CSE 221: Probabilistic Analysis of Computer Systems Topics covered: Special discrete distributions Sec. 2.5.1.-2.5.4.

Slides:



Advertisements
Similar presentations
ELEC 303, Koushanfar, Fall’09 ELEC 303 – Random Signals Lecture 6 – More Discrete Random Variables Farinaz Koushanfar ECE Dept., Rice University Sept 10,
Advertisements

Discrete Uniform Distribution
Review of Basic Probability and Statistics
Chapter 1 Probability Theory (i) : One Random Variable
CSE 221: Probabilistic Analysis of Computer Systems Topics covered: Conditional probability Independent events Bayes rule Bernoulli trials (Sec )
CSE 221: Probabilistic Analysis of Computer Systems Topics covered: Expectation of random variables Moments (Sec )
CSE 221: Probabilistic Analysis of Computer Systems Topics covered: Discrete random variables Probability mass function Distribution function (Secs )
CSE 221: Probabilistic Analysis of Computer Systems Topics covered: Independent events Bayes rule Bernoulli trials (Sec )
CSE 3504: Probabilistic Analysis of Computer Systems Topics covered: Probability axioms Combinatorial problems (Sec )
CSE 221: Probabilistic Analysis of Computer Systems Topics covered: Pure death process Availability analysis (Sec , 8.4.1)
CSE 3504: Probabilistic Analysis of Computer Systems Topics covered: Moments and transforms of special distributions (Sec ,4.5.3,4.5.4,4.5.5,4.5.6)
Probability Distributions
CSE 221: Probabilistic Analysis of Computer Systems Topics covered: Multiple random variables Transform methods (Sec , 4.5.7)
CSE 221: Probabilistic Analysis of Computer Systems Topics covered: Confidence intervals.
1 Review of Probability Theory [Source: Stanford University]
CSE 221: Probabilistic Analysis of Computer Systems Topics covered: Statistical inference (Sec. )
CSE 221: Probabilistic Analysis of Computer Systems Topics covered: Course outline and schedule Introduction Event Algebra (Sec )
CSE 221: Probabilistic Analysis of Computer Systems Topics covered: Statistical inference.
CSE 221: Probabilistic Analysis of Computer Systems Topics covered: Continuous random variables Uniform and Normal distribution (Sec. 3.1, )
CSE 221: Probabilistic Analysis of Computer Systems Topics covered: Exponential distribution Reliability and failure rate (Sec )
CSE 221: Probabilistic Analysis of Computer Systems Topics covered: Combinatorial problems Conditional probability Independent events (Sec , )
CSE 221: Probabilistic Analysis of Computer Systems Topics covered: Event algebra Probability axioms Combinatorial problems (Sec )
A random variable that has the following pmf is said to be a binomial random variable with parameters n, p The Binomial random variable.
CSE 221: Probabilistic Analysis of Computer Systems Topics covered: Course outline and schedule Introduction (Sec )
CSE 221: Probabilistic Analysis of Computer Systems Topics covered: Discrete time Markov chains (Sec )
CSE 3504: Probabilistic Analysis of Computer Systems Topics covered: Discrete time Markov chains (Sec )
2. Random variables  Introduction  Distribution of a random variable  Distribution function properties  Discrete random variables  Point mass  Discrete.
C4: DISCRETE RANDOM VARIABLES CIS 2033 based on Dekking et al. A Modern Introduction to Probability and Statistics Longin Jan Latecki.
CSE 221: Probabilistic Analysis of Computer Systems Topics covered: Stochastic processes Bernoulli and Poisson processes (Sec. 6.1,6.3.,6.4)
CSE 221: Probabilistic Analysis of Computer Systems Topics covered: Discrete time Markov chains (Sec. 7.1)
CSE 221: Probabilistic Analysis of Computer Systems Topics covered: Statistical inference.
Chapter 21 Random Variables Discrete: Bernoulli, Binomial, Geometric, Poisson Continuous: Uniform, Exponential, Gamma, Normal Expectation & Variance, Joint.
1 Performance Evaluation of Computer Systems By Behzad Akbari Tarbiat Modares University Spring 2009 Introduction to Probabilities: Discrete Random Variables.
General information CSE : Probabilistic Analysis of Computer Systems
CSE 531: Performance Analysis of Systems Lecture 2: Probs & Stats review Anshul Gandhi 1307, CS building
Section 15.8 The Binomial Distribution. A binomial distribution is a discrete distribution defined by two parameters: The number of trials, n The probability.
Winter 2006EE384x1 Review of Probability Theory Review Session 1 EE384X.
Statistics for Engineer Week II and Week III: Random Variables and Probability Distribution.
1 Bernoulli trial and binomial distribution Bernoulli trialBinomial distribution x (# H) 01 P(x)P(x)P(x)P(x)(1 – p)p ?
The Negative Binomial Distribution An experiment is called a negative binomial experiment if it satisfies the following conditions: 1.The experiment of.
Random Variables. A random variable X is a real valued function defined on the sample space, X : S  R. The set { s  S : X ( s )  [ a, b ] is an event}.
COMP 170 L2 L17: Random Variables and Expectation Page 1.
King Saud University Women Students
Week 21 Conditional Probability Idea – have performed a chance experiment but don’t know the outcome (ω), but have some partial information (event A) about.
Math b (Discrete) Random Variables, Binomial Distribution.
IE 300, Fall 2012 Richard Sowers IESE. 8/30/2012 Goals: Rules of Probability Counting Equally likely Some examples.
Topic 3 - Discrete distributions Basics of discrete distributions - pages Mean and variance of a discrete distribution - pages ,
By Satyadhar Joshi. Content  Probability Spaces  Bernoulli's Trial  Random Variables a. Expectation variance and standard deviation b. The Normal Distribution.
Random Variables Example:
C4: DISCRETE RANDOM VARIABLES CIS 2033 based on Dekking et al. A Modern Introduction to Probability and Statistics Longin Jan Latecki.
Discrete Random Variable Random Process. The Notion of A Random Variable We expect some measurement or numerical attribute of the outcome of a random.
Conditional probability
Probabilistic Analysis of Computer Systems
Ch3.5 Hypergeometric Distribution
Random variables (r.v.) Random variable
Random Variables.
C4: DISCRETE RANDOM VARIABLES
STAT 312 Chapter 7 - Statistical Intervals Based on a Single Sample
Chapter 3 Discrete Random Variables and Probability Distributions
Probability & Statistics Probability Theory Mathematical Probability Models Event Relationships Distributions of Random Variables Continuous Random.
Econometric Models The most basic econometric model consists of a relationship between two variables which is disturbed by a random error. We need to use.
Some Discrete Probability Distributions
Distributions and expected value
Useful Discrete Random Variable
Bernoulli Trials Two Possible Outcomes Trials are independent.
CSE 321 Discrete Structures
Discrete Random Variables: Basics
Discrete Random Variables: Basics
Discrete Random Variables: Expectation, Mean and Variance
Discrete Random Variables: Basics
Presentation transcript:

CSE 221: Probabilistic Analysis of Computer Systems Topics covered: Special discrete distributions Sec

Bernoulli pmf  What is a Bernoulli trial?  Sample space:  Bernoulli random variable:

Bernoulli pmf (contd..)  pmf:  Distribution function:  Parameters:

Bernoulli pmf (contd..)  Where to use:  Example: Tossing a biased coin.

Binomial pmf  Relationship between Bernoulli and Binomial random variables:  Sample space:  Definition of Binomial random variable:

Binomial pmf (contd..)  pmf:  Distribution function:  Parameters of a Binomial distribution:

Binomial pmf (contd..)  Where to use:  Example: Sequence of three coin tosses:

Geometric pmf  Relation between Bernoulli and Geometric pmf:  Sample space:  Definition of Geometric pmf:

Geometric pmf (contd..)  pmf:  Distribution function:  Parameters of a Geometric distribution:

Modified geometric pmf  Relation between Bernoulli pmf, geometric pmf and modified geometric pmf:  Sample space:  Definition of modified geometric pmf:

Modified geometric pmf (contd..)  pmf:  Distribution function:  Parameters of a modified geometric distribution:

Modified geometric pmf (contd..)  Where to use:  Example:

Geometric distribution: Memoryless property  Definition: