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    Software Testing

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    A JOURNAL OF LTEX CLASS FILES‚ VOL. 6‚ NO. 1‚ JANUARY 2007 1 Enhancing Performance of Random Testing Through Markov Chain Monte Carlo Methods Bo Zhou‚ Hiroyuki Okamura‚ Member‚ IEEE‚ and Tadashi Dohi‚ Member‚ IEEE Abstract—In this paper‚ we propose a probabilistic approach to finding failure-causing inputs based on Bayesian estimation. According to our probabilistic insights of software testing‚ the test case generation algorithms are developed by Markov chain Mote Carlo (MCMC) methods. Dissimilar

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    Finance Mangerial

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    Sofia Yanez Malik Homework6 1. What is a prior probability? Prior probability is the  probability that is initially calculated based on the information or data that is available in that moment. 2. Explain the purpose behind using Bayes Theorem The purpose of the Bayes’ theorem is to revise previously calculated probabilities based on new information. 19.36 Again consider the oil company case that was described in Example 19.1. Recall that the oil company wishes to decide whether to drill

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    Barbie Sentences

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    The idea of what my future career may be often changed throughout my childhood‚ but I always knew one thing: I wanted to help people‚ and I wanted to be in the medical field. I have finally decided that I wanted to be a Registered Nurse and work in the Emergency Room of a large hospital. I have always admired my mother for her knowledge in her job. She is a RN as well‚ but she works in geriatrics. Working in the Emergency Room is completely different from geriatrics‚ but I feel that this is

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    Contrastive analysis

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    languages‚ in this case English. Questions would be ask by anyone regarding the rational of using contrastive analysis in learning language and why it is vital for us to keep practicing it when in the end‚ all that matters is the meaning of the sentences project. One must know that understanding and knowing the language make a major difference on indicating the level of proficiency of the language. Language does not usually appear on the literal meaning.  Saussure‚ in 1969 said one thing that may

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    Oxford Poverty & Human Development Initiative (OPHI) Oxford Department of International Development Queen Elizabeth House (QEH)‚ University of Oxford OPHI WORKING PAPER NO. 39 A Dissimilarity Index of Multidimensional Inequality of Opportunity Gaston Yalonetzky * November 2010 Abstract A recent literature on inequality of opportunity offers quantitative tools for comparisons and measurement based on stochastic dominance criteria and traditional inequality indices. In this paper I suggest

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    Bayesian Probabilistic Matrix Factorization using Markov Chain Monte Carlo Ruslan Salakhutdinov rsalakhu@cs.toronto.edu Andriy Mnih amnih@cs.toronto.edu Department of Computer Science‚ University of Toronto‚ Toronto‚ Ontario M5S 3G4‚ Canada Abstract Low-rank matrix approximation methods provide one of the simplest and most effective approaches to collaborative filtering. Such models are usually fitted to data by finding a MAP estimate of the model parameters‚ a procedure that can be

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    Sentence Correction

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    1. A “calendar stick” carved centuries ago by the Winnebago tribe may provide the first evidence that the North American Indians have developed advanced full-year calendars basing them on systematic astronomical observation. (A) that the North American Indians have developed advanced full-year calendars basing them (B) of the North American Indians who have developed advanced full-year calendars and based them (C) of the development of advanced full-year calendars by North American Indians

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    Tddc17 - Lab 2 Search

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     failure  and  water  leak.  What  is  the  difference?  The  answers  must  be  expressed   as  conditional  probabilities  of  the  observed  variables‚  P(Meltdown|...).   P(Meltdown | PumpFailureWarning‚ WaterLeakWarning) = 0‚14535 P (Meltdown | PumpFailure‚ WaterLeak) = 0‚2 c)  The  conditional  probabilities  for  the  stochastic  variables  are  often  estimated  by   repeated  experiments

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    Probability Theory

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    events are represented as circles within the sample space. Union of A and B : The event consisting of all sample points belonging to A or B or both. Intersection of A and B : The event containing the sample points belonging to both A and B. Conditional probabilities: the probability of an event given that another event already occurred. Joint probability: The probability of two events both is occurring that is the probability of the intersection of two events. Marginal probabilities: The values

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    introducing the products to the market? If he will contract any of the two companies‚ which one would he choose (MAI or I&K)? Should the new products be even introduced to the market? 2) Developing a Model: MAI’s proposal directly gives Steve the conditional probabilities he needs - probability of a successful venture given a favorable survey. Although the information from I&K is different‚ we can easily use Bayes’ theorem to on I&K information to compute the revised probabilities. As such‚ does not

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