Self-diffusion in crystalline silicon is controlled by a network of elementary steps whose activation energies are important to know in a variety of applications in microelectronic fabrication. The present work employs maximum a posteriori (MAP) estimation to improve existing values for these activation energies, based on self-diffusion data collected at different values of the loss rates for i...

The three-dimensional image-reconstruction problem solved here for optical-sectioning microscopy is to estimate the fluorescence intensity lambda(x), where x epsilon R3, given a series of Poisson counting process measurements [Mj(dx)]jJ = 1, each with intensity [formula: see text] with [formula: see text] being the point spread of the optics focused to the jth plane and sj(y) the detection prob...

Technical Report GT-IRIM-CP&R-2015-001 Christian Forster, Luca Carlone, Frank Dellaert, and Davide Scaramuzza May 30, 2015 This report provides additional derivations and implementation details to support the paper [4]. Therefore, it should not be considered a self-contained document, but rather regarded as an appendix of [4], and cited as: “C. Forster, L. Carlone, F. Dellaert, and D. Scaramuzz...

In this paper, we present a Gaussian Markov random field (GMRF) model for the transition matrices (TMs) of Markov chains (MCs) by assuming the existence of a neighborhood relationship between states, and develop the maximum a posteriori (MAP) estimators under different observation conditions. Unlike earlier work on TM estimation, our method can make full use of the similarity between different ...

In this paper the expectation-maximization (EM) algorithm for maximum a posteriori (MAP) estimation of a random vector is applied to the problem of symbol detection for CPM signals transmitted over timeselective Ftayleigh fading channels. This results in a soft-in soft-out (SISO) detection algorithm suitable for iterative detection/decoding schemes. Simulation results show that the error perfor...

Interest rate modeling is a challenging but important problem in financial econometrics. This work is concerned with the parameter estimation of the short term interest models. In light of a recent development in Markov Chain Monte Carlo simulation techniques based on Gibbs sampling, numerical experimentations are carried out for finding an effective and convergent Beyesian estimation scheme. T...

In this paper, we develop a novel modeling scheme for discrete-mixture HMMs (DMHMMs) by using maximum a posteriori (MAP) estimation. Also the MAP estimated DMHMMs are used for speech recognition to improve the accuracy under noisy conditions. The DMHMMs were originally proposed to reduce calculation costs in decoding process [1][2]. We propose a new method for MAP estimation of DMHMM parameters...

This paper describes a real-time method, called PreFEst (Predominant-F0 Estimation method), for estimating the fundamental frequency (F0) of simultaneous sounds in monaural polyphonic audio signals. Without assuming the number of sound sources, PreFEst can estimate the relative dominance of every possible harmonic structure in the input mixture. It treats the mixture as if it contains all possi...

In this paper, we present an information theoretic analysis of the blind signal classification algorithm. We show that the algorithm is equivalent to a Maximum A Posteriori (MAP) estimator based on estimated parametric probability models. We prove a lower bound on the error exponents of the parametric model estimation. It is shown that the estimated model parameters converge in probability to t...

Probabilistic topic models are widely used to discover latent topics in document collections, while latent feature word vectors have been used to obtain high performance in many natural language processing (NLP) tasks. In this paper, we present a new approach by incorporating word vectors to directly optimize the maximum a posteriori (MAP) estimation in a topic model. Preliminary results show t...