The difficulty in inverse reinforcement learning (IRL) arises in choosing the best reward function since there are typically an infinite number of reward functions that yield the given behaviour data as optimal. Using a Bayesian framework, we address this challenge by using the maximum a posteriori (MAP) estimation for the reward function, and show that most of the previous IRL algorithms can b...

This paper presents a novel recursive maximum a posteriori update for the Kalman formulation of undelayed bearing-only SLAM. The estimation update step is cast as an optimization problem for which we can prove the global minimum is reachable via a bidirectional search using Gauss-Newton’s method along a one-dimensional manifold. While the filter is designed for mapping just one landmark, it is ...

The authors present a Markov random field model which allows realistic edge modeling while providing stable maximum a posterior (MAP) solutions. The model, referred to as a generalized Gaussian Markov random field (GGMRF), is named for its similarity to the generalized Gaussian distribution used in robust detection and estimation. The model satisfies several desirable analytical and computation...

The paper deals with the still-to-video face recognition for the small sample size problem based on computation of distances between high-dimensional deep bottleneck features. We present the novel statistical recognition method, in which the still-to-video recognition task is casted into Maximum A Posteriori estimation. In this method we maximize the joint probabilistic density of the distances...

We are interested in restoring a degraded scene while preserving the edges. Edges are represented as line pr& cesses and are estimated along with the intensities in a Maximum A Posien'ori (MAP) framework. Aasumptions regarding the prior and degradation distributions reduce the problem to one of energy function minimization. The energy function is highly non-convex and finding the global minimum...

This paper deals with the problem of reconstructing a highresolution image from an incomplete set of undersampled, blurred and noisy images shifted with subpixel displacement. We derive mathematical expressions for the calculation of the maximum a posteriori estimate of the high resolution image and the estimation of the parameters involved in the model. We also examine the role played by the p...

We consider an inventory competition game between two firms. The question we address is this: If players do not know the opponent’s action and opponent’s utility function can they learn to play the Nash policy in a repeated game by observing their own sales? In this work it is proven that by means of Maximum A Posteriori (MAP) estimation, players can learn the Nash policy. It is proven that pla...

The possibilities of an a posteriori synchronization of nonsynchronized data records are investigated for the purpose of signal enhancement of a periodic signal. The theoretical background for an effective frequency domain method is given, and the errors of the averaged complex amplitudes are calculated. The time delay between records is identified via maximum likelihood estimation, which is sl...

This letter compares pilot-aided phase-shift keyed (PSK) and differential PSK (DPSK) modulation when using new maximum a posteriori joint channel estimation and equalization receivers with frequency-selective fast-fading channels. We conclude that pilot-aided PSK has superior bit-error rate performance in this case. However, at low signal-to-noise ratio, performance is similar, and DPSK is comp...

We describe Allomorfessor, which extends the unsupervised morpheme segmentation method Morfessor to account for the linguistic phenomenon of allomorphy, where one morpheme has several different surface forms. The method discovers common base forms for allomorphs from an unannotated corpus by finding small modifications, called mutations, for them. Using Maximum a Posteriori estimation, the mode...