PPT-Probability Lesson 4 .1 Randomness, Probability, and Simulation

4 Interpret probability as a longrun relative frequency Dispel common myths about randomness Use simulation to model chance behavior Randomness Probability and Simulation Randomness Probability and Simulation. Both Sides of the Coin. Dave Mark – Intrinsic Algorithm LLC. Brian Schwab – Blizzard. Dave Mark. President & Lead Designer . of . Intrinsic . Algorithm . LLC. Independent Game Studio. AI Consulting Company. . device. independent . randomness. . amplification. with . few. devices. F.G.S.L Brandao. 1. , R. . . Ramanathan. 2. . A. Grudka. 3. , K.. 4. , M.. 5. ,P.. 6. Horodeccy. 1. Department . of Computer Science, University College London. How can we draw the line?. Taner Edis. Department . of Physics,. . Truman . State . University. Supernatural fiction. Stories of ghosts, gods, spirits, magic, the occult.. Personality and agency (“spirit”) somehow fundamental to how the world works.. 5.1.1 Random Variables and Their Distributions. A random variable is a quantity that (prior to observation) can be thought of as dependent on chance phenomena. . . Toss a coin 10 times. X=# of heads. Upper and Lower Bounds. Matthew . Coudron. , Thomas . Vidick. , Henry Yuen. arXiv:1305.6626. The motivating question. Is it possible to test randomness?. The motivating question. Is it possible to test randomness?. 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 . 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. Matt . Coudron. and Henry Yuen. 6.845. 12/12/12. God does not play dice. . . --. Albert Einstein. Einstein, stop telling God what to do. . . --. Niels. Bohr. The Motivating Question. Is it possible to test randomness?. Randomness. ？. 隨機是指缺乏模式及可預測性的. 事件. e.g.. . 擲骰、六合彩、粒子運動等. 甚麼是機率. Probability. ？. 機率是用以描述隨機事件結果的數學. Xiaodi Wu. University of Oregon. 1. Yaoyun. Shi. . University of Michigan. Kai-. Min Chung. Academia . Sinica. 2. Right Time for Quantum Information Theorists to . . Jump Into Black-holes. 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.. Feb 18. th. , 2014. IQI Seminar, Caltech. Kai-Min Chung. . IIS, . Sinica,Taiwan. Yaoyun. Shi. . University of Michigan. Xiaodi Wu. . MIT/UC Berkeley. device. …….. Ext(. x,s. i. ). Ext(x,0). Decouple. Joanie Selman, MSN, RN. Med-. Surg. Course Coordinator. DeWitt School of Nursing. Stephen F. Austin State University. Background. DeWitt School of Nursing at . Stephen F. Austin State University. Lovett (IAS). Coding, Complexity and . Sparsity. workshop. August 2011. Overview. Pseudo-randomness – what? why?. Concrete examples. Local independence. Expander graphs. Small space computations.