultivariateNormal