Estimating probabilities

User simulations are shown to be useful in spoken dialog system development. Since most current user simulations deploy probability models to mimic human user behaviors, how to set up user action probabilities in these models is a key problem to solve. One generally used approach is to estimate these probabilities from human user data. However, when building a new dialog system, usually no data or only a small amount of data is available.
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This paper compares two different ways of estimating statistical language models. Many statistical NLP tagging and parsing models are estimated by maximizing the (joint) likelihood of the fullyobserved training data. However, since these applications only require the conditional probability distributions, these distributions can in principle be learnt by maximizing the conditional likelihood of the training data.
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In recent years there is much interest in word cooccurrence relations, such as ngrams, verbobject combinations, or cooccurrence within a limited context. This paper discusses how to estimate the probability of cooccurrences that do not occur in the training data. We present a method that makes local analogies between each specific unobserved cooccurrence and other cooccurrences that contain similar words, as determined by an appropriate word similarity metric.
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A cost estimate for a project such as the acquisition of a new aircraft or satellite system carries with it an inherent probability that the actual cost will exceed the estimate
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Instances of a word drawn from different domains may have different sense priors (the proportions of the different senses of a word). This in turn affects the accuracy of word sense disambiguation (WSD) systems trained and applied on different domains. This paper presents a method to estimate the sense priors of words drawn from a new domain, and highlights the importance of using well calibrated probabilities when performing these estimations. By using well calibrated probabilities, we are able to estimate the sense priors effectively to achieve signiﬁcant improvements in WSD accuracy.
8p hongvang_1 16042013 19 1 Download

We present an algorithm for computing ngram probabilities from stochastic contextfree grammars, a procedure that can alleviate some of the standard problems associated with ngrams (estimation from sparse data, lack of linguistic structure, among others). The method operates via the computation of substring expectations, which in turn is accomplished by solving systems of linear equations derived from the grammar. The procedure is fully implemented and has proved viable and useful in practice. confirming its practical feasibility and utility.
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We present a neuralnetworkbased statistical parser, trained and tested on the Penn Treebank. The neural network is used to estimate the parameters of a generative model of leftcorner parsing, and these parameters are used to search for the most probable parse. The parser's performance (88.8% Fmeasure) is within 1% of the best current parsers for this task, despite using a small vocabulary size (512 inputs).
8p bunthai_1 06052013 14 1 Download

An important issue encountered in various branches of science is how to estimate the quantities of interest from a given ﬁnite set of uncertain (noisy) measurements. This is studied in estimation theory, which we shall discuss in this chapter. There exist many estimation techniques developed for various situations; the quantities to be estimated may be nonrandom or have some probability distributions themselves, and they may be constant or timevarying.
28p duongph05 09062010 80 13 Download

The original motivation for writing this book was rather personal. The first author, in the course of his teaching career in the Department of Pure Mathematics and Mathematical Statistics (DPMMS), University of Cambridge, and St John’s College, Cambridge, had many painful experiences when good (or even brilliant) students, who were interested in the subject of mathematics and its applications and who performed well during their first academic year, stumbled or nearly failed in the exams. This led to great frustration, which was very hard to overcome in subsequent undergraduate years.
373p thienbinh1311 13122012 33 12 Download

A very popular approach for estimating the independent component analysis (ICA) model is maximum likelihood (ML) estimation. Maximum likelihood estimation is a fundamental method of statistical estimation; a short introduction was provided in Section 4.5. One interpretation of ML estimation is that we take those parameter values as estimates that give the highest probability for the observations. In this section, we show how to apply ML estimation to ICA estimation.
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Applied statistics for civil and environmental engineers has many contents: Preliminary Data Analysis, Basic Probability Concepts, Random Variables and Their Properties, Model Estimation and Testing, Methods of Regression and Multivariate Analysis, Frequency Analysis of Extreme Events, Simulation Techniques for Design, Risk and Reliability Analysis, Bayesian Decision Methods and Parameter Uncertainty.
737p doremon3244 05062014 35 11 Download

Where the possible values could have significant impact on project’s profitability, a decision will involve taking a risk. In some situations, degree of risk can be objectively determined. Estimating probability of an event usually involves subjectivity.
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An effective budget should enable you to monitor progress at each phase and to identify precisely when and why actual expenses vary from your estimate. Thus, your budget cannot be constructed on an overall project basis; it needs to be broken down by phase. All of the budget elements—labor, fixed expenses, and variable expenses—will vary according to the demands of each phase. Some phases will move along relatively quickly and will require minimal team involvement and little or no expense.
0p tainhacmienphi 25022013 23 6 Download

This volume pulls together and republishes, with some editing, updating, and additions, articles written during 197886 for internal use within the CIA Directorate of Intelligence. Four of the articles also appeared in the Intelligence Community journal Studies in Intelligence during that time frame. The information is relatively timeless and still relevant to the neverending quest for better analysis. The articles are based on reviewing cognitive psychology literature concerning how people process information to make judgments on incomplete and ambiguous information.
206p cronus75 13012013 16 5 Download

Indoor air pollution poses many challenges to the health pro fessional. This booklet offers an overview of those challenges, focusing on acute conditions, with patterns that point to par ticular agents and suggestions for appropriate remedial action. The individual presenting with environmentally associated symptoms is apt to have been exposed to airborne substances originating not outdoors, but indoors. Studies from the United States and Europe show that persons in industrial ized nations spend more than 90 percent of their time indoors1.
0p saimatkhauroi 01022013 16 5 Download

Digital signal processing is currently in a period of rapid growth caused by recent advances inVLSI technology. This is especially true of three areas of optimum signal pro cessing; namely, realtime adaptive signal processing, eigenvector methods of spectrum estimation, and parallel processor implementations of optimum filtering and prediction algorithms. In this edition the book has been brought up to date by increasing the emphasis on the above areas and including several new developments.
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It is difﬁ cult to estimate the number of adult ESOL students in the United States because many are highly mobile and some are undocu mented. According to the National Center for ESL Literacy Education, “The most recent statistics from the U.S. Department of Education, Ofﬁ ce of Vocational and Adult Education, show that 1,119,589 learners were enrolled in federally funded, stateadministered adult ESL classes. This represents 42% of the enrollment in federally funded, stateadministered adult education classes” (Florez, personal communication, 2001).
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This paper describes an extension to the hidden Markov model for partofspeech tagging using secondorder approximations for both contextual and lexical probabilities. This model increases the accuracy of the tagger to state of the art levels. These approximations make use of more contextual information than standard statistical systems. New methods of smoothing the estimated probabilities are also introduced to address the sparse data problem.
8p bunrieu_1 18042013 13 4 Download

Applied statistics and probabilty for engineers_This is an introductory textbook for a first course in applied statistics and probability for undergraduate students in engineering and the physical or chemical sciences. These individuals play a significant role in designing and developing new products and manufacturing systems and processes, and they also improve existing systems. Statistical methods are an important tool in these activities because they provide the engineer with both descriptive and analytical methods for dealing with the variability in observed data.
792p doremon3244 15062014 16 4 Download

Lecture "Advanced Econometrics (Part II)  Chapter 3: Discrete choice analysis  Binary outcome models" presentation of content: Discrete choice model, basic types of discrete values, the probability models, estimation and inference in binary choice model, binary choice models for panel data.
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