1 / 38

Review of Final Part I Sections 2.2 -- 4.5

Review of Final Part I Sections 2.2 -- 4.5. Jiaping Wang Department of Mathematics 02/29/2013, Monday. Outline. Sample Space and Events Definition of Probability Counting Rules Conditional Probability and Independence Probability Distribution and Expected Values

sema
Download Presentation

Review of Final Part I Sections 2.2 -- 4.5

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Review of Final Part ISections 2.2 -- 4.5 Jiaping Wang Department of Mathematics 02/29/2013, Monday

  2. Outline Sample Space and Events Definition of Probability Counting Rules Conditional Probability and Independence Probability Distribution and Expected Values Bernoulli, Binomial and Geometric Distributions Negative Binomial, Poisson, Hypergeometric Distributions and MGF

  3. Part 1. Sample Space and Events

  4. Definition 2.1 A sample space S is a set that includes all possible outcomes for a random experiment listed in a mutually exclusive and exhaustive way. Mutually Exclusive means the outcomes of the set do not overlap. Exhaustive means the list contains all possible outcomes. Definition 2.2: An event is any subset of a sample space.

  5. Event Operators and Venn Diagram There are three operators between events: Intersection: ∩ --- A∩B or AB – a new event consisting of common elements from A and B Union: U --- AUB – a new event consisting of all outcomes from A or B. Complement: ¯, A, -- a subset of all outcomes in S that are not in A. S S S AUB A∩B A

  6. Some Laws Commutative laws: Associate laws: Distributive laws: DeMorgan’s laws:

  7. Part 2. Definition of Probability

  8. Suppose that a random experiment has associated with a sample space S. A probability is a numerically valued function that assigned a number P(A) to every event A so that the following axioms hold: (1) P(A) ≥ 0 (2) P(S) = 1 (3) If A1, A2, … is a sequence of mutually exclusive events (that is Ai∩Aj=ø for any i≠j), then

  9. Some Basic Properties 1. P( ø ) = 0, P(S) = 1. 2. 0≤ P(A) ≤1for any event A. 3. P(AUB) = P(A) + P(B) if A and B are mutually exclusively. 4. P(AUB) = P(A) + P(B) – P(A∩B) for general events A and B. 5. If A is a subset of B, then P(A) ≤ P(B). 6. P(A) = 1 – P(A). 7. P(A∩B) = P(A) – P(A∩B).

  10. Inclusive-Exclusive Principle Theorem 2.1. For events A1, A2, …, An from the sample space S, We can use induction to prove this.

  11. Determine the Probability Values The definition of probability only tells us the axioms that the probability function must obey; it doesn’t tell us what values to assign to specific event. The value of the probability is usually based on empirical evidence or on careful thought about the experiment. For example, if a die is balanced, then we may think P(Ai)=1/6 for Ai={ i }, i = 1, 2, 3, 4, 5, 6 However, if a die is not balanced, to determine the probability, we need run lots of experiments to find the frequencies for each outcome.

  12. Part 3. Counting Rules

  13. Theorem 2.2 Fundamental Principle of Counting: If the first task of an experiment can result in n1 possible outcomes and for each such outcome, the second task can result in n2 possible outcomes, then there are n1n2 possible outcomes for the two tasks together. The principle can extend to more tasks in a sequence.

  14. Order and Replacement

  15. Theorem 2.5 Partitions Consider a case: If we roll a die for 12 times, how many possible ways to have 2 1’s, 2 2’s, 3 3’s, 2 4’s, 2 5’s and 1 6’s? Solution: First, choose 2 1’s from 12 which gives 12!/(2!10!), second, since there are two positions are filled by 1’s, the next choice appears in the left 10 positions, so there are 10!/(8!2!) ways, and so similar for next other selections which provides the final result is 12!/(2!10!)x10!/(2!8!)x8!/(3!5!)x5!/(2!3!)x3!/(2!1!)x1!/(1!0!) =12!/(2!x2!x3!x2!x2!x1!) Theorem 2.5 Partitions. The number of partitioning n distinct objects into k groups containing n1, n2,•••, nk objects, respectively, is

  16. Part 4. Conditional Probability and Independence

  17. Definition 3.1 If A and B are any two events, then the conditional probability of A given B, denoted as P(A|B), is Provided that P(B)>0. Notice that P(A∩B) = P(A|B)P(B) or P(A∩B) = P(B|A)P(A). This definition also follows the three axioms of probability. A∩B is a subset of B, so P(A∩B )≤P(B), then 0≤P(A|B)≤1; P(S|B)=P(S∩B)/P(B)=P(B)/P(B)=1; If A1, A2, …, are mutually exclusively, then so are A1∩B, A2 ∩B, …; and P(UAi|B) = P((UAi) ∩B)/P(B)=P(U(Ai ∩B)/P(B)=∑P(Ai ∩B)/P(B)= ∑P(Ai|B).

  18. Definition 3.2 and Theorem 3.2 Definition 3.2: Two events A and B are said to be independent if P(A∩B)=P(A)P(B). This is equivalent to stating that P(A|B)=P(A), P(B|A)=P(B) If the conditional probability exist. Theorem 3.2: Multiplicative Rule. If A and B are any two events, then P(A∩B) = P(A)P(B|A) = P(B)P(A|B) If A and B are independent, then P(A∩B) = P(A)P(B).

  19. Theorem of Total Probability: If B1, B2, …, Bk is a collection of mutually exclusive and exhaustive events, then for any event A, we have Bayes’ Rule. If the events B1, B2, …, Bk form a partition of the sample space S, and A is any event in S, then

  20. Part 5. Probability Distribution and Expected Value

  21. A random variable is a real-valued function whose domain is a sample space. A random variable X is said to be discrete if it can take on only a finite number – or a countably infinite number – of possible values x. The probability function of X, denoted by p(x), assigns probability to each value x of X so that the following conditions hold: P(X=x)=p(x)≥0; ∑ P(X=x) =1, where the sum is over all possible values of x.

  22. The distribution function F(b) for a random variable X is F(b)=P(X ≤ b); If X is discrete, Where p(x) is the probability function. The distribution function is often called the cumulative distribution function (CDF). Any function satisfies the following 4 properties is a distribution function: 1. 2. 3. The distribution function is a non-decreasing function: if a<b, then F(a)≤ F(b). The distribution function can remain constant, but it can’t decrease as we increase from a to b. 4. The distribution function is right-hand continuous:

  23. Definition 4.4 Definition 4.4 The expected value of a discrete random variable X with probability distribution p(x) is given as (The sum is over all values of x for which p(x)>0) We sometimes use the notation E(X)=μ for this equivalence. Note: Not all expected values exist, the sum above must converge absolutely, ∑|x|p(x)<∞. Theorem 4.1 If X is a discrete random variable with probability p(x) and if g(x) is any real-valued function of X, then E(g(x))=∑g(x)p(x).

  24. Definitions 4.5 and 4.6 The variance of a random variable X with expected value μ is given by V(X)=E[(X- μ)2] Sometimes we use the notation σ2 = E[(X- μ)2] For this equivalence. The standard deviation is a measure of variation that maintains the original units of measure. The standard deviation of a random variable is the square root of the variance and is given by

  25. Theorem 4.2 For any random variable X and constants a and b. E(aX + b) = aE(X) + b V(aX + b) = a2V(X) Standardized random variable: If X has mean μ and standard deviation σ, then Y=(X – μ)/ σ has E(Y)=0 and V(Y)=1, thus Y can be called the standardized random variable of X. Theorem 4.3 If X is a random variable with mean μ, then V(X)= E(X2) – μ2 Tchebysheff’s Theorem. Let X be a random variable with mean μ and standard deviation σ. Then for any positive k, P(|X – μ|/ σ < k) ≥ 1-1/k2

  26. Part 6. Bernoulli, Binomial and Geometric Distribution

  27. Bernoulli Distribution Let the probability of success is p, then the probability of failure is 1-p, the distribution of X is given by p(x)=px(1-p)1-x, x=0 or 1 Where p(x) denotes the probability that X=x. E(X) = ∑xp(x) = 0p(0)+1p(1)=0(1-p)+p= p  E(X)=p V(X)=E(X2)-E2(X)= ∑x2p(x) –p2=0(1-p)+1(p)-p2=p-p2=p(1-p)  V(X)=p(1-p)

  28. Binomial Distribution Suppose we conduct n independent Bernoulli trials, each with a probability p of success. Let the random variable X be the number of successes in these n trials. The distribution of X is called binomial distribution. Let Yi = 1 if ith trial is a success = 0 if ith trial is a failure, Then X=∑ Yi denotes the number of the successes in the n independent trials. So X can be {0, 1, 2, 3, …, n}. For example, when n=3, the probability of success is p, then what is the probability of X?

  29. Cont. The mass function of binomial distribution: From the binomial formula, we can have = A random variable X is a binomial distribution if 1. The experiment consists of a fixed number n of identical trials. 2. Each trial only have two possible outcomes, that is the Bernoulli trials. 3. The probability p is constant from trial to trial. 4. The trials are independent. 5. X is the number of successes in n trails.

  30. E(X)=np Bernoulli random variables Y1, Y2, …, Yn, then V(X)=np(1-p) Bernoulli random variables Y1, Y2, …, Yn, then V

  31. Geometric Distribution: Probability Function The geometric distribution function: P(X=x)=p(x)=(1-p)xp=qxp, x= 0, 1, 2, …., q=1-p P(X=x) = qxp = p[qx-1p] = qP(X=x-1) <P(X=x-1) as q ≤ 1, for x=1, 2, … A Geometric Distribution Function with p=0.5

  32. Geometric Series and CDF The geometric series: {tx: x=0, 1, 2, …} Sum of Geometric series: For |t|<1, we have = Sum of partial series: = Then we can verify The cumulative distribution function: F(x)=P(X≤x)===1-qx+1 And P(X≥x)=1-F(x-1)=qx

  33. Mean and Variance The Expected Value E(X)= The Variance V(X)= E(X)= So E(X)/(pq) = And E(X)/p = [0 + q + 2q2 + … ] Thus, E(X)/(pq)-E(X)/p = 1+q+q2+q3+ • • • = 1/(1-q)  E(X)=

  34. Part7. Negative Binomial, Poisson, HypergeometricDistributions and MGF

  35. Negative Binomial Distribution What if we were interested in the number of failures prior to the second success, or the third success or (in general) the r-th success? Let X denote the number of failures prior to the r-th success, p denotes the common probability. The negative binomial distribution function: P(X=x)=p(x)=, x= 0, 1, 2, …., q=1-p If r=1, then the negative binomial distribution becomes the geometric distribution. In summary,

  36. PoissonDistribution The Poisson probability function: P(X=x)=p(x)=, x= 0, 1, 2, …., for λ> 0 The distribution function is F(x)=P(X≤x)= Recall that λ denotes the mean number of occurrences in one time period, if there are t non-overlapped time periods, then the mean would be λt. Poisson distribution is often referred to as the distribution of rare events. E(X)= V(X) = λfor Poisson random variable.

  37. Hypergeometric Distribution Now we consider a general case: Suppose a lot consists of N items, of which k are of one type (called successes) and N-k are of another type (called failures). Now n items are sampled randomly and sequentially without replacement. Let X denote the number of successes among the n sampled items. So What is P(X=x) for some integer x? The probability function is: P(X=x) = p(x) = Which is called hypergeometric probability distribution.

  38. Moment Generating Function The k-th moment is defined as E(Xk)=∑xkp(x). For example, E(X) is the 1st moment, E(X2) is the 2nd moment. The moment generating function is defined as M(t)=E(etX) So we have M(k)(0)=E(Xk). For example, So if set t=0, then M(1)(0)=E(X). It often is easier to evaluate M(t) and its derivatives than to find the moments of the random variable directly.

More Related