We consider a Bayesian approach to multiple hypothesis testing. A hierarchical prior model is based on imposing a prior distribution π(k) on the number of hypotheses arising from alternatives (false nulls). We then apply the maximum a posteriori (MAP) rule to find the most likely configuration of null and alternative hypotheses. The resulting MAP procedure and its closely related step-up and st...

This paper presents a promising super-resolution (SR) approach using maximum a posteriori (MAP) estimation. We consider the high resolution (HR) estimation as a Markov Random Field (MRF), using a transformed gradient field prior to repair the image fuzzy problem caused by MRF. An improved Normalized Convolution method is proposed to obtain a first good estimation. We build a reasonable energy f...

Noise power spectral density (PSD) estimation is a crucial part of speech enhancement system due to its contributory effect on the quality of the noise reduced speech. A novel estimation method for color noise PSD on the basis of an assumption of generalized Gamma distribution and maximum a posteriori (MAP) criterion is proposed. In the experiment, generalized Gamma PDF which is a natural exten...

An integrated approach to image segmentation is presented that combines region and boundary information using maximum a posteriori estimation and decision theory. The algorithm employs iterative, decision-directed estimation performed on a novel multiresolution representation. The use of a multiresolution technique ensures both robustness in noise and efficiency of computation, while the model-...

Demodulation of received signals for a known channel parameters over Rayleigh fading channel is done by BEM (Bayesian Estimation Maximization) algorithm using MAP (Maximum A Posteriori) Probability decisions. Simulation results were produced using this demodulator for the specified mobile satellite based trans-receiver system to find the BER (Bit Error Rate). Comparisons are made to that of a Q...

In order to overcome the limitation of the traditional adaptive Unscented Kalman Filtering (UKF) algorithm in noise covariance estimation for statement and measurement, we propose a hybrid adaptive UKF algorithm based on combining Maximum a posteriori (MAP) criterion and Maximum likelihood (ML) criterion, in this paper. First, to prevent the actual noise covariance deviating from the true value...

In this paper, we present a Stationary Wavelet Transform (SWT) based method for the purpose of despeckling the Synthetic Aperture radar (SAR) images by applying a maximum a posteriori probability (MAP) condition to estimate the noise free wavelet coefficients. The solution of the MAP estimator is based on the assumption that the wavelet coefficients have a known distribution. Rayleigh distribut...

The two-alternative forced-choice (2AFC) task is the workhorse of psychophysics and is used to measure the just-noticeable difference, generally assumed to accurately quantify sensory precision. However, this assumption is not true for all mechanisms of decision making. Here we derive the behavioral predictions for two popular mechanisms, sampling and maximum a posteriori, and examine how they ...

Here, we present a novel approach to solve the problem of reconstructing perceived stimuli from brain responses by combining probabilistic inference with deep learning. Our approach first inverts the linear transformation from latent features to brain responses with maximum a posteriori estimation and then inverts the nonlinear transformation from perceived stimuli to latent features with adver...