Probability Theory As a Physical Theory Points to Superdeterminism. Louis
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Measure-Theoretic Probability I
Measure-Theoretic Probability I Steven P.Lalley Winter 2017 1 1 Measure Theory 1.1 Why Measure Theory? There are two different views – not necessarily exclusive – on what “probability” means: the subjectivist view and the frequentist view. To the subjectivist, probability is a system of laws that should govern a rational person’s behavior in situations where a bet must be placed (not necessarily just in a casino, but in situations where a decision must be made about how to proceed when only imperfect information about the outcome of the decision is available, for instance, should I allow Dr. Scissorhands to replace my arthritic knee by a plastic joint?). To the frequentist, the laws of probability describe the long- run relative frequencies of different events in “experiments” that can be repeated under roughly identical conditions, for instance, rolling a pair of dice. For the frequentist inter- pretation, it is imperative that probability spaces be large enough to allow a description of an experiment, like dice-rolling, that is repeated infinitely many times, and that the mathematical laws should permit easy handling of limits, so that one can make sense of things like “the probability that the long-run fraction of dice rolls where the two dice sum to 7 is 1/6”. But even for the subjectivist, the laws of probability should allow for description of situations where there might be a continuum of possible outcomes, or pos- sible actions to be taken. Once one is reconciled to the need for such flexibility, it soon becomes apparent that measure theory (the theory of countably additive, as opposed to merely finitely additive measures) is the only way to go. -
Foundations of Probability Theory and Statistical Mechanics
Chapter 6 Foundations of Probability Theory and Statistical Mechanics EDWIN T. JAYNES Department of Physics, Washington University St. Louis, Missouri 1. What Makes Theories Grow? Scientific theories are invented and cared for by people; and so have the properties of any other human institution - vigorous growth when all the factors are right; stagnation, decadence, and even retrograde progress when they are not. And the factors that determine which it will be are seldom the ones (such as the state of experimental or mathe matical techniques) that one might at first expect. Among factors that have seemed, historically, to be more important are practical considera tions, accidents of birth or personality of individual people; and above all, the general philosophical climate in which the scientist lives, which determines whether efforts in a certain direction will be approved or deprecated by the scientific community as a whole. However much the" pure" scientist may deplore it, the fact remains that military or engineering applications of science have, over and over again, provided the impetus without which a field would have remained stagnant. We know, for example, that ARCHIMEDES' work in mechanics was at the forefront of efforts to defend Syracuse against the Romans; and that RUMFORD'S experiments which led eventually to the first law of thermodynamics were performed in the course of boring cannon. The development of microwave theory and techniques during World War II, and the present high level of activity in plasma physics are more recent examples of this kind of interaction; and it is clear that the past decade of unprecedented advances in solid-state physics is not entirely unrelated to commercial applications, particularly in electronics. -
Accommodating Retrocausality with Free Will Yakir Aharonov Chapman University, [email protected]
Chapman University Chapman University Digital Commons Mathematics, Physics, and Computer Science Science and Technology Faculty Articles and Faculty Articles and Research Research 2016 Accommodating Retrocausality with Free Will Yakir Aharonov Chapman University, [email protected] Eliahu Cohen Tel Aviv University Tomer Shushi University of Haifa Follow this and additional works at: http://digitalcommons.chapman.edu/scs_articles Part of the Quantum Physics Commons Recommended Citation Aharonov, Y., Cohen, E., & Shushi, T. (2016). Accommodating Retrocausality with Free Will. Quanta, 5(1), 53-60. doi:http://dx.doi.org/10.12743/quanta.v5i1.44 This Article is brought to you for free and open access by the Science and Technology Faculty Articles and Research at Chapman University Digital Commons. It has been accepted for inclusion in Mathematics, Physics, and Computer Science Faculty Articles and Research by an authorized administrator of Chapman University Digital Commons. For more information, please contact [email protected]. Accommodating Retrocausality with Free Will Comments This article was originally published in Quanta, volume 5, issue 1, in 2016. DOI: 10.12743/quanta.v5i1.44 Creative Commons License This work is licensed under a Creative Commons Attribution 3.0 License. This article is available at Chapman University Digital Commons: http://digitalcommons.chapman.edu/scs_articles/334 Accommodating Retrocausality with Free Will Yakir Aharonov 1;2, Eliahu Cohen 1;3 & Tomer Shushi 4 1 School of Physics and Astronomy, Tel Aviv University, Tel Aviv, Israel. E-mail: [email protected] 2 Schmid College of Science, Chapman University, Orange, California, USA. E-mail: [email protected] 3 H. H. Wills Physics Laboratory, University of Bristol, Bristol, UK. -
Probability and Statistics Lecture Notes
Probability and Statistics Lecture Notes Antonio Jiménez-Martínez Chapter 1 Probability spaces In this chapter we introduce the theoretical structures that will allow us to assign proba- bilities in a wide range of probability problems. 1.1. Examples of random phenomena Science attempts to formulate general laws on the basis of observation and experiment. The simplest and most used scheme of such laws is: if a set of conditions B is satisfied =) event A occurs. Examples of such laws are the law of gravity, the law of conservation of mass, and many other instances in chemistry, physics, biology... If event A occurs inevitably whenever the set of conditions B is satisfied, we say that A is certain or sure (under the set of conditions B). If A can never occur whenever B is satisfied, we say that A is impossible (under the set of conditions B). If A may or may not occur whenever B is satisfied, then A is said to be a random phenomenon. Random phenomena is our subject matter. Unlike certain and impossible events, the presence of randomness implies that the set of conditions B do not reflect all the necessary and sufficient conditions for the event A to occur. It might seem them impossible to make any worthwhile statements about random phenomena. However, experience has shown that many random phenomena exhibit a statistical regularity that makes them subject to study. For such random phenomena it is possible to estimate the chance of occurrence of the random event. This estimate can be obtained from laws, called probabilistic or stochastic, with the form: if a set of conditions B is satisfied event A occurs m times =) repeatedly n times out of the n repetitions. -
Randomized Algorithms Week 1: Probability Concepts
600.664: Randomized Algorithms Professor: Rao Kosaraju Johns Hopkins University Scribe: Your Name Randomized Algorithms Week 1: Probability Concepts Rao Kosaraju 1.1 Motivation for Randomized Algorithms In a randomized algorithm you can toss a fair coin as a step of computation. Alternatively a bit taking values of 0 and 1 with equal probabilities can be chosen in a single step. More generally, in a single step an element out of n elements can be chosen with equal probalities (uniformly at random). In such a setting, our goal can be to design an algorithm that minimizes run time. Randomized algorithms are often advantageous for several reasons. They may be faster than deterministic algorithms. They may also be simpler. In addition, there are problems that can be solved with randomization for which we cannot design efficient deterministic algorithms directly. In some of those situations, we can design attractive deterministic algoirthms by first designing randomized algorithms and then converting them into deter- ministic algorithms by applying standard derandomization techniques. Deterministic Algorithm: Worst case running time, i.e. the number of steps the algo- rithm takes on the worst input of length n. Randomized Algorithm: 1) Expected number of steps the algorithm takes on the worst input of length n. 2) w.h.p. bounds. As an example of a randomized algorithm, we now present the classic quicksort algorithm and derive its performance later on in the chapter. We are given a set S of n distinct elements and we want to sort them. Below is the randomized quicksort algorithm. Algorithm RandQuickSort(S = fa1; a2; ··· ; ang If jSj ≤ 1 then output S; else: Choose a pivot element ai uniformly at random (u.a.r.) from S Split the set S into two subsets S1 = fajjaj < aig and S2 = fajjaj > aig by comparing each aj with the chosen ai Recurse on sets S1 and S2 Output the sorted set S1 then ai and then sorted S2. -
Bell's Theorem and Its Tests
PHYSICS ESSAYS 33, 2 (2020) Bell’s theorem and its tests: Proof that nature is superdeterministic—Not random Johan Hanssona) Division of Physics, Lulea˚ University of Technology, SE-971 87 Lulea˚, Sweden (Received 9 March 2020; accepted 7 May 2020; published online 22 May 2020) Abstract: By analyzing the same Bell experiment in different reference frames, we show that nature at its fundamental level is superdeterministic, not random, in contrast to what is indicated by orthodox quantum mechanics. Events—including the results of quantum mechanical measurements—in global space-time are fixed prior to measurement. VC 2020 Physics Essays Publication. [http://dx.doi.org/10.4006/0836-1398-33.2.216] Resume: En analysant l’experience de Bell dans d’autres cadres de reference, nous demontrons que la nature est super deterministe au niveau fondamental et non pas aleatoire, contrairement ace que predit la mecanique quantique. Des evenements, incluant les resultats des mesures mecaniques quantiques, dans un espace-temps global sont fixes avant la mesure. Key words: Quantum Nonlocality; Bell’s Theorem; Quantum Measurement. Bell’s theorem1 is not merely a statement about quantum Registered outcomes at either side, however, are classi- mechanics but about nature itself, and will survive even if cal objective events (for example, a sequence of zeros and quantum mechanics is superseded by an even more funda- ones representing spin up or down along some chosen mental theory in the future. Although Bell used aspects of direction), e.g., markings on a paper printout ¼ classical quantum theory in his original proof, the same results can be facts ¼ events ¼ points defining (constituting) global space- obtained without doing so.2,3 The many experimental tests of time itself. -
Indeterminism in Physics and Intuitionistic Mathematics
Indeterminism in Physics and Intuitionistic Mathematics Nicolas Gisin Group of Applied Physics, University of Geneva, 1211 Geneva 4, Switzerland (Dated: November 5, 2020) Most physics theories are deterministic, with the notable exception of quantum mechanics which, however, comes plagued by the so-called measurement problem. This state of affairs might well be due to the inability of standard mathematics to “speak” of indeterminism, its inability to present us a worldview in which new information is created as time passes. In such a case, scientific determinism would only be an illusion due to the timeless mathematical language scientists use. To investigate this possibility it is necessary to develop an alternative mathematical language that is both powerful enough to allow scientists to compute predictions and compatible with indeterminism and the passage of time. We argue that intuitionistic mathematics provides such a language and we illustrate it in simple terms. I. INTRODUCTION I have always been amazed by the huge difficulties that my fellow physicists seem to encounter when con- templating indeterminism and the highly sophisticated Physicists are not used to thinking of the world as in- circumlocutions they are ready to swallow to avoid the determinate and its evolution as indeterministic. New- straightforward conclusion that physics does not neces- ton’s equations, like Maxwell’s and Schr¨odinger’s equa- sarily present us a deterministic worldview. But even tions, are (partial) differential equations describing the more surprising to me is the attitude of most philoso- continuous evolution of the initial condition as a func- phers, especially philosophers of science. Indeed, most tion of a parameter identified with time. -
Probability Theory Review for Machine Learning
Probability Theory Review for Machine Learning Samuel Ieong November 6, 2006 1 Basic Concepts Broadly speaking, probability theory is the mathematical study of uncertainty. It plays a central role in machine learning, as the design of learning algorithms often relies on proba- bilistic assumption of the data. This set of notes attempts to cover some basic probability theory that serves as a background for the class. 1.1 Probability Space When we speak about probability, we often refer to the probability of an event of uncertain nature taking place. For example, we speak about the probability of rain next Tuesday. Therefore, in order to discuss probability theory formally, we must first clarify what the possible events are to which we would like to attach probability. Formally, a probability space is defined by the triple (Ω, F,P ), where • Ω is the space of possible outcomes (or outcome space), • F ⊆ 2Ω (the power set of Ω) is the space of (measurable) events (or event space), • P is the probability measure (or probability distribution) that maps an event E ∈ F to a real value between 0 and 1 (think of P as a function). Given the outcome space Ω, there is some restrictions as to what subset of 2Ω can be considered an event space F: • The trivial event Ω and the empty event ∅ is in F. • The event space F is closed under (countable) union, i.e., if α, β ∈ F, then α ∪ β ∈ F. • The even space F is closed under complement, i.e., if α ∈ F, then (Ω \ α) ∈ F. -
Wittgenstein on Freedom of the Will: Not Determinism, Yet Not Indeterminism
Wittgenstein on Freedom of the Will: Not Determinism, Yet Not Indeterminism Thomas Nadelhoffer This is a prepublication draft. This version is being revised for resubmission to a journal. Abstract Since the publication of Wittgenstein’s Lectures on Freedom of the Will, his remarks about free will and determinism have received very little attention. Insofar as these lectures give us an opportunity to see him at work on a traditional—and seemingly intractable—philosophical problem and given the voluminous secondary literature written about nearly every other facet of Wittgenstein’s life and philosophy, this neglect is both surprising and unfortunate. Perhaps these lectures have not attracted much attention because they are available to us only in the form of a single student’s notes (Yorick Smythies). Or perhaps it is because, as one Wittgenstein scholar put it, the lectures represent only “cursory reflections” that “are themselves uncompelling." (Glock 1996: 390) Either way, my goal is to show that Wittgenstein’s views about freedom of the will merit closer attention. All of these arguments might look as if I wanted to argue for the freedom of the will or against it. But I don't want to. --Ludwig Wittgenstein, Lectures on Freedom of the Will Since the publication of Wittgenstein’s Lectures on Freedom of the Will,1 his remarks from these lectures about free will and determinism have received very little attention.2 Insofar as these lectures give us an opportunity to see him at work on a traditional—and seemingly intractable— philosophical problem and given the voluminous secondary literature written about nearly every 1 Wittgenstein’s “Lectures on Freedom of the Will” will be abbreviated as LFW 1993 in this paper (see bibliography) since I am using the version reprinted in Philosophical Occasions (1993). -
Applied Mathematics 1
Applied Mathematics 1 MATH 0180 Intermediate Calculus 1 Applied Mathematics MATH 0520 Linear Algebra 2 1 APMA 0350 Applied Ordinary Differential Equations 2 & APMA 0360 and Applied Partial Differential Equations Chair I 3 4 Bjorn Sandstede Select one course on programming from the following: 1 APMA 0090 Introduction to Mathematical Modeling Associate Chair APMA 0160 Introduction to Computing Sciences Kavita Ramanan CSCI 0040 Introduction to Scientific Computing and The Division of Applied Mathematics at Brown University is one of Problem Solving the most prominent departments at Brown, and is also one of the CSCI 0111 Computing Foundations: Data oldest and strongest of its type in the country. The Division of Applied CSCI 0150 Introduction to Object-Oriented Mathematics is a world renowned center of research activity in a Programming and Computer Science wide spectrum of traditional and modern mathematics. It explores the connections between mathematics and its applications at both CSCI 0170 Computer Science: An Integrated the research and educational levels. The principal areas of research Introduction activities are ordinary, functional, and partial differential equations: Five additional courses, of which four should be chosen from 5 stochastic control theory; applied probability, statistics and stochastic the 1000-level courses taught by the Division of Applied systems theory; neuroscience and computational molecular biology; Mathematics. APMA 1910 cannot be used as an elective. numerical analysis and scientific computation; and the mechanics of Total Credits 10 solids, materials science and fluids. The effort in virtually all research 1 ranges from applied and algorithmic problems to the study of fundamental Substitution of alternate courses for the specific requirements is mathematical questions. -
Determinism, Indeterminism and the Statistical Postulate
Tipicality, Explanation, The Statistical Postulate, and GRW Valia Allori Northern Illinois University [email protected] www.valiaallori.com Rutgers, October 24-26, 2019 1 Overview Context: Explanation of the macroscopic laws of thermodynamics in the Boltzmannian approach Among the ingredients: the statistical postulate (connected with the notion of probability) In this presentation: typicality Def: P is a typical property of X-type object/phenomena iff the vast majority of objects/phenomena of type X possesses P Part I: typicality is sufficient to explain macroscopic laws – explanatory schema based on typicality: you explain P if you explain that P is typical Part II: the statistical postulate as derivable from the dynamics Part III: if so, no preference for indeterministic theories in the quantum domain 2 Summary of Boltzmann- 1 Aim: ‘derive’ macroscopic laws of thermodynamics in terms of the microscopic Newtonian dynamics Problems: Technical ones: There are to many particles to do exact calculations Solution: statistical methods - if 푁 is big enough, one can use suitable mathematical technique to obtain information of macro systems even without having the exact solution Conceptual ones: Macro processes are irreversible, while micro processes are not Boltzmann 3 Summary of Boltzmann- 2 Postulate 1: the microscopic dynamics microstate 푋 = 푟1, … . 푟푁, 푣1, … . 푣푁 in phase space Partition of phase space into Macrostates Macrostate 푀(푋): set of macroscopically indistinguishable microstates Macroscopic view Many 푋 for a given 푀 given 푀, which 푋 is unknown Macro Properties (e.g. temperature): they slowly vary on the Macro scale There is a particular Macrostate which is incredibly bigger than the others There are many ways more to have, for instance, uniform temperature than not equilibrium (Macro)state 4 Summary of Boltzmann- 3 Entropy: (def) proportional to the size of the Macrostate in phase space (i.e. -
Is the Cosmos Random?
IS THE RANDOM? COSMOS QUANTUM PHYSICS Einstein’s assertion that God does not play dice with the universe has been misinterpreted By George Musser Few of Albert Einstein’s sayings have been as widely quot- ed as his remark that God does not play dice with the universe. People have naturally taken his quip as proof that he was dogmatically opposed to quantum mechanics, which views randomness as a built-in feature of the physical world. When a radioactive nucleus decays, it does so sponta- neously; no rule will tell you when or why. When a particle of light strikes a half-silvered mirror, it either reflects off it or passes through; the out- come is open until the moment it occurs. You do not need to visit a labora- tory to see these processes: lots of Web sites display streams of random digits generated by Geiger counters or quantum optics. Being unpredict- able even in principle, such numbers are ideal for cryptography, statistics and online poker. Einstein, so the standard tale goes, refused to accept that some things are indeterministic—they just happen, and there is not a darned thing anyone can do to figure out why. Almost alone among his peers, he clung to the clockwork universe of classical physics, ticking mechanistically, each moment dictating the next. The dice-playing line became emblemat- ic of the B side of his life: the tragedy of a revolutionary turned reaction- ary who upended physics with relativity theory but was, as Niels Bohr put it, “out to lunch” on quantum theory.