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Found 4 papers, 1 papers with code
The linear conditional expectation in Hilbert space
The linear conditional expectation (LCE) provides a best linear (or rather, affine) estimate of the conditional expectation and hence plays an important r\^ole in approximate Bayesian inference, especially the Bayes linear approach.
A Rigorous Theory of Conditional Mean Embeddings
Conditional mean embeddings (CMEs) have proven themselves to be a powerful tool in many machine learning applications.
Markov Chain Importance Sampling -- a highly efficient estimator for MCMC
As a by-product it enables estimating the normalizing constant, an important quantity in Bayesian machine learning and statistics.
Objective Priors in the Empirical Bayes Framework
Motivated by this principle and following an information-theoretic approach similar to the construction of reference priors, we suggest a penalty term that guarantees this kind of invariance.
Methodology 62G07
