Probability models

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.
18p nghe123 06052016 20 4 Download

Word alignment plays a crucial role in statistical machine translation. Wordaligned corpora have been found to be an excellent source of translationrelated knowledge. We present a statistical model for computing the probability of an alignment given a sentence pair. This model allows easy integration of contextspeciﬁc features. Our experiments show that this model can be an effective tool for improving an existing word alignment.
8p bunbo_1 17042013 15 1 Download

(BQ) Part 2 book "Fundamentals of algebraic modeling  An introduction to mathematical modeling with algebra and statistics" has contents: Additional applications of algebraic modeling, modeling with systems of equations, probability models, modeling with statistics.
220p bautroibinhyen23 02042017 2 1 Download

This paper proposes a novel method for learning probability models of subcategorization preference of verbs. We consider the issues of case dependencies and noun class generalization in a uniform way by employing the maximum entropy modeling method. We also propose a new model selection algorithm which starts from the most general model and gradually examines more specific models.
7p bunrieu_1 18042013 24 5 Download

Tuyển tập các báo cáo nghiên cứu về lâm nghiệp được đăng trên tạp chí lâm nghiệp Original article đề tài: A ﬁre probability model for forest stands in Catalonia (north east Spain)...
1p toshiba5 15092011 21 2 Download

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.
9p hongphan_1 14042013 24 2 Download

Language models for speech recognition typically use a probability model of the form Pr(an[al,a2,...,ani). Stochastic grammars, on the other hand, are typically used to assign structure to utterances, A language model of the above form is constructed from such grammars by computing the prefix probability ~we~* Pr(al..artw), where w represents all possible terminations of the prefix al...an. The main result in this paper is an algorithm to compute such prefix probabilities given a stochastic Tree Adjoining Grammar (TAG). The algorithm achieves the required computation in O(n 6) time. ...
7p bunrieu_1 18042013 22 2 Download

(BQ) Part 1 book "Business statistics" has contents: Statistics and variation, surveys and sampling, displaying and describing categorical data, displaying and describing quantitative data, correlation and linear regression, randomness and probability, random variables and probability models,...and other contents.
479p bautroibinhyen23 02042017 5 2 Download

Tuyển tập các báo cáo nghiên cứu về lâm nghiệp được đăng trên tạp chí lâm nghiệp Original article đề tài:"A fire probability model for forest stands in Catalonia (northeast Spain)"
8p toshiba5 19092011 26 1 Download

This paper compares a number of generative probability models for a widecoverage Combinatory Categorial Grammar (CCG) parser. These models are trained and tested on a corpus obtained by translating the Penn Treebank trees into CCG normalform derivations. According to an evaluation of unlabeled wordword dependencies, our best model achieves a performance of 89.9%, comparable to the ﬁgures given by Collins (1999) for a linguistically less expressive grammar. In contrast to Gildea (2001), we ﬁnd a significant improvement from modeling wordword dependencies. ...
8p bunmoc_1 20042013 15 1 Download

Chapter 4: Bayes Classifier present of you about The naïve Bayes Probabilistic model, Constructing a Classifier from the probability model, An application of Naïve Bayes Classifier, Bayesian network.
27p cocacola_10 08122015 13 1 Download

This book will teach you how to bring together what you know of finance, accounting, and the spreadsheet to give you a new skill—building financial models. The ability to create and unde stand models is one of the most valued skills in business an finance today. It’s an expertise that will stand you in good stea in any arena—Wall Street or Main Street—where numbers ar important. Whether you are a veteran, just starting out on you career, or still in school, having this expertise can give you competitive advantage in what you want to do....
353p batoan 15072009 753 610 Download

This book is about foundational issues in risk and risk analysis; how risk should be expressed; what the meaning of risk is; how to understand and use models; how to understand and address uncertainty; and how parametric probability models like the Poisson model should be understood and used. A unifying and holistic approach to risk and uncertainty is presented, for different applications and disciplines.
198p kimngan_1 06112012 39 16 Download

Air pollution has always been a transboundary environmental problem and a matter of global concern for past many years. High concentrations of air pollutants due to numerous anthropogenic activities influence the air quality. There are many books on this subject, but the one in front of you will probably help in filling the gaps existing in the area of air quality monitoring, modelling, exposure, health and control, and can be of great help to graduate students professionals and researchers.
0p qsczaxewd 22092012 46 6 Download

Tham khảo sách '.probability for financepatrick roger strasbourg university, em strasbourg business school may', tài chính  ngân hàng, tài chính doanh nghiệp phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả
115p tuanloc_do 04122012 26 4 Download

This paper describes an unsupervised dynamic graphical model for morphological segmentation and bilingual morpheme alignment for statistical machine translation. The model extends Hidden SemiMarkov chain models by using factored output nodes and special structures for its conditional probability distributions. It relies on morphosyntactic and lexical sourceside information (partofspeech, morphological segmentation) while learning a morpheme segmentation over the target language. Our model outperforms a competitive word alignment system in alignment quality. ...
10p hongdo_1 12042013 18 4 Download

This paper discusses a decisiontree approach to the problem of assigning probabilities to words following a given text. In contrast with previous decisiontree language model attempts, an algorithm for selecting nearly optimal questions is considered. The model is to be tested on a standard task, The Wall Street Journal, allowing a fair comparison with the wellknown trigram model.
4p bunrieu_1 18042013 31 4 Download

This paper presents an algorithm for learning the probabilities of optional phonological rules from corpora. The algorithm is based on using a speech recognition system to discover the surface pronunciations of words in spe.ech corpora; using an automatic system obviates expensive phonetic labeling by hand. We describe the details of our algorithm and show the probabilities the system has learned for ten common phonological rules which model reductions and coarticulation effects.
8p bunmoc_1 20042013 29 4 Download

In the quest for knowledge, it is not uncommon for researchers to push the limits of simulation techniques to the point where they have to be adapted or totally new techniques or approaches become necessary. True multiscale modeling techniques are becoming increasingly necessary given the growing interest in materials and processes on which largescale properties are dependent or that can be tuned by their lowscale properties. An example would be nanocomposites, where embedded nanostructures completely change the matrix properties due to effects occurring at the atomic level.
0p thienbinh1311 13122012 18 3 Download

The language model (LM) is a critical component in most statistical machine translation (SMT) systems, serving to establish a probability distribution over the hypothesis space. Most SMT systems use a static LM, independent of the source language input. While previous work has shown that adapting LMs based on the input improves SMT performance, none of the techniques has thus far been shown to be feasible for online systems.
5p hongdo_1 12042013 17 3 Download