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Although three-dimensional electron microscopy (3D-EM) permits structural characterization of macromolecular assemblies in distinct functional states, the inability to classify projections from structurally heterogeneous samples has severely limited its application. We present a maximum likelihood-based classification method that does not depend on prior(More)
General formulation of Shannon's main theorem in information theory, " Usp. van der Meulen, " Density-free convergence properties of various estimators of entropy, " Comput. Statist. Abstract— In the random sampling setting we estimate the entropy of a probability density distribution by the entropy of a kernel density estimator using the double exponential(More)
Almost sure bounds are established on the uniform error of smoothing spline estimators in nonparametric regression with random designs. Some results of Einmahl and Mason (2005) are used to derive uniform error bounds for the approximation of the spline smoother by an " equivalent " reproducing kernel regression estimator, as well as for proving uniform(More)
We present a multiplicative algorithm for image reconstruction, together with a partial convergence proof. The iterative scheme aims to maximize cross Burg entropy between modeled and measured data. Its application to infrared astronomical satellite (IRAS) data shows reduced ringing around point sources, compared to the EM (Richardson-Lucy) algorithm.
The EM algorithm is not a single algorithm, but a framework for the design of iterative likelihood maximization methods for parameter estimation. Any algorithm based on the EM framework we refer to as an " EM algorithm ". Because there is no inclusive theory that applies to all EM algorithms, the subject is a work in progress, and we find it appropriate to(More)
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