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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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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What is a probability space in probability theory?
A probability space in probability theory consists of three components: a sample space, an event space, and a probability measure. The sample space is the set of all possible outcomes of an experiment, the event space is a collection of subsets of the sample space representing different events, and the probability measure assigns a probability to each event in the event space. Together, these components define the mathematical framework for analyzing the likelihood of different outcomes in a probabilistic setting.
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Does market research hinder innovation in business administration?
Market research does not necessarily hinder innovation in business administration. In fact, it can provide valuable insights into consumer needs and preferences, helping businesses to develop innovative products and services that meet market demands. By understanding market trends and customer behavior, businesses can identify opportunities for innovation and stay ahead of competitors. However, relying too heavily on market research without allowing room for creativity and risk-taking can limit the potential for groundbreaking innovations. It is important for businesses to strike a balance between leveraging market research and fostering a culture of innovation to drive success in business administration.
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What are the rules of probability in probability theory?
In probability theory, the rules of probability govern how probabilities are calculated and combined. The rules include the addition rule, which states that the probability of either of two mutually exclusive events occurring is the sum of their individual probabilities. The multiplication rule is used to calculate the probability of two independent events both occurring. Additionally, the complement rule states that the probability of an event not occurring is 1 minus the probability of the event occurring. These rules are fundamental in determining the likelihood of different outcomes in various situations.
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The Science of Conjecture : Evidence and Probability before Pascal
How did we make reliable predictions before Pascal and Fermat's discovery of the mathematics of probability in 1654?What methods in law, science, commerce, philosophy, and logic helped us to get at the truth in cases where certainty was not attainable?In The Science of Conjecture, James Franklin examines how judges, witch inquisitors, and juries evaluated evidence; how scientists weighed reasons for and against scientific theories; and how merchants counted shipwrecks to determine insurance rates.The Science of Conjecture provides a history of rational methods of dealing with uncertainty and explores the coming to consciousness of the human understanding of risk.
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Probability and Statistics for Data Science : Math + R + Data
Probability and Statistics for Data Science: Math + R + Data covers "math stat"—distributions, expected value, estimation etc.—but takes the phrase "Data Science" in the title quite seriously:* Real datasets are used extensively. * All data analysis is supported by R coding. * Includes many Data Science applications, such as PCA, mixture distributions, random graph models, Hidden Markov models, linear and logistic regression, and neural networks. * Leads the student to think critically about the "how" and "why" of statistics, and to "see the big picture."* Not "theorem/proof"-oriented, but concepts and models are stated in a mathematically precise manner. Prerequisites are calculus, some matrix algebra, and some experience in programming. Norman Matloff is a professor of computer science at the University of California, Davis, and was formerly a statistics professor there.He is on the editorial boards of the Journal of Statistical Software and The R Journal.His book Statistical Regression and Classification: From Linear Models to Machine Learning was the recipient of the Ziegel Award for the best book reviewed in Technometrics in 2017.He is a recipient of his university's Distinguished Teaching Award.
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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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How do you correctly calculate probability in probability theory?
In probability theory, the probability of an event occurring is calculated by dividing the number of favorable outcomes by the total number of possible outcomes. This can be represented as P(A) = (Number of favorable outcomes) / (Total number of possible outcomes). It is important to ensure that all possible outcomes are accounted for and that the favorable outcomes are correctly identified. Additionally, the probability of multiple events occurring can be calculated using the multiplication rule for independent events or the addition rule for mutually exclusive events.
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What is the probability in percent in probability theory?
In probability theory, the probability of an event is a measure of the likelihood that the event will occur. It is usually expressed as a number between 0 and 1, or as a percentage between 0% and 100%. A probability of 0% means the event is impossible, while a probability of 100% means the event is certain to occur. The probability of an event can be calculated using various methods, such as counting outcomes, using probability distributions, or applying statistical techniques.
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With what probability?
With what probability? The probability of an event occurring is a measure of how likely it is to happen, expressed as a number between 0 and 1. The probability of an event that is certain to happen is 1, while the probability of an event that is impossible is 0. Probabilities between 0 and 1 indicate the likelihood of an event occurring, with higher probabilities indicating a greater likelihood.
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What is the expected value and probability in probability theory?
In probability theory, the expected value is a measure of the central tendency of a random variable. It represents the average value of a random variable over a large number of trials. The expected value is calculated by multiplying each possible outcome by its probability and then summing up these products. Probability, on the other hand, is a measure of the likelihood of a particular event or outcome occurring. It represents the ratio of the number of favorable outcomes to the total number of possible outcomes. Probability theory is used to analyze and predict the likelihood of different outcomes in various situations.
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