Topic
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.
Papers published on a yearly basis
Papers
More filters
TL;DR: In this article, a simulated annealing algorithm involving a sequence of spatial birth-and-death processes is developed and shown to converge in total variation to a uniform distribution on the set of posterior mode solutions.
Abstract: We study convergence in total variation of non-stationary Markov chains in continuous time and apply the results to the image analysis problem of object recognition. The input is a grey-scale or binary image and the desired output is a graphical pattern in continuous space, such as a list of geometric objects or a line drawing. The natural prior models are Markov point processes found in stochastic geometry. We construct well-defined spatial birth-and-death processes that converge weakly to the posterior distribution. A simulated annealing algorithm involving a sequence of spatial birth-and-death processes is developed and shown to converge in total variation to a uniform distribution on the set of posterior mode solutions. The method is demonstrated on a tame example.
56 citations
TL;DR: This paper provides a counterexample which shows that in general this claim that maximum a posteriori estimators are a limiting case of Bayes estimators with 0–1 loss is false and corrects that by providing a level-set condition for posterior densities such that the result holds.
Abstract: Maximum a posteriori and Bayes estimators are two common methods of point estimation in Bayesian statistics. It is commonly accepted that maximum a posteriori estimators are a limiting case of Bayes estimators with 0–1 loss. In this paper, we provide a counterexample which shows that in general this claim is false. We then correct the claim that by providing a level-set condition for posterior densities such that the result holds. Since both estimators are defined in terms of optimization problems, the tools of variational analysis find a natural application to Bayesian point estimation.
56 citations
TL;DR: In this article, the effect of a priori model on the performance of the algorithm at different noise levels in the measured data was analyzed and the results showed that the mean and maximum a posteriori estimates for thermal conductivity and the convection heat transfer coefficient were insensitive to the a priora model at all the considered noise levels for the single-parameter estimation problem.
56 citations
TL;DR: It is proposed that texture segmentation is a part of the early visual system's overall strategy to infer surfaces of objects in a visual scene and the Bayesian inference paradigm is used to formulate the texture segmentations problem into a maximum a posteriori surface inference problem.
56 citations
26 Sep 2004
TL;DR: In this paper, a maximum a posteriori (MAP) model was used for segmentation and registration of brain MR images, and an additional hidden Markov random vector field was incorporated into the model to solve both rigid and non-rigid registration.
Abstract: Although segmentation and registration are usually considered separately in medical image analysis, they can obviously benefit a great deal from each other In this paper, we propose a novel scheme of simultaneously solving for segmentation and registration This is achieved by a maximum a posteriori (MAP) model The key idea is to introduce an additional hidden Markov random vector field into the model Both rigid and non-rigid registration have been incorporated We have used a B-spline based free-form deformation for non-rigid registration case The method has been applied to the segmentation and registration of brain MR images
56 citations