'Rare events' presentation slideshows

Statistical Concepts and Methodologies for Data Analyses

Statistical Concepts and Methodologies for Data Analyses. Benilton Carvalho Computational Biology and Statistics Group Department of Oncology University of Cambridge. From Random Variables to Hypothesis Testing. Random Variables. Function that associates probability to:

806 views • 69 slides

Statistical Concepts and Methodologies for Data Analyses

Statistical Concepts and Methodologies for Data Analyses. Benilton Carvalho Computational Biology and Statistics Group Department of Oncology University of Cambridge. From Random Variables to Hypothesis Testing. Random Variables. Function that associates probability to:

810 views • 69 slides

By Brenda Crowe

Comparison of Bayesian and Frequentist Meta-Analytical Approaches for Analyzing Time to Event Data. By Brenda Crowe (Joint work with Monica Bennett, Karen Price, James Stamey and John Seaman Jr ) Midwest Biopharmaceutical Statistics Workshop 2014. Outline. Background/motivation

541 views • 41 slides

Chapter 4

Chapter 4. Discrete Random Variables. Discrete Random Variables. 4.1 Two Types of Random Variables 4.2 Discrete Probability Distributions 4.3 The Binomial Distribution 4.4 The Poisson Distribution 4.5 The Hypergeometric Distribution . Random Variables. L01. L02.

555 views • 39 slides

Data Discretization Unification

Data Discretization Unification. Ruoming Jin Yuri Breitbart Chibuike Muoh Kent State University, Kent, USA. Outline. Motivation Problem Statement Prior Art & Our Contributions Goodness Function Definition Unification Parameterized Discretization

561 views • 36 slides

A VIEW ON THE MATTER POWER AT MEDIUM SCALES MATTEO VIEL

460 views • 35 slides

Efficient Production of High Quality, Probabilistic Weather Forecasts F. Anthony Eckel

Efficient Production of High Quality, Probabilistic Weather Forecasts F. Anthony Eckel National Weather Service Office of Science and Technology, and University of WA Atmospheric Sciences Luca Delle Monache , Daran Rife, and Badrinath Nagarajan National Center for Atmospheric Research

333 views • 25 slides

SIMULATION FOR EDUCATION AND TRAINING IN ANESTHESIA AND CRITICAL CARE

SIMULATION FOR EDUCATION AND TRAINING IN ANESTHESIA AND CRITICAL CARE. Agenda. 1:00- 1:15 What is Medical Simulation and Why Do We Need It? Jeffrey Cooper, PhD 1:15- 1:45 How is Simulation Being Used in Anesthesia Education, Training, Patient Safety and Research? David Gaba, M.D.

1k views • 23 slides

Chapter 4. Discrete Probability Distributions Sections 4.7, 4.8: Poisson and Hypergeometric Distributions

Chapter 4. Discrete Probability Distributions Sections 4.7, 4.8: Poisson and Hypergeometric Distributions. Jiaping Wang Department of Mathematical Science 03/04/2013, Monday. Outline. Poisson: Probability Function Poisson: Mean and Variance Hypergeometric : Probability Function

433 views • 23 slides

Safety Review

410 views • 21 slides

ALICE

396 views • 21 slides

Decision Making and Reasoning

1.04k views • 20 slides

Stream morphology and hydrology

Stream morphology and hydrology. Hydraulic geometry. Typical at a station values (midwestern streams). b = 0.25 f = 0.4 m = 0.35. B = aQ b h = cQ f U = kQ m. Typical downstream values. b = 0.5 f = 0.4 m = 0.1. Hydrology short overview. Stream records Flow duration curves

318 views • 16 slides

A News-Based Approach for Computing Historical Value-at-Risk

A News-Based Approach for Computing Historical Value-at-Risk. Introduction (1). Value-at-Risk ( VaR ) is a threshold value, such that the probability of future returns exceeding this threshold is at a given confidence level

268 views • 14 slides

Rare Events, Probability and Sample Size

Rare Events, Probability and Sample Size. Rare Events An event E is rare if its probability is very small , that is, if Pr{E} ? 0. Rare events require large samples to ensure proper observation. Minimal Sample Size as a Function of Probability

243 views • 14 slides

6.10 - Discrete Random Variables: The Poisson Distribution

6.10 - Discrete Random Variables: The Poisson Distribution. IBHLY1/Y2 - Santowski. (A) The Poisson Distribution. the Poisson distribution resembles the binomial distribution in that is models counts of events .

404 views • 12 slides

Perception of Risk Posed by Extreme Events

Perception of Risk Posed by Extreme Events. Risk Management Strategies in an Uncertain World Kim B. Staking. Risk Analysis. Risk Assessment Risk Management Politics of Risk

523 views • 10 slides

ALICE Computing Resources requirements

ALICE Computing Resources requirements. WLCG OB 18 March 2011. USAGE in 2010. Data collected (filtered by physics trigger) Data processing. Reconstruction: 10% MC & Trains: 75% End users A: 15%. Storage filling up quickly. Memory profile PbPb reco. New in the Computing model.

166 views • 8 slides

1.

1. . Unexpected or rare events have more newsworthiness than routine events because they are out of the ordinary. 2. . The bigger the event the more likely it will be nationally reported. . 3. .

146 views • 6 slides

The Poisson Probability Distribution

677 views • 4 slides