PPT-Experimental Probability and Simulation

A simulation imitates a real situation Is supposed to give similar results And so acts as a predictor of what should actually happen It is a model in which repeated experiments are carried out for the purpose of estimating in real life. 1. Simulation. Summer 2013. Simulation. 2. Many definitions.. It is the process . of studying the behavior of a real . system using . a computer-based model that replicates the behavior of that system.. th. edition – For AP*. STARNES, YATES, MOORE. Chapter 5: Probability: What are the Chances?. Section 5.1. Randomness, Probability, and Simulation. Chapter 5. Probability: What Are the Chances?. 5.1 . Monte . carlo. simulation. 1. Arwa Ibrahim Ahmed. Princess Nora University. EMPIRICAL PROBABILITY AND AXIOMATIC PROBABILITY. :. 2. • The main characterization of Monte Carlo simulation system is being . Lars Thomsen, Avondale College . Re-. conceptualising. probability. What has changed?. Changes in the Probability Standard. Old 2.6. New 2.13. Simulation . Tree diagrams. (old Level 1 probability). Simulate probability situations. Experimental probability. : . Probability based on a collection of data.. Will have a table of results or data from the experiment(s)!. What is the difference between . theoretical probability. and . 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 . 3.1 . The Concept of Probability. 3.2 . Sample Spaces and Events. 3.3 . Some Elementary Probability Rules. 3.4 . Conditional Probability and Independence. 3.5 . Bayes’ Theorem. 3-. 2. Probability Concepts. 4. Introduction. (slide 1 of 3). A key . aspect of solving real business problems is dealing appropriately with uncertainty.. This involves recognizing explicitly that uncertainty exists and using quantitative methods to model uncertainty.. Section 5.1. Randomness, Probability, and Simulation. HAPPY HALLOWEEN!!!!!!. Example 1: . When you toss a coin, there are only two possible outcomes, heads or tails. The figure below on the left shows the results of tossing a coin 20 times. For each number of tosses from 1 to 20, we have plotted the proportion of those tosses that gave a head. You can see that the proportion of heads starts at 1 on the first toss, falls to 0.5 when the second toss gives a tail, then rises to 0.67, and then falls to 0.5, and 0.4 as we get two more tails. After that, the proportion of heads continues to fluctuate but never exceeds 0.5 again.. Starnes, Tabor, Yates, Moore . Bedford Freeman Worth Publishers. CHAPTER 5. Probability: What Are . the Chances?. 5.1. Randomness, Probability, . and Simulation. Learning Objectives. After this section, you should be able to:. Sixth Edition. Douglas C. Montgomery George C. . Runger. Chapter 2 Title and Outline. 2. 2. Probability. 2-1 Sample Spaces and Events . 2-1.1 Random Experiments. 2-1.2 Sample Spaces . probability of simple events. Why: . To calculate the probability of simple events and to analyze the difference . between theoretical probability and experimental probability.. Vocabulary:. . Probability– . Dr. Chris L. S. . Coryn. Kristin . A. Hobson. Fall 2013. Agenda. Course overview. Introductions. Course pretest examination. Discussion . and questions. Course Material. Website. http. ://www.wmich.edu/evalphd/courses/eval-6970-experimental-and-quasi-experimental-designs-for-applied-research-and-evaluation. Rustom D. Sutaria – Avia Intelligence 2016 , Dubai Introduction Risk analysis is an increasing part of every decision we make where aircraft maintenance planning & reliability are concerned . A