Material conditional

60) from the last great earthquake of 2001 in Figure 3(a,b,c) for Weibull, Gamma and Lognormal models, respectively. These are the cumulative conditional probabilities that an earthquake will have occurred at a time = t +  after the last earthquake if it has not occurred at a time t. It is seen from these curves that the time difference between curves for different values of t at a particular probability

CSIT 270 Homework 6 Q1.1 Ans 1.1 If S is a finite sample space of equal likely outcomes and E is an event , that is a subset of S Then the probability of E is : P(E) = The probability that a five-card poker hand does not contain the queen of hearts is determined as follows: If 5 cards are drawn then chances of not getting the queen of hearts in the first draw is If there is no chance getting the queen of hearts in drawn and the chance of not getting it in 2nd draw is If there is no chance

Robotic Emotions Time will inevitably affects the way we view the world around us. As time moves on so to does ones opinions and views on the world around. Transitioning through age also affects our view on reality. In Alison Gopnik’s “Possible Worlds: Why Do Children Pretend?” she shows us the difference between how children and adults perceive things. At the same time in Sherry Turkle’s “Alone Together” we are shown how growing technology affects are views on reality. When one combines the ideas

AIM OF THE EXPLORATION Way of scoring in Tennis makes it a different from other games. The unique point is that the scoring at the point level is not cumulative and hence, it is possible for a player scoring less points than her or his opponent to win a match. In this portfolio, we will explore  How to construct a probabilistic model for a tennis match in which the probabilities of winning points are used to analyze the probability of winning matches.  Charts, which will illustrate the

A method of using MRF model in image segmentation Abstract Machine vision is a high-speed developing field in AI, and image identification is becoming more and more important with higher and higher requires of AI. As a big part of image identification, it is abviously necessary to develop better solution in image segmentation, thus machine could identity objects easier. The division technique for pictures based on Markov Random Field (MRF) demonstrate ready to combine the contextual information from

can be calculated using the given formula. This assumption results in Bayesian Network. A Bayesian network is a directed acyclic graph containing nodes and edges, where nodes denote the random variables and the edges denote the conditional dependencies. It is a conditional exponential classifier that takes the feature sets with label and converts them into

Bayes’ Theorem and the Monty Hall Problem Faizan Hasan Introduction The Monty Hall problem is that has troubled many, including myself when I first heard of it. The Monty Hall problem is based on a game show from the 1960’s, named Let’s Make a Deal, where the host Monty Hall offers a contestant a choice to choose between three doors. Behind two of which are goats and the other one contains a very desirable car. He asks the contestant to choose one door where they would

Conditional Probability Huafeng Zhang I. Introduction Everyone in a way use conditional probability even if they don’t realize the science behind it. Non-statisticians use conditional probability without recognizing it mostly when they make decisions or judgments, for example they will be less likely to lend money to people who borrowed his money but haven 't returned the money back. Statisticians think of conditional probability in both logical and quantitative way. II. Conditional Probability

Investigating and Expanding the Monty Hall Problem ___________________________________________________________ TABLE OF CONTENTS Chapter 1 Page 3 Introduction _____________________________________________________________________ Chapter 2 Page 5 Analyzing the problem _____________________________________________________________________

Rainfall induced landslide probability mapping for central province 1Edward H. Waithaka, Jomo Kenyatta University of Agriculture and Technology, Department of Geomatic Engineering & Geospatial Information Systems. P.o. Box 62000-00200, Nairobi, Kenya Email: hunja@eng.jkuat.ac.ke 2 Thomas G. Ngigi Email: tomngigi@hotmail.com 3Mercy W. Mwaniki, Email: mercimwaniki@yahoo.com Abstract Rainfall induced landslide hazards in Kenya represents a major challenge and remain an important issue in disaster