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Unformatted text preview: Lecture 1: A rapid overview of probability theory Biology 429 Carl Bergstrom January 2, 2008 Sources: These lecture notes draw upon material from Taylor and Karlin An Introduction to Stochastic Modeling 3rd Edition (1998), Parzen (1962) Stochastic Processes , Pitman (1993) Probability , Van Campen (1997) Stochastic Processes in Physics and Chemistry , and Dill and Bromberg (2003) Molecular Driving Forces . In places, definitions and statements of theorems may be taken directly from these sources. Probability theory can be developed in a completely formal, rigorous fashion grounded in set theory and measure theory; this is known as ax iomatic probability theory . Alternatively, one can be develop probability theory in a commonsense fashion, allowing our our basic intuitions about chance to replace much of the formalism. I will take the latter approach throughout. 1 Discrete probability 1.1 Random Variables Definition 1 A random variable is a variable that takes on its value prob abilistically. A random variable X is defined by a set of possible values x , and a distri bution of probabilities p ( x ) over this set, such that 1 1. p ( x ) 0 for all x , and 2. x p ( x ) = 1 . For example, if X is the random variable representing the outcome of rolling a fair die, the random variable X has a set of possible values { 1 , 2 , 3 , 4 , 5 , 6 } each with probability p ( x ) = 1 / 6. If X drawn is from dis tribution F, we write x F . In this die example, X is a discrete random variable because it takes one of a set of discrete values. Random variables can also be continuous; they can take on any of a set of continuous val ues. Here we will treat discrete random variables first, and then move on to continuous random variables. 1.2 Events Closely related to the notion of a random variable is the concept of an event. Definition 2 An event is the case that a random variable takes on a value within a described subset of possible values. For example, the event that I roll a die and get an odd number can be represented as the event X { 1 , 3 , 5 } . Events can include single values of random variables, e.g. the event that X = 4; they can include all possible values X { 1 , 2 ,..., 6 } , and they can include no possible values X {} . If events A 1 ,A 2 ,...,A n are mutually exclusive events with probabilities P [ A i ], the probability that any one of them occurs is P [ A 1 or A 2 ... or A n ] = X i P [ A i ] . 1.3 Conditional probability Conditional probability lets us talk about the chance that one event occurs given that another occurs. Definition 3 The conditional probability of A given B is P [ A  B ] = P [ A and B ] P [ B ] 2 (We will write P [ AB ] as shorthand for P [ A and B ]). Note that we can also write P [ AB ] = P [ A  B ] P [ B ] We can extend this to write down a chain rule for probabilities. This rule tells us the probability that a series of events A 1 , A 2 ..., A n happen in succession....
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This note was uploaded on 02/03/2012 for the course ENGR 202 taught by Professor Hi during the Spring '11 term at UCLA.
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