Probability spaces

Xem 1-20 trên 45 kết quả Probability spaces
  • 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

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  • This chapter is of an introductory nature, its purpose being to indicate some concepts and results from the theory of probability which are used in later chapters . Most of these are contained in Chapters 1-9 of Gnedenko [47], and will therefore be cited without proof. The first section is somewhat isolated, and contains a series of results from the foundations of the theory of probability. A detailed account may be found in [76], or in Chapter I of [31] . Some of these will not be needed in the first part of the book, in which attention is confined to independent random variables ....

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  • Statistical machine translation systems are based on one or more translation models and a language model of the target language. While many different translation models and phrase extraction algorithms have been proposed, a standard word n-gram back-off language model is used in most systems. In this work, we propose to use a new statistical language model that is based on a continuous representation of the words in the vocabulary. A neural network is used to perform the projection and the probability estimation. ...

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  • This paper presents noisy-channel based Korean preprocessor system, which corrects word spacing and typographical errors. The proposed algorithm corrects both errors simultaneously. Using Eojeol transition pattern dictionary and statistical data such as Eumjeol n-gram and Jaso transition probabilities, the algorithm minimizes the usage of huge word dictionaries.

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  • 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.

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  • In this chapter, you will learn to: Explain the terms random experiment, outcome, sample space, permutations, and combinations; define probability; describe the classical, empirical, and subjective approaches to probability; explain and calculate conditional probability and joint probability;...

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  • The origins of this book lie in our earlier book Random Processes: A Mathematical Approach for Engineers, Prentice Hall, 1986. This book began as a second edition to the earlier book and the basic goal remains unchanged | to introduce the fundamental ideas and mechanics of random processes to engineers in a way that accurately reects the underlying mathematics, but does not require an extensive mathematical background and does not belabor detailed general proofs when simple cases suce to get the basic ideas across....

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  • In this chapter, we study the mathematical structure of a simple one-period model of a financial market. We consider a finite number of assets. Their initial prices at time t = 0 are known, their future prices at time t = 1 are described as random variables on some probability space. Trading takes place at time t = 0. Already in this simple model, some basic principles of mathematical finance appear very clearly. In Section 1.2, we single out those models which satisfy a condition of market efficiency: There are no trading opportunities which yield a profit without any downside risk.

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  • Stochastic Calculus of Variations (or Malliavin Calculus) consists, in brief, in constructing and exploiting natural differentiable structures on abstract probability spaces; in other words, Stochastic Calculus of Variations proceeds from a merging of differential calculus and probability theory. As optimization under a random environment is at the heart of mathematical finance, and as differential calculus is of paramount importance for the search of extrema, it is not surprising that Stochastic Calculus of Variations appears in mathematical finance.

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  • In the following pages I have confined myself in the main to those problems of philosophy in regard to which I thought it possible to say something positive and constructive, since merely negative criticism seemed out of place. For this reason, theory of knowledge occupies a larger space than metaphysics in the present volume, and some topics much discussed by philosophers are treated very briefly, if at all. I have derived valuable assistance from unpublished writings of G. E. Moore and J. M.

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  • Chapter 4 Conditional Probability 4.1 Discrete Conditional Probability In this section we ask and answer the following question. Suppose we assign a distribution function to a sample space and then learn that an event E has occurred. How should we change the probabilities of the remaining events?

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  • Chapter 12 Random Walks 12.1 Random Walks in Euclidean Space In the last several chapters, we have studied sums of random variables with the goal being to describe the distribution and density functions of the sum. In this chapter, we shall look at sums of discrete random variables from a different perspective.

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  • The first report of a quantitative risk evaluation applied to health goes back to Laplace, in the late eighteenth century, which calculated the probability of death among people with and without vaccination for smallpox. With Pasteur's studies in the late nineteenth century, it was possible to use the tools of statistics to evaluate the factors related to communicable diseases, giving birth to the concept of epidemiological risk (Covello; Munpower, 1985, Czeresnia, 2004).

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  • To introduce the ]orward-backward stochastic differential equations (FBS- DEs, for short), let us begin with some examples. Unless otherwise speci- fled, throughout the book, we let (~, •, {Ft)t_0, P) be a complete filtered probability space on which is defined a d-dimensional standard Brownian motion W(t), such that {5~t }t_0 is the natural filtration of W(t), augmented by...

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  • Astronomy is certainly the oldest science and that of astronomer probably the oldest profession. This second assertion is notoriously debatable, but one can safely assume that in a primitive civilized society the (remunerated) shaman or priest had to be an astronomer to be credible.

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  • I’ve been a diehard baseball fan since I was old enough to turn on the TV—which was a little harder back then since there were no remotes. There was also no fantasy baseball, which meant I grew up watching games and reading box scores just for the fun of it. That’s probably why, truth be told, I never had much use for the fantasy game. I had always regarded myself as something of a purist, and I fi gured fantasy baseball served mainly to muddle the traditional concept of simply being a fan of your team and its players....

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  • Lecture Quantiative methods for bussiness - Chapter 2 introduction to probability. This chapter presents the following content: Experiments and the sample space; assigning probabilities to experimental outcomes; events and their probabilities; some basic relationships of probability; Bayes’ theorem.

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  • Variable stars are those that change brightness. Their variability may be due to geometric processes such as rotation, or eclipse by a companion star, or physical processes such as vibration, flares, or cataclysmic explosions. In each case, variable stars provide unique information about the properties of stars, and the processes that go on within them.

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  • Chapter 4 - Probability. After completing this unit, you should be able to: Define a probability and a sample space, list the outcomes in a sample space and use the list to compute probabilities, use elementary probability rules to compute probabilities, compute conditional probabilities and assess independence,...

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  • Kinds of Learning (Q&A) inductive learning and the acquisition of new knowledge, Come up with some function, Inductive Bias definition, Occam’s Razor, Probably Approximately Correct (PAC) Learning, Version Space.

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