'Random variables x' presentation slideshows

STOCHASTIC HYDROLOGY Bivariate Simulation

STOCHASTIC HYDROLOGY Bivariate Simulation. Bivariate normal Bivariate exponential Bivariate gamma. Professor Ke-Sheng Cheng Department of Bioenvironmental Systems Engineering National Taiwan University.

913 views • 63 slides

STT 315

STT 315. Ashwini Maurya. This lecture is based on Chapter 4.6-4.11. Acknowledgement: Author is thankful to Dr. Ashok Sinha, Dr . Jennifer Kaplan and Dr. Parthanil Roy for allowing him to use/edit some of their slides. Normal Distribution. Normal random variable.

703 views • 57 slides

Sampling Distributions

Sampling Distributions. Central Limit Theorem. Objectives. Investigate the variability in sample statistics from sample to sample. Find measures of central tendency for distribution of sample statistics. Find measures of dispersion for distribution of sample statistics.

625 views • 54 slides

Information Theoretic Clustering, Co-clustering and Matrix Approximations Inderjit S. Dhillon Uni

Information Theoretic Clustering, Co-clustering and Matrix Approximations Inderjit S. Dhillon University of Texas, Austin. Joint work with A. Banerjee, J. Ghosh, Y. Guan, S. Mallela,

707 views • 39 slides

Probability theory

511 views • 37 slides

427 views • 30 slides

Independence of random variables

Independence of random variables. Definition Random variables X and Y are independent if their joint distribution function factors into the product of their marginal distribution functions Theorem

1k views • 29 slides

Advanced Concepts in Expectation

Advanced Concepts in Expectation. Expectation of a Random Variable. Discrete distribution Continuous distribution E ( X ) is called expected value , mean or expectation of X . E ( X ) can be regarded as being the center of gravity of that distribution. E ( X ) exists if and only if

519 views • 27 slides

Joint Distribution of two or More Random Variables

Joint Distribution of two or More Random Variables. Sometimes more than one measurement (r.v.) is taken on each member of the sample space. In cases like this there will be a few random variables defined on the same probability space and we would like to explore their joint distribution.

344 views • 23 slides

Multivariate Distributions

Multivariate Distributions. CIVL 7012/8012. Multivariate Distributions. Engineers often are interested in more than one measurement from a single item. Multivariate distributions describe the probability of events defined in terms of multiple random variables. Joint Probability Distributions.

526 views • 22 slides

Learning In Bayesian Networks

310 views • 18 slides

Joint and marginal distribution functions

Joint and marginal distribution functions. For any two random variables X and Y defined on the same sample space, the joint c.d.f. is For an example, see next slide. The marginal distributions can be obtained from the joint distributions as follows:

2.22k views • 16 slides

P. STATISTICS LESSON 7.2 ( DAY 2)

P. STATISTICS LESSON 7.2 ( DAY 2). STATISTICAL INFORMATION AND THE LAW OF LARGE NUMBERS. ESSENTIAL QUESTION: What is the law of large numbers and how does it work?. To understand the law of large numbers. To dispel the law of small numbers. To define the rules of means.

276 views • 15 slides

Lecture 9

241 views • 14 slides

Chapter 7

186 views • 8 slides

Stat 13 Lecture 22 comparing proportions

Stat 13 Lecture 22 comparing proportions. Estimation of population proportion Confidence interval ; hypothesis testing Two independent samples One sample, competitive categories (negative covariance) One sample, non-competitive categories (usually, positive covariance). An Example.

215 views • 7 slides