PPT-Chapter 5: Random Variables and Discrete Probability Distributions

1 httpwwwlanderscoukstatisticscartoons 5152 Random Variables Goals Be able to define what a random variable is Be able to differentiate between discrete and continuous random variables. RANDOM VARIABLES Definition usually denoted as X or Y or even Z and it is th e numerical outcome of a random process Example random process The number of heads in 10 tosses of a coin Example The number 5 rating AS91586 Apply probability distributions in solving problems. NZC level 8. Investigate situations that involve elements of chance. calculating and interpreting expected values and standard deviations of discrete random variables. 1. 3. Discrete Random Variables and Probability Distributions. 3-1 Discrete Random Variables. 3-2 Probability Distributions and Probability Mass Functions. 3-3 Cumulative Distribution Functions. 3-4 Mean and Variance of a Discrete Random Variable. 1. http://www.landers.co.uk/statistics-cartoons/. 5.1-5.2: Random Variables - Goals. Be able to define what a random variable is.. Be able to differentiate between discrete and continuous random variables.. 1. Matt Gormley. Lecture 2. August 31, 2016. School of Computer Science. Readings:. Mitchell Ch. 1, 2, 6.1 – 6.3. Murphy Ch. 2. Bishop Ch. 1 - 2. 10-601 Introduction to Machine Learning. Reminders. . .. . . Week 05 . Tues. . .. MAT135 Statistics. Random Variables. A random variable . . Random Variables. A random variable . . “varies” . . (not always the same). Random Variables. A random variable . How . can it be that mathematics, being after all a product of human thought independent of experience, is so admirably adapted to the objects . of reality. Albert Einstein. Some parts of these slides were prepared based on . 1. 5. Joint Probability Distributions. 5-1 Two or More Random Variables. 5-1.1 Joint Probability Distributions. 5-1.2 Marginal Probability Distributions. 5-1.3 Conditional Probability Distributions. St. . Edward’s. University. .. .. .. .. .. .. .. .. .. .. .. Chapter 5. Discrete Probability Distributions. .10. .20. .30. .40. 0 . . 1 . . 2 3 4. Random Variables. http://www.answers.com/topic/binomial-distribution. Chapter 13: Bernoulli Random Variables. http://www.boost.org/doc/libs/1_42_0/libs/math/doc/sf_and_dist/html. /. math_toolkit. /. dist. /. dist_ref. How . can it be that mathematics, being after all a product of human thought independent of experience, is so admirably adapted to the objects . of reality. Albert Einstein. Some parts of these slides were prepared based on . smb@isa.ulisboa.pt. . Monte Carlo . Simulation. Forestry. . Applications. Applied. . Operations. Research . 2020-2021. 1. What is Monte Carlo? Basic Principles. 2. 3. Random Numbers. 4. Sample Sizes. Expected value for discrete data. 2 July 2020. The. . theoretical mean. , . μ. , of a discrete random variable . X. is the average value that we should expect for . X. over many trial of the experiment.. Nisheeth. Random Variables. 2. Informally, a random variable (. r.v.. ) . denotes possible outcomes of an event. Can be discrete (i.e., finite many possible outcomes) or continuous. Some examples of discrete .