Products related to Probability:
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Probability for Information Technology
This book introduces probabilistic modelling and explores its role in solving a broad spectrum of engineering problems that arise in Information Technology (IT).Divided into three parts, it begins by laying the foundation of basic probability concepts such as sample space, events, conditional probability, independence, total probability law and random variables.The second part delves into more advanced topics including random processes and key principles like Maximum A Posteriori (MAP) estimation, the law of large numbers and the central limit theorem.The last part applies these principles to various IT domains like communication, social networks, speech recognition, and machine learning, emphasizing the practical aspect of probability through real-world examples, case studies, and Python coding exercises. A notable feature of this book is its narrative style, seamlessly weaving together probability theories with both classical and contemporary IT applications. Each concept is reinforced with tightly-coupled exercise sets, and the associated fundamentals are explored mostly from first principles.Furthermore, it includes programming implementations of illustrative examples and algorithms, complemented by a brief Python tutorial. Departing from traditional organization, the book adopts a lecture-notes format, presenting interconnected themes and storylines.Primarily tailored for sophomore-level undergraduates, it also suits junior and senior-level courses.While readers benefit from mathematical maturity and programming exposure, supplementary materials and exercise problems aid understanding.Part III serves to inspire and provide insights for students and professionals alike, underscoring the pragmatic relevance of probabilistic concepts in IT.
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Probability Essentials
We have made small changes throughout the book, including the exercises, and we have tried to correct if not all, then at least most of the typos.We wish to thank the many colleagues and students who have commented c- structively on the book since its publication two years ago, and in particular Professors Valentin Petrov, Esko Valkeila, Volker Priebe, and Frank Knight.Jean Jacod, Paris Philip Protter, Ithaca March, 2002 Preface to the Second Printing of the Second Edition We have bene?ted greatly from the long list of typos and small suggestions sent to us by Professor Luis Tenorio.These corrections have improved the book in subtle yet important ways, and the authors are most grateful to him.Jean Jacod, Paris Philip Protter, Ithaca January, 2004 Preface to the First Edition We present here a one semester course on Probability Theory.We also treat measure theory and Lebesgue integration, concentrating on those aspects which are especially germane to the study of Probability Theory.The book is intended to ?ll a current need: there are mathematically sophisticated s- dents and researchers (especially in Engineering, Economics, and Statistics) who need a proper grounding in Probability in order to pursue their primary interests.Many Probability texts available today are celebrations of Pr- ability Theory, containing treatments of fascinating topics to be sure, but nevertheless they make it di?cult to construct a lean one semester course that covers (what we believe) are the essential topics.
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Probability Models
The purpose of this book is to provide a sound introduction to the study of real-world phenomena that possess random variation.It describes how to set up and analyse models of real-life phenomena that involve elements of chance.Motivation comes from everyday experiences of probability, such as that of a dice or cards, the idea of fairness in games of chance, and the random ways in which, say, birthdays are shared or particular events arise. Applications include branching processes, random walks, Markov chains, queues, renewal theory, and Brownian motion.This textbook contains many worked examples and several chapters have been updated and expanded for the second edition.Some mathematical knowledge is assumed. The reader should have the ability to work with unions, intersections and complements of sets; a good facility with calculus, including integration, sequences and series; and appreciation of the logical development of an argument.Probability Modelsis designed to aid students studying probability as part of an undergraduate course on mathematics or mathematics and statistics.
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Inductive Probability
First published in 1961, Inductive Probability is a dialectical analysis of probability as it occurs in inductions.The book elucidates on the various forms of inductive, the criteria for their validity, and the consequent probabilities.This survey is complemented with a critical evaluation of various arguments concerning induction and a consideration of relation between inductive reasoning and logic.The book promises accessibility to even casual readers of philosophy, but it will hold particular interest for students of Philosophy, Mathematics and Logic.
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Are TV and radio stations obligated to broadcast news?
TV and radio stations are not legally obligated to broadcast news, but many choose to do so as part of their commitment to serving the public interest. In some countries, there may be regulations or licensing requirements that mandate a certain amount of news programming, but this varies by jurisdiction. Ultimately, the decision to include news in their programming is up to the individual stations and their management.
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What is the probability of the following events?
I'm happy to help! Could you please provide me with the specific events you would like me to calculate the probability for?
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What are events and how is their probability determined?
Events are specific outcomes or occurrences that can happen in a given situation or experiment. The probability of an event is determined by calculating the ratio of the number of favorable outcomes to the total number of possible outcomes. This can be expressed as a fraction, decimal, or percentage. The probability of an event can range from 0 (impossible) to 1 (certain), with values in between representing the likelihood of the event occurring. Probability can be determined using mathematical formulas, counting techniques, or by conducting experiments and collecting data.
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Why are the two events not disjoint in probability theory?
The two events are not disjoint in probability theory because they have some elements in common. Disjoint events, also known as mutually exclusive events, have no outcomes in common, meaning they cannot occur simultaneously. However, in the case of the two events not being disjoint, there is a possibility of some outcomes being shared between them. This means that the occurrence of one event does not necessarily preclude the occurrence of the other event.
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High-Probability Trading
"The Goal Is to Teach All Traders to Think with the Mindset of a Successful Trader..." While successful trading requires tremendous skill and knowledge, it begins and ends with mindset.What do exceptional traders think when they purchase a quality stock and the price immediately plummets?How do they keep one bad trade from destroying their confidence - and bankroll?What do they know that the rest of us don't? "Some trades are not worth the risk and should never be done."High Probability Trading" shows you how to trade only when the odds are in your favor.From descriptions of the software and equipment an exceptional trader needs high probability signals that either a top or bottom has been reached, it is today's most complete guidebook to thinking like an exceptional trader - every day, on every trade. "It's not how good you are at one individual thing, but it's the culmination of every aspect of trading that makes one successful."Before he became a successful trader, Marcel Link spent years wading from one system to the next, using trial and error to figure out what worked, what didn't, and why. In "High Probability Trading", Link reveals the steps he took to become a consistent, patient, and winning trader - by learning what to watch for, what to watch out for, and what to do to make each trade a high probability trade. "Why do a select few traders repeatedly make money while the masses lose?What do bad traders do that good traders avoid, and what do winning traders do that is different?Throughout this book I will detail how successful traders behave differently and consistently make money by making high probability trades and avoiding common pitfalls..." - From the preface.Within 6 months of beginning their careers full of promise and hope, most traders are literally out of money and out of trading. "High Probability Trading" reduces the likelihood that you will have to pay this "traders' tuition," by detailing a market-proven program for weathering those first few months and becoming a profitable trader from the beginning.Combining a uniquely blunt look at the realities of trading with examples, charts, and case studies detailing actual hits and misses of both short- and long-term traders, this straightforward guidebook discusses: the 10 consistent attributes of a successful trader, and how to make them work for you; strategies for controlling emotions in the heat of trading battle; technical analysis methods for identifying trends, breakouts, reversals, and more; market-tested signals for consistently improving the timing of entry and exit points; how to "trade the news" - and understand when the market has already discounted it; and learning how to get out of a bad trade before it can hurt you. The best traders enter the markets only when the odds are in their favor. "High Probability Trading" shows you how to know the difference between low and high probability situations, and only trade the latter.It goes far beyond simply pointing out the weaknesses and blind spots that hinder most traders to explaining how those defects can be understood, overcome, and turned to each trader's advantage.While it is a cliche, it is also true that there are no bad traders, only bad trades.Let "High Probability Trading" show you how to weed the bad trades from your trading day by helping you see them before they occur.Packed with charts, trading tips, and questions traders should be asking themselves, plus real examples of traders in every market situation, this powerful book will first give you the knowledge and tools you need to tame the markets and then show you how to meld them seamlessly into a customized trading program - one that will help you join the ranks of elite traders and increase your probability of success on every trade.
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Probability : An Introduction
Probability is an area of mathematics of tremendous contemporary importance across all aspects of human endeavour.This book is a compact account of the basic features of probability and random processes at the level of first and second year mathematics undergraduates and Masters' students in cognate fields.It is suitable for a first course in probability, plus a follow-up course in random processes including Markov chains. A special feature is the authors' attention to rigorous mathematics: not everything is rigorous, but the need for rigour is explained at difficult junctures.The text is enriched by simple exercises, together with problems (with very brief hints) many of which are taken from final examinations at Cambridge and Oxford.The first eight chapters form a course in basic probability, being an account of events, random variables, and distributions - discrete and continuous random variables are treated separately - together with simple versions of the law of large numbers and the central limit theorem.There is an account of moment generating functions and their applications.The following three chapters are about branching processes, random walks, and continuous-time random processes such as the Poisson process.The final chapter is a fairly extensive account of Markov chains in discrete time. This second edition develops the success of the first edition through an updated presentation, the extensive new chapter on Markov chains, and a number of new sections to ensure comprehensive coverage of the syllabi at major universities.
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Probability with Martingales
Probability theory is nowadays applied in a huge variety of fields including physics, engineering, biology, economics and the social sciences.This book is a modern, lively and rigorous account which has Doob's theory of martingales in discrete time as its main theme.It proves important results such as Kolmogorov's Strong Law of Large Numbers and the Three-Series Theorem by martingale techniques, and the Central Limit Theorem via the use of characteristic functions.A distinguishing feature is its determination to keep the probability flowing at a nice tempo.It achieves this by being selective rather than encyclopaedic, presenting only what is essential to understand the fundamentals; and it assumes certain key results from measure theory in the main text.These measure-theoretic results are proved in full in appendices, so that the book is completely self-contained.The book is written for students, not for researchers, and has evolved through several years of class testing.Exercises play a vital rôle. Interesting and challenging problems, some with hints, consolidate what has already been learnt, and provide motivation to discover more of the subject than can be covered in a single introduction.
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Probability via Expectation
The third edition of 1992 constituted a major reworking of the original text, and the preface to that edition still represents my position on the issues that stimulated me first to write.The present edition contains a number of minor modifications and corrections, but its principal innovation is the addition of material on dynamic programming, optimal allocation, option pricing and large deviations.These are substantial topics, but ones into which one can gain an insight with less labour than is generally thought.They all involve the expectation concept in an essential fashion, even the treatment of option pricing, which seems initially to forswear expectation in favour of an arbitrage criterion.I am grateful to readers and to Springer-Verlag for their continuing interest in the approach taken in this work.Peter Whittle Preface to the Third Edition This book is a complete revision of the earlier work Probability which appeared in 1970.While revised so radically and incorporatingso much new material as to amount to a new text, it preserves both the aim and the approach of the original.That aim was stated as the provision of a 'first text in probability, demanding a reasonable but not extensive knowledge of mathematics, and taking the reader to what one might describe as a good intermediate level' .In doing so it attempted to break away from stereotyped applications, and consider applications of a more novel and significant character.
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Why are the two events in probability theory not disjoint?
The two events in probability theory are not disjoint because they can have outcomes that overlap or have elements in common. Disjoint events, also known as mutually exclusive events, have no outcomes in common and cannot occur simultaneously. However, in the case of non-disjoint events, there is a possibility of shared outcomes or elements, allowing both events to occur at the same time. This distinction is important in probability theory as it affects the calculation of probabilities and the understanding of the relationship between different events.
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What is the probability of two events occurring in statistics?
In statistics, the probability of two events occurring is calculated using the concept of joint probability. The joint probability of two events A and B occurring is denoted as P(A and B) and is calculated by multiplying the probability of event A (P(A)) by the probability of event B given that event A has occurred (P(B|A)). Mathematically, it can be expressed as P(A and B) = P(A) * P(B|A). This calculation allows us to determine the likelihood of both events happening simultaneously.
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What distinguishes conditional probability from independent probability?
Conditional probability is the probability of an event occurring given that another event has already occurred. It takes into account the information about the occurrence of one event when calculating the probability of another event. Independent probability, on the other hand, is the probability of one event occurring without any influence from the occurrence of another event. In other words, conditional probability is influenced by the occurrence of a specific event, while independent probability is not influenced by any other event.
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Can current events from the news be included in a term paper?
Yes, current events from the news can be included in a term paper to provide relevant and up-to-date information. Incorporating recent events can help to make the paper more engaging and demonstrate the real-world application of the topic being discussed. However, it is important to ensure that the sources are credible and reliable, and to properly cite any information taken from the news.
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