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tf-probability error using Wishart prior #194
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There are several issues with the use of Wishart distributions. The first one seems to be the use of float32 precision, then Cholesky decomposition may easily fails, UCL-SML/Doubly-Stochastic-DGP#9 This code does not raise this error. Note the use of inf.set_floatx('float64')' to modify the precision. Note also how cov0 is defined as well as the qmodel to guarantee a proper cholesky decomposition of the data. ` inf.set_floatx('float64') n=10 @inf.probmodel @inf.probmodel m = flat_model() data = m.prior().sample(100)["x"] m.fit({"x": data}, VI) m.posterior("meanrets").parameters() Even though it seems the VI algorithm does not converge. This notebook analyze the problem of covariance estimation using a Bayesian approach in detail. By it is really involve and requires of the use of bijectors. If we find a simpler way to achieve it, we will include it in the documentation. |
I am having trouble estimating a covariance matrix using a Wishart prior. This may be related to a previously reported issue in tensorflow-probability: https://github.com/GPflow/GPflow/issues/553
Error message:
InvalidArgumentError: Cholesky decomposition was not successful. The input might not be valid. [[{{node cov_194/log_prob/Cholesky}}]]
Code:
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