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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. -
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. -
Random Variable = a Real-Valued Function of an Outcome X = F(Outcome)
Random Variables (Chapter 2) Random variable = A real-valued function of an outcome X = f(outcome) Domain of X: Sample space of the experiment. Ex: Consider an experiment consisting of 3 Bernoulli trials. Bernoulli trial = Only two possible outcomes – success (S) or failure (F). • “IF” statement: if … then “S” else “F” • Examine each component. S = “acceptable”, F = “defective”. • Transmit binary digits through a communication channel. S = “digit received correctly”, F = “digit received incorrectly”. Suppose the trials are independent and each trial has a probability ½ of success. X = # successes observed in the experiment. Possible values: Outcome Value of X (SSS) (SSF) (SFS) … … (FFF) Random variable: • Assigns a real number to each outcome in S. • Denoted by X, Y, Z, etc., and its values by x, y, z, etc. • Its value depends on chance. • Its value becomes available once the experiment is completed and the outcome is known. • Probabilities of its values are determined by the probabilities of the outcomes in the sample space. Probability distribution of X = A table, formula or a graph that summarizes how the total probability of one is distributed over all the possible values of X. In the Bernoulli trials example, what is the distribution of X? 1 Two types of random variables: Discrete rv = Takes finite or countable number of values • Number of jobs in a queue • Number of errors • Number of successes, etc. Continuous rv = Takes all values in an interval – i.e., it has uncountable number of values. • Execution time • Waiting time • Miles per gallon • Distance traveled, etc. Discrete random variables X = A discrete rv. -
39 Section J Basic Probability Concepts Before We Can Begin To
Section J Basic Probability Concepts Before we can begin to discuss inferential statistics, we need to discuss probability. Recall, inferential statistics deals with analyzing a sample from the population to draw conclusions about the population, therefore since the data came from a sample we can never be 100% certain the conclusion is correct. Therefore, probability is an integral part of inferential statistics and needs to be studied before starting the discussion on inferential statistics. The theoretical probability of an event is the proportion of times the event occurs in the long run, as a probability experiment is repeated over and over again. Law of Large Numbers says that as a probability experiment is repeated again and again, the proportion of times that a given event occurs will approach its probability. A sample space contains all possible outcomes of a probability experiment. EX: An event is an outcome or a collection of outcomes from a sample space. A probability model for a probability experiment consists of a sample space, along with a probability for each event. Note: If A denotes an event then the probability of the event A is denoted P(A). Probability models with equally likely outcomes If a sample space has n equally likely outcomes, and an event A has k outcomes, then Number of outcomes in A k P(A) = = Number of outcomes in the sample space n The probability of an event is always between 0 and 1, inclusive. 39 Important probability characteristics: 1) For any event A, 0 ≤ P(A) ≤ 1 2) If A cannot occur, then P(A) = 0. -
Probability and Counting Rules
blu03683_ch04.qxd 09/12/2005 12:45 PM Page 171 C HAPTER 44 Probability and Counting Rules Objectives Outline After completing this chapter, you should be able to 4–1 Introduction 1 Determine sample spaces and find the probability of an event, using classical 4–2 Sample Spaces and Probability probability or empirical probability. 4–3 The Addition Rules for Probability 2 Find the probability of compound events, using the addition rules. 4–4 The Multiplication Rules and Conditional 3 Find the probability of compound events, Probability using the multiplication rules. 4–5 Counting Rules 4 Find the conditional probability of an event. 5 Find the total number of outcomes in a 4–6 Probability and Counting Rules sequence of events, using the fundamental counting rule. 4–7 Summary 6 Find the number of ways that r objects can be selected from n objects, using the permutation rule. 7 Find the number of ways that r objects can be selected from n objects without regard to order, using the combination rule. 8 Find the probability of an event, using the counting rules. 4–1 blu03683_ch04.qxd 09/12/2005 12:45 PM Page 172 172 Chapter 4 Probability and Counting Rules Statistics Would You Bet Your Life? Today Humans not only bet money when they gamble, but also bet their lives by engaging in unhealthy activities such as smoking, drinking, using drugs, and exceeding the speed limit when driving. Many people don’t care about the risks involved in these activities since they do not understand the concepts of probability. -
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. -
Topic 1: Basic Probability Definition of Sets
Topic 1: Basic probability ² Review of sets ² Sample space and probability measure ² Probability axioms ² Basic probability laws ² Conditional probability ² Bayes' rules ² Independence ² Counting ES150 { Harvard SEAS 1 De¯nition of Sets ² A set S is a collection of objects, which are the elements of the set. { The number of elements in a set S can be ¯nite S = fx1; x2; : : : ; xng or in¯nite but countable S = fx1; x2; : : :g or uncountably in¯nite. { S can also contain elements with a certain property S = fx j x satis¯es P g ² S is a subset of T if every element of S also belongs to T S ½ T or T S If S ½ T and T ½ S then S = T . ² The universal set is the set of all objects within a context. We then consider all sets S ½ . ES150 { Harvard SEAS 2 Set Operations and Properties ² Set operations { Complement Ac: set of all elements not in A { Union A \ B: set of all elements in A or B or both { Intersection A [ B: set of all elements common in both A and B { Di®erence A ¡ B: set containing all elements in A but not in B. ² Properties of set operations { Commutative: A \ B = B \ A and A [ B = B [ A. (But A ¡ B 6= B ¡ A). { Associative: (A \ B) \ C = A \ (B \ C) = A \ B \ C. (also for [) { Distributive: A \ (B [ C) = (A \ B) [ (A \ C) A [ (B \ C) = (A [ B) \ (A [ C) { DeMorgan's laws: (A \ B)c = Ac [ Bc (A [ B)c = Ac \ Bc ES150 { Harvard SEAS 3 Elements of probability theory A probabilistic model includes ² The sample space of an experiment { set of all possible outcomes { ¯nite or in¯nite { discrete or continuous { possibly multi-dimensional ² An event A is a set of outcomes { a subset of the sample space, A ½ . -
Measure Theory and Probability
Measure theory and probability Alexander Grigoryan University of Bielefeld Lecture Notes, October 2007 - February 2008 Contents 1 Construction of measures 3 1.1Introductionandexamples........................... 3 1.2 σ-additive measures ............................... 5 1.3 An example of using probability theory . .................. 7 1.4Extensionofmeasurefromsemi-ringtoaring................ 8 1.5 Extension of measure to a σ-algebra...................... 11 1.5.1 σ-rings and σ-algebras......................... 11 1.5.2 Outermeasure............................. 13 1.5.3 Symmetric difference.......................... 14 1.5.4 Measurable sets . ............................ 16 1.6 σ-finitemeasures................................ 20 1.7Nullsets..................................... 23 1.8 Lebesgue measure in Rn ............................ 25 1.8.1 Productmeasure............................ 25 1.8.2 Construction of measure in Rn. .................... 26 1.9 Probability spaces ................................ 28 1.10 Independence . ................................. 29 2 Integration 38 2.1 Measurable functions.............................. 38 2.2Sequencesofmeasurablefunctions....................... 42 2.3 The Lebesgue integral for finitemeasures................... 47 2.3.1 Simplefunctions............................ 47 2.3.2 Positivemeasurablefunctions..................... 49 2.3.3 Integrablefunctions........................... 52 2.4Integrationoversubsets............................ 56 2.5 The Lebesgue integral for σ-finitemeasure................. -
Probability Theory Review 1 Basic Notions: Sample Space, Events
Fall 2018 Probability Theory Review Aleksandar Nikolov 1 Basic Notions: Sample Space, Events 1 A probability space (Ω; P) consists of a finite or countable set Ω called the sample space, and the P probability function P :Ω ! R such that for all ! 2 Ω, P(!) ≥ 0 and !2Ω P(!) = 1. We call an element ! 2 Ω a sample point, or outcome, or simple event. You should think of a sample space as modeling some random \experiment": Ω contains all possible outcomes of the experiment, and P(!) gives the probability that we are going to get outcome !. Note that we never speak of probabilities except in relation to a sample space. At this point we give a few examples: 1. Consider a random experiment in which we toss a single fair coin. The two possible outcomes are that the coin comes up heads (H) or tails (T), and each of these outcomes is equally likely. 1 Then the probability space is (Ω; P), where Ω = fH; T g and P(H) = P(T ) = 2 . 2. Consider a random experiment in which we toss a single coin, but the coin lands heads with 2 probability 3 . Then, once again the sample space is Ω = fH; T g but the probability function 2 1 is different: P(H) = 3 , P(T ) = 3 . 3. Consider a random experiment in which we toss a fair coin three times, and each toss is independent of the others. The coin can come up heads all three times, or come up heads twice and then tails, etc. -
1 Probability Measure and Random Variables
1 Probability measure and random variables 1.1 Probability spaces and measures We will use the term experiment in a very general way to refer to some process that produces a random outcome. Definition 1. The set of possible outcomes is called the sample space. We will typically denote an individual outcome by ω and the sample space by Ω. Set notation: A B, A is a subset of B, means that every element of A is also in B. The union⊂ A B of A and B is the of all elements that are in A or B, including those that∪ are in both. The intersection A B of A and B is the set of all elements that are in both of A and B. ∩ n j=1Aj is the set of elements that are in at least one of the Aj. ∪n j=1Aj is the set of elements that are in all of the Aj. ∩∞ ∞ j=1Aj, j=1Aj are ... Two∩ sets A∪ and B are disjoint if A B = . denotes the empty set, the set with no elements. ∩ ∅ ∅ Complements: The complement of an event A, denoted Ac, is the set of outcomes (in Ω) which are not in A. Note that the book writes it as Ω A. De Morgan’s laws: \ (A B)c = Ac Bc ∪ ∩ (A B)c = Ac Bc ∩ ∪ c c ( Aj) = Aj j j [ \ c c ( Aj) = Aj j j \ [ (1) Definition 2. Let Ω be a sample space. A collection of subsets of Ω is a σ-field if F 1. -
Sample Space, Events and Probability
Sample Space, Events and Probability Sample Space and Events There are lots of phenomena in nature, like tossing a coin or tossing a die, whose outcomes cannot be predicted with certainty in advance, but the set of all the possible outcomes is known. These are what we call random phenomena or random experiments. Probability theory is concerned with such random phenomena or random experiments. Consider a random experiment. The set of all the possible outcomes is called the sample space of the experiment and is usually denoted by S. Any subset E of the sample space S is called an event. Here are some examples. Example 1 Tossing a coin. The sample space is S = fH; T g. E = fHg is an event. Example 2 Tossing a die. The sample space is S = f1; 2; 3; 4; 5; 6g. E = f2; 4; 6g is an event, which can be described in words as "the number is even". Example 3 Tossing a coin twice. The sample space is S = fHH;HT;TH;TT g. E = fHH; HT g is an event, which can be described in words as "the first toss results in a Heads. Example 4 Tossing a die twice. The sample space is S = f(i; j): i; j = 1; 2;:::; 6g, which contains 36 elements. "The sum of the results of the two toss is equal to 10" is an event. Example 5 Choosing a point from the interval (0; 1). The sample space is S = (0; 1). E = (1=3; 1=2) is an event. Example 6 Measuring the lifetime of a lightbulb. -
Notes for Math 450 Lecture Notes 2
Notes for Math 450 Lecture Notes 2 Renato Feres 1 Probability Spaces We first explain the basic concept of a probability space, (Ω, F,P ). This may be interpreted as an experiment with random outcomes. The set Ω is the collection of all possible outcomes of the experiment; F is a family of subsets of Ω called events; and P is a function that associates to an event its probability. These objects must satisfy certain logical requirements, which are detailed below. A random variable is a function X :Ω → S of the output of the random system. We explore some of the general implications of these abstract concepts. 1.1 Events and the basic set operations on them Any situation where the outcome is regarded as random will be referred to as an experiment, and the set of all possible outcomes of the experiment comprises its sample space, denoted by S or, at times, Ω. Each possible outcome of the experiment corresponds to a single element of S. For example, rolling a die is an experiment whose sample space is the finite set {1, 2, 3, 4, 5, 6}. The sample space for the experiment of tossing three (distinguishable) coins is {HHH,HHT,HTH,HTT,THH,THT,TTH,TTT } where HTH indicates the ordered triple (H, T, H) in the product set {H, T }3. The delay in departure of a flight scheduled for 10:00 AM every day can be regarded as the outcome of an experiment in this abstract sense, where the sample space now may be taken to be the interval [0, ∞) of the real line.