Maximum a posteriori estimation

About: Maximum a posteriori estimation is a research topic. Over the lifetime, 7486 publications have been published within this topic receiving 222291 citations. The topic is also known as: Maximum a posteriori, MAP & maximum a posteriori probability.

Multiple resolution Bayesian segmentation of ultrasound images

Abstract: We propose a novel method for obtaining the maximum a posteriori (MAP) probabilistic segmentation of speckle-laden ultrasound images. Our technique is multiple-resolution based, and relies on the conversion of speckle images with Rayleigh statistics to subsampled images with Gaussian statistics. This conversion reduces computation time, as well as allowing accurate parameter estimation. Results appear to provide improvements over previous techniques, in terms of both low-resolution detail and accuracy.© (1994) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

On Sampling from the Gibbs Distribution with Random Maximum A-Posteriori Perturbations

Abstract: In this paper we describe how MAP inference can be used to sample efficiently from Gibbs distributions. Specifically, we provide means for drawing either approximate or unbiased samples from Gibbs' distributions by introducing low dimensional perturbations and solving the corresponding MAP assignments. Our approach also leads to new ways to derive lower bounds on partition functions. We demonstrate empirically that our method excels in the typical "high signal - high coupling" regime. The setting results in ragged energy landscapes that are challenging for alternative approaches to sampling and/or lower bounds.

Maximum Likelihood Approach

Abstract: In voting, the joint decision is made based on the agents' preferences. Therefore, in some sense, this means that the agents' preferences are the " causes " of the joint decision. However, there is a different (and almost reversed) point of view: there is a " correct " joint decision, but the agents may have different perceptions (estimates) of what this correct decision is. Thus, the agents' preferences can be viewed as noisy reports on the correct joint decision. Even in this framework, the agents still need to make a joint decision based on their preferences, and it makes sense to choose their best estimate of the correct decision. Given a noise model, one natural approach is to choose the maximum likelihood estimate of the correct decision. The maximum likelihood estimator is a function from profiles to alternatives (more accurately, subsets of alternatives, since there may be ties), and as such is a voting rule (more accurately, a correspondence). This maximum likelihood approach was first studied by Condorcet (1785) for the cases of two and three alternatives. Much later, Young (1995) and Young (1988) showed that for arbitrary numbers of alternatives, the MLE rule derived from Con-dorcet's noise model coincides with Kemeny's rule (Kemeny, 1959). The approach 182

A block-based MAP segmentation for image compressions

Abstract: A novel block-based image segmentation algorithm using the maximum a posteriori (MAP) criterion is proposed. The conditional probability in the MAP criterion, which is formulated by the Bayesian framework, is in charge of classifying image blocks into edge, monotone, and textured blocks. On the other hand, the a priori probability is responsible for edge connectivity and homogeneous region continuity. After a few iterations to achieve a deterministic MAP optimization, we can obtain a block-based segmented image in terms of edge, monotone, or textured blocks. Then, using a connected block-labeling algorithm, we can assign a number to all connected homogeneous blocks to define an interior of a region. Finally, uncertainty blocks, which are not given any region number yet, are assigned to one of the neighboring homogeneous regions by a block-based region-growing method. During this process, we can also check the balance between the accuracy and the cost of the contour coding by adjusting the size of the uncertainty blocks. Experimental results show that the proposed algorithm yields larger homogeneous regions which are suitable for the object-based image compression.

Discriminative map for acoustic model adaptation

Abstract: In this paper we show how a discriminative objective function such as Maximum Mutual Information (MMI) can be combined with a prior distribution over the HMM parameters to give a discriminative Maximum A Posteriori (MAP) estimate for HMM training. The prior distribution can be based around the Maximum Likelihood (ML) parameter estimates, leading to a technique previously referred to as I-smoothing; or for adaptation it can be based around a MAP estimate of the ML parameters, leading to what we call MMI-MAP. This latter approach is shown to be effective for task adaptation, where data from one task (Voicemail) is used to adapt a HMM set trained on another task (Switchboard). It is shown that MMI-MAP results in a 2.1% absolute reduction in word error rate relative to standard ML-MAP with 30 hours of Voicemail task adaptation data starting from a MMI-trained Switchboard system.