We show how the benefits of the pathwise sensitivity approach to computing Monte Carlo Greeks can be extended to discontinuous payoff functions through a combination of the pathwise approach and the Likelihood Ratio Method. With a variance reduction modification, this results in an estimator which for timestep h has a variance which is O(h−1/2) for discontinuous payoffs and O(1) for continuous ...

This paper investigates a projection-based likelihood meaure that improves speech recognition performance in noisy environment. The projection-based likelihood measure is modi ed to give the weighting and projection e ect and to reduce computational complexity. It is evaluated in sub-model based word recognition using semi-continuous hidden Markov model with speaker independent mode. Experiment...

This paper investigates whether codependence restrictions can be uniquely imposed on VAR and VEC models via the so-called pseudo-structural form used in the literature. Codependence of order q is given if a linear combination of autocorrelated variables eliminates the serial correlation after q lags. Importantly, maximum likelihood estimation and likelihood ratio testing are only possible if th...

two different methods of bayesian segmentation algorithm were used with different band combinations. sequential maximum a posteriori (smap) is a bayesian image segmentation algorithm which unlike the traditional maximum likelihood (ml) classification attempts to improve accuracy by taking contextual information into account, rather than classifying pixels separately. landsat 7 etm+ data with pa...

We propose the method for selecting classification boundaries for the logistic regression model, by applying the sparse regularization. We can investigate which combination of classification boundaries are truly necessary for the multinomial logistic model by encouraging some of coefficient parameters themselves or their differences toward exactly zeros. The model is estimated by the maximum pe...

We suggest a model in which theories are ranked given various databases. Certain axioms on such rankings imply a numerical representation that is the sum of the log-likelihood of the theory and a fixed number for each theory, which may be interpreted as a measure of its complexity. This additive combination of log-likelihood and a measure of complexity generalizes both the Akaike Information Cr...

This paper presents a novel approach for inducing lexical taxonomies automatically from text. We recast the learning problem as that of inferring a hierarchy from a graph whose nodes represent taxonomic terms and edges their degree of relatedness. Our model takes this graph representation as input and fits a taxonomy to it via combination of a maximum likelihood approach with a Monte Carlo Samp...

This paper presents a detection algorithm for pacemakers which is based on a signal model including a linear combination of descriptive functions. The functions are defined as different time scales of the two fundamental waveforms in the electrogram. An efficient detector structure is provided by the use of a dyadic wavelet transform with integer filter coefficients, followed by a generalized l...

In this paper we discuss identification of codependent VAR and VEC models. Codependence of order q is given if a linear combination of autocorrelated variables eliminates the serial correlation after q lags. Importantly, maximum likelihood estimation and corresponding likelihood ratio testing are only possible if the codependence restrictions can be uniquely imposed. However, our study reveals ...

We propose an approximate maximum likelihood parameter estimation algorithm, combined with a model order estimator, for superimposed undamped exponentials in noise. The algorithm combines the robustness of Fourier-based estimators and the high-resolution capabilities of parametric methods. We use a combination of a Wald statistic and a MAP test for order selection and initialize an iterative ma...