Suppose we want to derandomize A—that is, give a deterministic variant of A which succeeds with probabilty 1 on every input. Sometimes we can do this using the method of conditional expectation. We can think of A as a binary tree which, given x, branches on the sampled value of each random bit Ri in turn. Paths in this tree correspond to different possible random strings R1, . . . , Rm that cou...

A general methodology is proposed for the explanation of variability in a quantity of interest x in terms of covariates z = (z1, . . . ,zL). It provides the conditional mean x̄(z) as a sum of components, where each component is represented as a product of non-parametric one-dimensional functions of each covariate zl that are computed through an alternating projection procedure. Both x and the zl...

The ‘expectation–conditional maximization either’ (ECME) algorithm has proven to be an effective way of accelerating the expectation–maximization algorithm for many problems. Recognizing the limitation of using prefixed acceleration subspaces in the ECME algorithm, we propose a dynamic ECME (DECME) algorithm which allows the acceleration subspaces to be chosen dynamically. The simplest DECME im...

The behavior of the conditional expectation E{X I Y) under a small perturbation Z of the conditioning random variable Y is analyzed. We show that if Y and Z are independent then E(XIY + EZ) converges to E{XIY) in mean as E --* 0 for all integrable X, provided the distribution of Y is absolutely continuous. We also show that the limit is E{X 1 Y, Z} rather than E(X 1 Y}, i.e., there is no stabil...

Given an operator T : UX(Σ) → Y or T : C(H,X) → Y , one may consider the net of conditional expectation operators (Tπ) directed by refinement of the partitions π. It has been shown previously that (Tπ) does not always converge to T . This paper gives several conditions under which this convergence does occur, including complete characterizations when X = R or when X∗ has the Radon-Nikodým prope...

In this paper, we analyze methods for estimating the density of a conditional expectation. We compare an estimator based on a straightforward application of kernel density estimation to a bias-corrected estimator that we propose. We prove convergence results for these estimators and show that the bias-corrected estimator has a superior rate of convergence. In a simulated test case, we show that...

We present the CEM (Conditional Expectation Maximization) algorithm as an extension of the EM (Expectation Maximization) algorithm to conditional density estimation under missing data. A bounding and maximization process is given to speciically optimize conditional likelihood instead of the usual joint likelihood. We apply the method to conditioned mixture models and use bounding techniques to ...

In the previous chapter, Kanamori and Shimodaira provided generalization error estimators which can be used for model selection and active learning. The accuracy of these estimators is theoretically guaranteed in terms of the expectation over realizations of training input-output samples. In practice, we are only given a single realization of training samples. Therefore, ideally, we want to hav...

This paper addresses the issue of matching rigid 3D object points with 2D image points through point registration based on maximum likelihood principle in computer simulated images. Perspective projection is necessary when transforming 3D coordinate into 2D. The problem then recasts into a missing data framework where unknown correspondences are handled via mixture models. Adopting the Expectat...

In this paper, we discuss how to approximate the conditional expectation of a random variable Y given a random variable X, i.e. E(Y|X). We propose and compare two different non parametric methodologies to approximate E(Y|X). The first approach (namely the OLP method) is based on a suitable approximation of the σ-algebra generated by X. A second procedure is based on the well known kernel non-pa...