A description will be given of a procedure to asslgn the most likely probabilitles to each of the rules of a given context-free grammar. The grammar developed by S. Kuno at Harvard University was picked as the basis and was successfully augmented with rule probabilities. A brief exposition of the method with some preliminary results, w h e n u s e d as a device for disamblguatingparsing English texts picked from natural corpus, will be given.
This book addresses the stochastic modeling of telecommunication networks, introducing the main mathematical tools for that purpose, such as Markov processes, real and spatial point processes and stochastic recursions, and presenting a wide list of results on stability, performances and comparison of systems.
The authors propose a comprehensive mathematical construction of the foundations of stochastic network theory: Markov chains, continuous time Markov chains are extensively studied using an original martingale-based approach.
Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí y học Molecular Biology cung cấp cho các bạn kiến thức về ngành sinh học đề tài: An automated stochastic approach to the identification of the protein specificity determinants and functional subfamilies...
There has been a time when statistical modeling of observation equations was clear in
disciplines like geodesy, geophysics, photogrammetry and practically always based
on the conceptual arsenal of least square theory despite the different physical realities
and laws involved in their respective observations.
This paper describes our work on building Part-of-Speech (POS) tagger for Bengali. We have use Hidden Markov Model (HMM) and Maximum Entropy (ME) based stochastic taggers. Bengali is a morphologically rich language and our taggers make use of morphological and contextual information of the words. Since only a small labeled training set is available (45,000 words), simple stochastic approach does not yield very good results. In this work, we have studied the effect of using a morphological analyzer to improve the performance of the tagger. ...
Artificial Intelligence (AI) is a big field, and this is a big book. We have tried to explore the
full breadth of the field, which encompasses logic, probability, and continuous mathematics;
perception, reasoning, learning, and action; and everything from microelectronic devices to
robotic planetary explorers. The book is also big because we go into some depth.
The subtitle of this book is “A Modern Approach.
This paper provides a review of the methods for measuring portfo-
lio performance and the evidence on the performance of profession-
ally managed investment portfolios. Traditional performance measures,
strongly inﬂuenced by the Capital Asset Pricing Model of Sharpe
(1964), were developed prior to 1990. We discuss some of the prop-
erties and important problems associated with these measures.
The following points support our choice
of methods. First, the specification of the stochastic process is complex due to the variety of
and requires a considerable number of parameters, which
increases the potential impact of estimation errors. Second, the estimation by means of finite
differences requires equally spaced strike prices. For our sample, this is a considerable
difficulty in the implementation, as section 2.2 demonstrates. Third, regarding the
performance of the tree approach, there is little evidence of superior results
The 3 billion base pair sequence of the human genome is now available, and attention is focusing on annotating it to extract biological meaning. I will discuss what we have obtained, and the methods that are being used to analyse biological sequences. In particular I will discuss approaches using stochastic grammars analogous to those used in computational linguistics, both for gene finding and protein family classification.
This paper discusses the supervised learning of morphology using stochastic transducers, trained using the ExpectationMaximization (EM) algorithm. Two approaches are presented: ﬁrst, using the transducers directly to model the process, and secondly using them to deﬁne a similarity measure, related to the Fisher kernel method (Jaakkola and Haussler, 1998), and then using a Memory-Based Learning (MBL) technique. These are evaluated and compared on data sets from English, German, Slovene and Arabic. ...
Concerning different approaches to automatic PoS tagging: EngCG-2, a constraintbased morphological tagger, is compared in a double-blind test with a state-of-the-art statistical tagger on a common disambiguation task using a common tag set. The experiments show that for the same amount of remaining ambiguity, the error rate of the statistical tagger is one order of magnitude greater than that of the rule-based one. The two related issues of priming effects compromising the results and disagreement between human annotators are also addressed. ...
This investigation proposes an approach to modeling the discourse of spoken dialogue using semantic dependency graphs. By characterizing the discourse as a sequence of speech acts, discourse modeling becomes the identification of the speech act sequence. A statistical approach is adopted to model the relations between words in the user’s utterance using the semantic dependency graphs.
We propose a bootstrapping approach to training a memoriless stochastic transducer for the task of extracting transliterations from an English-Arabic bitext. The transducer learns its similarity metric from the data in the bitext, and thus can function directly on strings written in different writing scripts without any additional language knowledge. We show that this bootstrapped transducer performs as well or better than a model designed speciﬁcally to detect Arabic-English transliterations. ...
Stochastic Optimality Theory (Boersma, 1997) is a widely-used model in linguistics that did not have a theoretically sound learning method previously. In this paper, a Markov chain Monte-Carlo method is proposed for learning Stochastic OT Grammars. Following a Bayesian framework, the goal is ﬁnding the posterior distribution of the grammar given the relative frequencies of input-output pairs. The Data Augmentation algorithm allows one to simulate a joint posterior distribution by iterating two conditional sampling steps. ...
We present a new approach to stochastic modeling of constraintbased grammars that is based on loglinear models and uses EM for estimation from unannotated data. The techniques are applied to an LFG grammar for German. Evaluation on an exact match task yields 86% precision for an ambiguity rate of 5.4, and 90% precision on a subcat frame match for an ambiguity rate of 25. Experimental comparison to training from a parsebank shows a 10% gain from EM training.
In some computer applications of linguistics (such as maximum-likelihood decoding of speech or handwriting), the purpose of the language-handling component (Language Model) is to estimate the linguistic (a priori) probability of arbitrary natural-language sentences.
Considered by many authors as a technique for modelling stochastic, dynamic and discretely evolving systems, this technique has gained widespread acceptance among the practitioners who want to represent and improve complex systems. Since DES is a technique applied in incredibly different areas, this book reflects many different points of view about DES, thus, all authors describe how it is understood and applied within their context of work, providing an extensive understanding of what DES is.
In this research monograph, we explain the development of a mechanistic, stochastic
theory of nonfickian solute dispersion in porous media. We have included sufficient
amount of background material related to stochastic calculus and the scale dependency
of diffusivity in this book so that it could be read independently.
When the market is not complete, there is a need to create new securities in order
to complete the market. One approach is to create derivative securities on the existing
securities such as European-type options.
A European call option written on a security gives its holder the right( not obligation)
to buy the underlying security at a prespecied price on a prespecied date; whilst a
European put option written on a security gives its holder the right( not obligation) to
sell the underlying security at a prespecied price on a prespecied date.