This paper presents a sequential state estimation method with arbitrary probabilistic models expressing the system’s belief. Probabilistic models can be estimated by Maximum a posteriori estimators (MAP), which fail, if the state is dynamic or the model contains hidden variables. The last typically requires iterative methods like expectation maximization (EM). The proposed approximative techniq...

In this paper, a general formula of disparity estimation based on Bayesian Maximum A Posteriori (MAP) algorithm is derived and implemented with simplified probabilistic models. The formula is the generalized probabilistic diffusion equation based on Bayesian model, and can be implemented into some different forms corresponding to the probabilistic models in the disparity neighborhood system or ...

( ) Transient enhanced diffusion TED of boron limits the formation of ultrashallow junctions needed in next-generation microelectronic de®ices. A comprehensi®e TED model needs many parameters go®erning the physical and chemical processes. Prior estimates of the most likely ®alues for the parameters as well as their accuracies are determined from maximum likelihood estimation applied to estimate...

We present a theoretical analysis of Maximum a Posteriori (MAP) sequence estimation for binary symmetric hidden Markov processes. We reduce the MAP estimation to the energy minimization of an appropriately defined Ising spin model, and focus on the performance of MAP as characterized by its accuracy and the number of solutions corresponding to a typical observed sequence. It is shown that for a...

Markov chain Monte Carlo (MCMC) methods, while facilitating the solution of many complex problems in Bayesian inference, are not currently well adapted to the problem of marginal maximum a posteriori (MMAP) estimation, especially when the number of parameters is large. We present here a simple and novel MCMC strategy, called State-Augmentation for Marginal Estimation (SAME), which leads to MMAP...

We introduce an approximate search algorithm for fast maximum a posteriori probability estimation in probabilistic programs, which we call Bayesian ascent Monte Carlo (BaMC). Probabilistic programs represent probabilistic models with varying number of mutually dependent finite, countable, and continuous random variables. BaMC is an anytime MAP search algorithm applicable to any combination of r...

The degradation of speech recognition performance in real-life environments and through transmission channels is a main embarrassment for many speech-based applications around the world, especially when non-stationary noise and changing channel exist. In this paper, we extend our previous works on Maximum-Likelihood (ML) dynamic channel compensation by introducing a phone-conditioned prior stat...

We present a new approach for separating two speech signals when only a single recording of their additive mixture is available. In this approach, log spectra of the sources are estimated using maximum a posteriori estimation given the mixture’s log spectrum and the probability density functions of the sources. It is shown that the estimation leads to a two-state, non-linear filter whose states...