ET Jaynes
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ET Jaynes
Edwin Thompson Jaynes (July 5, 1922 – April 30, 1998) was the Wayman Crow Distinguished Professor of Physics at Washington University in St. Louis. He wrote extensively on statistical mechanics and on foundations of probability and statistical inference, initiating in 1957 the maximum entropy interpretation of thermodynamics as being a particular application of more general Bayesian/ information theory techniques (although he argued this was already implicit in the works of Josiah Willard Gibbs). Jaynes strongly promoted the interpretation of probability theory as an extension of logic. In 1963, together with Fred Cummings, he modeled the evolution of a two-level atom in an electromagnetic field, in a fully quantized way. This model is known as the Jaynes–Cummings model. A particular focus of his work was the construction of logical principles for assigning prior probability distributions; see the principle of maximum entropy, the principle of maximum caliber, the p ...
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Waterloo, Iowa
Waterloo is a city in and the county seat of Black Hawk County, Iowa, United States. As of the 2020 United States Census the population was 67,314, making it the eighth-largest city in the state. The city is part of the Waterloo – Cedar Falls Metropolitan Statistical Area, and is the more populous of the two cities. History Waterloo was originally known as Prairie Rapids Crossing. The town was established near two Meskwaki American tribal seasonal camps alongside the Cedar River. It was first settled in 1845 when George and Mary Melrose Hanna and their children arrived on the east bank of the Red Cedar River (now just called the Cedar River). They were followed by the Virden and Mullan families in 1846. Evidence of these earliest families can still be found in the street names Hanna Boulevard, Mullan Avenue and Virden Creek. On December 8, 1845, the ''Iowa State Register and Waterloo Herald'' was the first newspaper published in Waterloo. The name Waterloo supplanted the o ...
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Probability Theory
Probability theory is the branch of mathematics concerned with probability. Although there are several different probability interpretations, probability theory treats the concept in a rigorous mathematical manner by expressing it through a set of axioms. Typically these axioms formalise probability in terms of a probability space, which assigns a measure taking values between 0 and 1, termed the probability measure, to a set of outcomes called the sample space. Any specified subset of the sample space is called an event. Central subjects in probability theory include discrete and continuous random variables, probability distributions, and stochastic processes (which provide mathematical abstractions of non-deterministic or uncertain processes or measured quantities that may either be single occurrences or evolve over time in a random fashion). Although it is not possible to perfectly predict random events, much can be said about their behavior. Two major results in probability ...
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Harold Jeffreys
Sir Harold Jeffreys, FRS (22 April 1891 – 18 March 1989) was a British mathematician, statistician, geophysicist, and astronomer. His book, ''Theory of Probability'', which was first published in 1939, played an important role in the revival of the objective Bayesian view of probability. Education Jeffreys was born in Fatfield, County Durham, England, the son of Robert Hal Jeffreys, headmaster of Fatfield Church School, and his wife, Elizabeth Mary Sharpe, a school teacher. He was educated at his father's school then studied at Armstrong College in Newcastle upon Tyne, then part of the University of Durham, and with the University of London External Programme. Career Jeffreys became a fellow of St John's College, Cambridge in 1914. At the University of Cambridge he taught mathematics, then geophysics and finally became the Plumian Professor of Astronomy. In 1940 he married fellow mathematician and physicist, Bertha Swirles (1903–1999), and together they wrote ''Methods ...
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Probability Theory As Extended Logic
Cox's theorem, named after the physicist Richard Threlkeld Cox, is a derivation of the laws of probability theory from a certain set of postulates. This derivation justifies the so-called "logical" interpretation of probability, as the laws of probability derived by Cox's theorem are applicable to any proposition. Logical (also known as objective Bayesian) probability is a type of Bayesian probability. Other forms of Bayesianism, such as the subjective interpretation, are given other justifications. Cox's assumptions Cox wanted his system to satisfy the following conditions: #Divisibility and comparability – The plausibility of a proposition is a real number and is dependent on information we have related to the proposition. #Common sense – Plausibilities should vary sensibly with the assessment of plausibilities in the model. #Consistency – If the plausibility of a proposition can be derived in many ways, all the results must be equal. The postul ...
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Mind Projection Fallacy
The mind projection fallacy is an informal fallacy first described by physicist and Bayesian philosopher E. T. Jaynes. In a first, "positive" form, it occurs when someone thinks that the way they see the world reflects the way the world really is, going as far as assuming the real existence of imagined objects. That is, someone's subjective judgments are "projected" to be inherent properties of an object, rather than being related to personal perception. One consequence is that others may be assumed to share the same perception, or that they are irrational or misinformed if they do not. In a second "negative" form of the fallacy, as described by Jaynes, occurs when someone assumes that their own lack of knowledge about a phenomenon (a fact about their state of mind) means that the phenomenon is not or cannot be understood (a fact about reality). (See also Map and territory.) Jaynes used this concept to argue against Copenhagen interpretation of quantum mechanics. He described ...
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Principle Of Indifference
The principle of indifference (also called principle of insufficient reason) is a rule for assigning epistemic probabilities. The principle of indifference states that in the absence of any relevant evidence, agents should distribute their credence (or 'degrees of belief') equally among all the possible outcomes under consideration. In Bayesian probability, this is the simplest non-informative prior. The principle of indifference is meaningless under the frequency interpretation of probability, in which probabilities are relative frequencies rather than degrees of belief in uncertain propositions, conditional upon state information. Examples The textbook examples for the application of the principle of indifference are coins, dice, and cards. In a macroscopic system, at least, it must be assumed that the physical laws that govern the system are not known well enough to predict the outcome. As observed some centuries ago by John Arbuthnot (in the preface of ''Of the Laws of ...
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Pierre-Simon Laplace
Pierre-Simon, marquis de Laplace (; ; 23 March 1749 – 5 March 1827) was a French scholar and polymath whose work was important to the development of engineering, mathematics, statistics, physics, astronomy, and philosophy. He summarized and extended the work of his predecessors in his five-volume ''Mécanique céleste'' (''Celestial Mechanics'') (1799–1825). This work translated the geometric study of classical mechanics to one based on calculus, opening up a broader range of problems. In statistics, the Bayesian interpretation of probability was developed mainly by Laplace. Laplace formulated Laplace's equation, and pioneered the Laplace transform which appears in many branches of mathematical physics, a field that he took a leading role in forming. The Laplacian differential operator, widely used in mathematics, is also named after him. He restated and developed the nebular hypothesis of the origin of the Solar System and was one of the first scientists to sugges ...
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Principle Of Transformation Groups
The principle of transformation groups is a rule for assigning ''epistemic'' probabilities in a statistical inference problem. It was first suggested by Edwin T. Jaynes and can be seen as a generalisation of the principle of indifference. This can be seen as a method to create ''objective ignorance probabilities'' in the sense that two people who apply the principle and are confronted with the same information will assign the same probabilities. Motivation and description of the method The method is motivated by the following normative principle, or desideratum: ''In two problems where we have the same prior information we should assign the same prior probabilities'' The method then comes about from "transforming" a given problem into an equivalent one. This method has close connections with group theory, and to a large extent is about finding symmetry in a given problem, and then exploiting this symmetry to assign prior probabilities. In problems with discrete variables (e. ...
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Principle Of Maximum Caliber
The principle of maximum caliber (MaxCal) or maximum path entropy principle, suggested by E. T. Jaynes, can be considered as a generalization of the principle of maximum entropy. It postulates that the most unbiased probability distribution of paths is the one that maximizes their Shannon entropy Shannon may refer to: People * Shannon (given name) * Shannon (surname) * Shannon (American singer), stage name of singer Shannon Brenda Greene (born 1958) * Shannon (South Korean singer), British-South Korean singer and actress Shannon Arrum W .... This entropy of paths is sometimes called the "caliber" of the system, and is given by the path integral : S rho[x() = \int D_x \,\, \rho[x()">().html" ;"title="rho[x()">rho[x() = \int D_x \,\, \rho[x() \, \ln History The principle of maximum caliber was proposed by Edwin T. Jaynes in 1980, in an article titled ''The Minimum Entropy Production Principle'' over the context of to find a principle for to derive the non-equilibrium stati ...
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Principle Of Maximum Entropy
The principle of maximum entropy states that the probability distribution which best represents the current state of knowledge about a system is the one with largest entropy, in the context of precisely stated prior data (such as a proposition that expresses testable information). Another way of stating this: Take precisely stated prior data or testable information about a probability distribution function. Consider the set of all trial probability distributions that would encode the prior data. According to this principle, the distribution with maximal information entropy is the best choice. History The principle was first expounded by E. T. Jaynes in two papers in 1957 where he emphasized a natural correspondence between statistical mechanics and information theory. In particular, Jaynes offered a new and very general rationale why the Gibbsian method of statistical mechanics works. He argued that the entropy of statistical mechanics and the information entropy of informati ...
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Prior Probability
In Bayesian statistical inference, a prior probability distribution, often simply called the prior, of an uncertain quantity is the probability distribution that would express one's beliefs about this quantity before some evidence is taken into account. For example, the prior could be the probability distribution representing the relative proportions of voters who will vote for a particular politician in a future election. The unknown quantity may be a parameter of the model or a latent variable rather than an observable variable. Bayes' theorem calculates the renormalized pointwise product of the prior and the likelihood function, to produce the ''posterior probability distribution'', which is the conditional distribution of the uncertain quantity given the data. Similarly, the prior probability of a random event or an uncertain proposition is the unconditional probability that is assigned before any relevant evidence is taken into account. Priors can be created using a num ...
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Two-level System
In quantum mechanics, a two-state system (also known as a two-level system) is a quantum system that can exist in any quantum superposition of two independent (physically distinguishable) quantum states. The Hilbert space describing such a system is two-dimensional. Therefore, a complete basis spanning the space will consist of two independent states. Any two-state system can also be seen as a qubit. Two-state systems are the simplest quantum systems that are of interest, since the dynamics of a one-state system is trivial (as there are no other states the system can exist in). The mathematical framework required for the analysis of two-state systems is that of linear differential equations and linear algebra of two-dimensional spaces. As a result, the dynamics of a two-state system can be solved analytically without any approximation. The generic behavior of the system is that the wavefunction's amplitude oscillates between the two states. A very well known example of a two-st ...
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