Spatial prediction is commonly achieved under the assumption of a Gaussian random field (GRF) by obtaining maximum likelihood estimates parameters, and then using kriging equations to arrive at predicted values. For massive datasets, fixed rank Expectation-Maximization (EM) algorithm for estimation has been proposed as an alternative usual but computationally prohibitive method. The method redu...

This study aims at comparing simulation-based approaches for estimating both the state and unknown parameters in nonlinear state-space models. Numerical results on different toy models show that combination of a Conditional Particle Filter (CPF) with Backward Simulation (BS) smoother Stochastic Expectation-Maximization (SEM) algorithm is promising approach. The CPFBS run small number particles ...

A well studied procedure for estimating a parameter from observed data is to maximize the likelihood function. When a maximizer cannot be obtained in closed form, iterative maximization algorithms, such as the expectation maximization (EM) maximum likelihood algorithms, are needed. The standard formulation of the EM algorithms postulates that finding a maximizer of the likelihood is complicated...

Much of the current research in learning Bayesian Networks fails to effectively deal with missing data. Most of the methods assume that the data is complete, or make the data complete using fairly ad-hoc methods; other methods do deal with missing data but learn only the conditional probabilities, assuming that the structure is known. We present a principled approach to learn both the Bayesian ...

Much of the current research in learning Bayesian Networks fails to eeectively deal with missing data. Most of the methods assume that the data is complete, or make the data complete using fairly ad-hoc methods; other methods do deal with missing data but learn only the conditional probabilities, assuming that the structure is known. We present a principled approach to learn both the Bayesian n...

In this paper, we present an Expectation-Maximization learning algorithm (E.M.) for estimating parameters of partially-constrained Bayesian trees. The Bayesian trees considered here consist of an unconstrained subtree and a set of constrained subtrees. In this tree structure, constraints are imposed on some of the parameters of the parametrized conditional distributions, such that all condition...

This paper compares three methods | em algorithm, Gibbs sampling, and Bound and Collapse (bc) | to estimate conditional probabilities from incomplete databases in a controlled experiment. Results show a substantial equivalence of the estimates provided by the three methods and a dramatic gain in e ciency using bc. Reprinted from: Proceedings of Uncertainty 99: Seventh International Workshop on ...

In this paper, we consider the problem of joint segmentation and classification of sequences in the framework of conditional random field (CRF) models. To effect this goal, we introduce a novel dual-functionality CRF model: on the first level, the proposed model conducts sequence segmentation, while, on the second level, the whole observed sequences are classified into one of the available lear...

In this paper, we propose a method for intrusion detection in a video surveillance scenario. For this purpose, we train a conditional random field (CRF) on features extracted from a video stream. CRFs estimate a state sequence, given a feature sequence. To detect intrusions, we analyze this state sequence. CRFs are usually trained in a supervised manner. Here, we especially propose a new traini...