Nonparametric models

Xem 1-20 trên 21 kết quả Nonparametric models
  • One of the key tasks for analyzing conversational data is segmenting it into coherent topic segments. However, most models of topic segmentation ignore the social aspect of conversations, focusing only on the words used. We introduce a hierarchical Bayesian nonparametric model, Speaker Identity for Topic Segmentation (SITS), that discovers (1) the topics used in a conversation, (2) how these topics are shared across conversations, (3) when these topics shift, and (4) a person-specific tendency to introduce new topics. ...

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  • (BQ) Part 2 book "Mathematical statistics" has contents: Estimation in nonparametric models, hypothesis tests, confidence sets, distribution estimators, statistical functionals, variance estimation.

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  • Machine transliteration is defined as automatic phonetic translation of names across languages. In this paper, we propose synchronous adaptor grammar, a novel nonparametric Bayesian learning approach, for machine transliteration. This model provides a general framework without heuristic or restriction to automatically learn syllable equivalents between languages.

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  • We present an unsupervised model for joint phrase alignment and extraction using nonparametric Bayesian methods and inversion transduction grammars (ITGs). The key contribution is that phrases of many granularities are included directly in the model through the use of a novel formulation that memorizes phrases generated not only by terminal, but also non-terminal symbols. This allows for a completely probabilistic model that is able to create a phrase table that achieves competitive accuracy on phrase-based machine translation tasks directly from unaligned sentence pairs. ...

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  • We investigate the problem of acoustic modeling in which prior language-specific knowledge and transcribed data are unavailable. We present an unsupervised model that simultaneously segments the speech, discovers a proper set of sub-word units (e.g., phones) and learns a Hidden Markov Model (HMM) for each induced acoustic unit.

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  • We present an unsupervised, nonparametric Bayesian approach to coreference resolution which models both global entity identity across a corpus as well as the sequential anaphoric structure within each document. While most existing coreference work is driven by pairwise decisions, our model is fully generative, producing each mention from a combination of global entity properties and local attentional state. Despite being unsupervised, our system achieves a 70.3 MUC F1 measure on the MUC-6 test set, broadly in the range of some recent supervised results. ...

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  • Since the publication of my book Mathematical Statistics (Shao, 2003), I have been asked many times for a solution manual to the exercises in my book. Without doubt, exercises form an important part of a textbook on mathematical statistics, not only in training students for their research ability in mathematical statistics but also in presenting many additional results as complementary material to the main text.

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  • We present a Bayesian nonparametric model for estimating tree insertion grammars (TIG), building upon recent work in Bayesian inference of tree substitution grammars (TSG) via Dirichlet processes. Under our general variant of TIG, grammars are estimated via the Metropolis-Hastings algorithm that uses a context free grammar transformation as a proposal, which allows for cubic-time string parsing as well as tree-wide joint sampling of derivations in the spirit of Cohn and Blunsom (2010).

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  • Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí sinh học quốc tế đề tài: Genomic breeding value estimation using nonparametric additive regression models

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  • (BQ) Part 2 book "Mathematical statistics with applications" has contents: Linear regression models, design of experiments, analysis of variance, bayesian estimation and inference, nonparametric tests, empirical methods, some issues in statistical applications - an overview.

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  • (BQ) Part 2 book "Statistics" has contents: Inferences based on a two samples - Confidence intervals and tests of hypotheses; analysis of variance - Comparing more than two means; simple linear regression; multiple regression and model building; categorical data analysis; nonparametric statistics.

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  • Stable laws { also called -stable or Levy-stable { are a rich family of probability distributions that allow skewness and heavy tails and have many interesting mathematical properties. They appear in the context of the Generalized Central Limit Theorem which states that the only possible non-trivial limit of normalized sums of independent identically distributed variables is -stable. The Standard Central Limit Theorem states that the limit of normalized sums of independent identically distributed terms with nite variance is Gaussian ( -stable with = 2).

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  • Important Notions and Definitions Random Processes • Spectra of Deterministic Signals • Spectra of Random Processes 14.3 The Problem of Power Spectrum Estimation 14.

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  • A common approach to adjusting for seasonal and long-term trends is to use semiparametric models which incorporate a smooth function of time. The use of nonparametric smoothing in time series models of air pollution and health was suggested in Schwartz (1994a), where generalized additive Poisson models were used with LOESS smooths of time, temperature, dewpoint temperature and PM10. This approach can be thought of as regressing residuals from the smoothed dependent variable on residuals from the smoothed regressors.

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  • Chapter 38 APPLIED WOLFGANG NONPARAMETRIC Although economic theory generally provides only loose restrictions on the distribution of observable quantities, much econometric work is based on tightly specified parametric models and likelihood based methods of inference.

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  • Chapter 48 ASPECTS OF MODELLING TIM0 TERASVIRTA NONLINEAR TIME SERIES* Contents Abstract 1. Introduction 2. Types of nonlinear models theory models parameter and long-memory model Models from time series theory Flexible statistical State-dependent, Nonparametric parametric time-varying models

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  • The emergence of social media brings chances, but also challenges, to linguistic analysis. In this paper we investigate a novel problem of discovering patterns based on emotion and the association of moods and affective lexicon usage in blogosphere, a representative for social media. We propose the use of normative emotional scores for English words in combination with a psychological model of emotion measurement and a nonparametric clustering process for inferring meaningful emotion patterns automatically from data. ...

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  • Chapter 41 ESTIMATION MODELS Semiparametric modelling is, as its name suggests, a hybrid of the parametric and nonparametric approaches to construction, fitting, and validation of statistical models. To place semiparametric methods in context, it is useful to review the way these other approaches are used to address a generic microeconometric

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  • Chapter 42 RESTRICTIONS OF ECONOMIC NONPARAMETRIC METHODS Increasingly, it appears that restrictions implied by economic theory provide extremely useful tools for developing nonparametric estimation and testing methods. Unlike parametric methods, in which the functions and distributions in a model are specified up to a finite dimensional vector

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  • This paper examines how a new class of nonparametric Bayesian models can be effectively applied to an open-domain event coreference task. Designed with the purpose of clustering complex linguistic objects, these models consider a potentially infinite number of features and categorical outcomes. The evaluation performed for solving both within- and cross-document event coreference shows significant improvements of the models when compared against two baselines for this task.

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