'Same probability distribution' presentation slideshows

817 views • 65 slides

Evaluating decadal hindcasts: why and how?

Evaluating decadal hindcasts: why and how?. Chris Ferro (University of Exeter) T. Fricker , F. Otto, D. Stephenson, E. Suckling. CliMathNet Conference (3 July 2013, Exeter, UK). Evaluating decadal hindcasts: why and how?. Chris Ferro (University of Exeter)

446 views • 32 slides

Normal Distributions

Chapter 6. Normal Distributions. Understandable Statistics Ninth Edition By Brase and Brase Prepared by Yixun Shi Bloomsburg University of Pennsylvania. The Normal Distribution. A continuous distribution used for modeling many natural phenomena.

399 views • 30 slides

Marieke Jesse and Maite Severins Theoretical Epidemiology Faculty of Veterinary Science Utrecht

Mathematical Epidemiology of infectious diseases Chapter 1 Section 1.1 until 1.3.3. Marieke Jesse and Maite Severins Theoretical Epidemiology Faculty of Veterinary Science Utrecht.

395 views • 30 slides

SUMS OF RANDOM VARIABLES

SUMS OF RANDOM VARIABLES. Changfei Chen. Sums of Random Variables. Let be a sequence of random variables, and let be their sum: . Mean and Variance of Sums of Random Variables.

1.44k views • 28 slides

Visual Cryptography

Moni Naor Adi Shamir. Visual Cryptography. Presented By: Salik Jamal Warsi Siddharth Bora. A very hot topic today which involves the following steps : Plain Text Encryption Cipher Text Channel Decryption Plain Text. Cryptography.

587 views • 26 slides

Generating Functions

Generating Functions. The Moments of Y. We have referred to E(Y) and E(Y 2 ) as the first and second moments of Y, respectively. In general, E(Y k ) is the k th moment of Y. Consider the polynomial where the moments of Y are incorporated into the coefficients. Moment Generating Function.

1.06k views • 18 slides

5. Combination of random variables

5. Combination of random variables. Understand why we need bottoms-up approach for reliability analysis

267 views • 18 slides

Generating Functions

Generating Functions. The Moments of Y. We have referred to E(Y) and E(Y 2 ) as the first and second moments of Y, respectively. In general, E(Y k ) is the k th moment of Y. Consider the polynomial where the moments of Y are incorporated into the coefficients. Moment Generating Function.

336 views • 17 slides

Ch. 17 Basic Statistical Models

Ch. 17 Basic Statistical Models. CIS 2033: Computational Probability and Statistics Prof. Longin Jan Latecki Prepared by: Nouf Albarakati. Basic Statistical Models. Random samples Statistical models Distribution features and sample statistics

323 views • 14 slides