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Prophet Inequality
In the theory of online algorithms and optimal stopping, a prophet inequality is a bound on the expected value of a decision-making process that handles a sequence of random inputs from known probability distributions, relative to the expected value that could be achieved by a "prophet" who knows all the inputs (and not just their distributions) ahead of time. These inequalities have applications in the theory of algorithmic mechanism design and mathematical finance. Single item The classical single-item prophet inequality was published by , crediting its tight form to D. J. H. (Ben) Garling. It concerns a process in which a sequence of random variables X_i arrive from known distributions \mathcal_i. When each X_i arrives, the decision-making process must decide whether to accept it and stop the process, or whether to reject it and go on to the next variable in the sequence. The value of the process is the single accepted variable, if there is one, or zero otherwise. It may be assum ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Online Algorithm
In computer science, an online algorithm is one that can process its input piece-by-piece in a serial fashion, i.e., in the order that the input is fed to the algorithm, without having the entire input available from the start. In contrast, an offline algorithm is given the whole problem data from the beginning and is required to output an answer which solves the problem at hand. In operations research, the area in which online algorithms are developed is called online optimization. As an example, consider the sorting algorithms selection sort and insertion sort: selection sort repeatedly selects the minimum element from the unsorted remainder and places it at the front, which requires access to the entire input; it is thus an offline algorithm. On the other hand, insertion sort considers one input element per iteration and produces a partial solution without considering future elements. Thus insertion sort is an online algorithm. Note that the final result of an insertion sort ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Optimal Stopping
In mathematics, the theory of optimal stopping or early stopping : (For French translation, secover storyin the July issue of ''Pour la Science'' (2009).) is concerned with the problem of choosing a time to take a particular action, in order to maximise an expected reward or minimise an expected cost. Optimal stopping problems can be found in areas of statistics, economics, and mathematical finance (related to the pricing of American options). A key example of an optimal stopping problem is the secretary problem. Optimal stopping problems can often be written in the form of a Bellman equation, and are therefore often solved using dynamic programming. Definition Discrete time case Stopping rule problems are associated with two objects: # A sequence of random variables X_1, X_2, \ldots, whose joint distribution is something assumed to be known # A sequence of 'reward' functions (y_i)_ which depend on the observed values of the random variables in 1: #: y_i=y_i (x_1, \ldots ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Expected Value
In probability theory, the expected value (also called expectation, expectancy, mathematical expectation, mean, average, or first moment) is a generalization of the weighted average. Informally, the expected value is the arithmetic mean of a large number of independently selected outcomes of a random variable. The expected value of a random variable with a finite number of outcomes is a weighted average of all possible outcomes. In the case of a continuum of possible outcomes, the expectation is defined by integration. In the axiomatic foundation for probability provided by measure theory, the expectation is given by Lebesgue integration. The expected value of a random variable is often denoted by , , or , with also often stylized as or \mathbb. History The idea of the expected value originated in the middle of the 17th century from the study of the so-called problem of points, which seeks to divide the stakes ''in a fair way'' between two players, who have to e ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Probability Distribution
In probability theory and statistics, a probability distribution is the mathematical function that gives the probabilities of occurrence of different possible outcomes for an experiment. It is a mathematical description of a random phenomenon in terms of its sample space and the probabilities of events (subsets of the sample space). For instance, if is used to denote the outcome of a coin toss ("the experiment"), then the probability distribution of would take the value 0.5 (1 in 2 or 1/2) for , and 0.5 for (assuming that the coin is fair). Examples of random phenomena include the weather conditions at some future date, the height of a randomly selected person, the fraction of male students in a school, the results of a survey to be conducted, etc. Introduction A probability distribution is a mathematical description of the probabilities of events, subsets of the sample space. The sample space, often denoted by \Omega, is the set of all possible outcomes of a ra ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Algorithmic Mechanism Design
Algorithmic mechanism design (AMD) lies at the intersection of economic game theory, optimization, and computer science. The prototypical problem in mechanism design is to design a system for multiple self-interested participants, such that the participants' self-interested actions at equilibrium lead to good system performance. Typical objectives studied include revenue maximization and social welfare maximization. Algorithmic mechanism design differs from classical economic mechanism design in several respects. It typically employs the analytic tools of theoretical computer science, such as worst case analysis and approximation ratios, in contrast to classical mechanism design in economics which often makes distributional assumptions about the agents. It also considers computational constraints to be of central importance: mechanisms that cannot be efficiently implemented in polynomial time are not considered to be viable solutions to a mechanism design problem. This often, for exa ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Mathematical Finance
Mathematical finance, also known as quantitative finance and financial mathematics, is a field of applied mathematics, concerned with mathematical modeling of financial markets. In general, there exist two separate branches of finance that require advanced quantitative techniques: derivatives pricing on the one hand, and risk and portfolio management on the other. Mathematical finance overlaps heavily with the fields of computational finance and financial engineering. The latter focuses on applications and modeling, often by help of stochastic asset models, while the former focuses, in addition to analysis, on building tools of implementation for the models. Also related is quantitative investing, which relies on statistical and numerical models (and lately machine learning) as opposed to traditional fundamental analysis when managing portfolios. French mathematician Louis Bachelier's doctoral thesis, defended in 1900, is considered the first scholarly work on mathematical ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Linearity Of Expectation
In probability theory, the expected value (also called expectation, expectancy, mathematical expectation, mean, average, or first moment) is a generalization of the weighted average. Informally, the expected value is the arithmetic mean of a large number of independently selected outcomes of a random variable. The expected value of a random variable with a finite number of outcomes is a weighted average of all possible outcomes. In the case of a continuum of possible outcomes, the expectation is defined by integration. In the axiomatic foundation for probability provided by measure theory, the expectation is given by Lebesgue integration. The expected value of a random variable is often denoted by , , or , with also often stylized as or \mathbb. History The idea of the expected value originated in the middle of the 17th century from the study of the so-called problem of points, which seeks to divide the stakes ''in a fair way'' between two players, who have to end t ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Competitive Analysis (online Algorithm)
Competitive analysis is a method invented for analyzing online algorithms, in which the performance of an online algorithm (which must satisfy an unpredictable sequence of requests, completing each request without being able to see the future) is compared to the performance of an optimal ''offline algorithm'' that can view the sequence of requests in advance. An algorithm is ''competitive'' if its ''competitive ratio''—the ratio between its performance and the offline algorithm's performance—is bounded. Unlike traditional worst-case analysis, where the performance of an algorithm is measured only for "hard" inputs, competitive analysis requires that an algorithm perform well both on hard and easy inputs, where "hard" and "easy" are defined by the performance of the optimal offline algorithm. For many algorithms, performance is dependent not only on the size of the inputs, but also on their values. For example, sorting an array of elements varies in difficulty depending on the ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Worst Case
In computer science, best, worst, and average cases of a given algorithm express what the resource usage is ''at least'', ''at most'' and ''on average'', respectively. Usually the resource being considered is running time, i.e. time complexity, but could also be memory or some other resource. Best case is the function which performs the minimum number of steps on input data of n elements. Worst case is the function which performs the maximum number of steps on input data of size n. Average case is the function which performs an average number of steps on input data of n elements. In real-time computing, the worst-case execution time is often of particular concern since it is important to know how much time might be needed ''in the worst case'' to guarantee that the algorithm will always finish on time. Average performance and worst-case performance are the most used in algorithm analysis. Less widely found is best-case performance, but it does have uses: for example, where th ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Competitive Ratio
Competitive analysis is a method invented for analyzing online algorithms, in which the performance of an online algorithm (which must satisfy an unpredictable sequence of requests, completing each request without being able to see the future) is compared to the performance of an optimal ''offline algorithm'' that can view the sequence of requests in advance. An algorithm is ''competitive'' if its ''competitive ratio''—the ratio between its performance and the offline algorithm's performance—is bounded. Unlike traditional worst-case analysis, where the performance of an algorithm is measured only for "hard" inputs, competitive analysis requires that an algorithm perform well both on hard and easy inputs, where "hard" and "easy" are defined by the performance of the optimal offline algorithm. For many algorithms, performance is dependent not only on the size of the inputs, but also on their values. For example, sorting an array of elements varies in difficulty depending on the ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Games And Economic Behavior
''Games and Economic Behavior'' (''GEB'') is a journal of game theory published by Elsevier. Founded in 1989, the journal's stated objective is to communicate game-theoretic ideas across theory and applications. It is considered to be the leading journal of game theory and one of the top journals in economics, and it is one of the two official journals of the ''Game Theory Society''. Apart from game theory and economics, the research areas of the journal also include applications of game theory in political science, biology, computer science, mathematics and psychology. The founding editor-in-chief of ''GEB'' is Ehud Kalai. Current editor-in-chief is Hervé Moulin (since January 1, 2021). Each paper is initially assigned by GEB's chief editor to one of the seven editors (including himself). The chief editor has final decision authority. Impact Factor According to the ''Journal Citation Reports'', the journal has a 2020 impact factor The impact factor (IF) or journal impa ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Annals Of Probability
The ''Annals of Probability'' is a leading peer-reviewed probability journal published by the Institute of Mathematical Statistics, which is the main international society for researchers in the areas probability and statistics. The journal was started in 1973 as a continuation in part of the ''Annals of Mathematical Statistics'', which was split into the ''Annals of Statistics'' and this journal. In July 2021, the journal was ranked 7th in the field Probability & Statistics with Applications according to Google Scholar. It had an impact factor of 1.470 (as of 2010), according to the ''Journal Citation Reports''. The impact factor for 2018 is 2.085. Its CiteScore is 4.3, and SCImago Journal Rank is 3.184, both from 2020. Editors-in-Chief: Past and Present The following persons have been editor-in-chief of the journal: * Ronald Pyke (1972–1975) * Patrick Billingsley (1976–1978) * Richard M. Dudley (1979–1981) * Thomas M. Liggett (1985–1987) * Peter E. Ney (1988–1990) * B ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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