一、課程說明(Course Description)
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<br>   In real world, many systems evolve over time with an inherent randomness. The purpose of this course is to develop and analyze probability models that capture the salient features of the system under study. This course will strike a proper balance between theory development and practical implementation. Students will learn to use programming language such as C, C++ or Matlab to simulate a stochastic system.
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<br>二、指定用書(Text Books)
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<br>    Introduction to Probability Models, 9th Ed. by Sheldon M. Ross, Academic Press, 2006.
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<br>三、參考書籍(References)
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<br>    Stochastic Processes, 2nd Ed. by Sheldon. M. Ross, 1996.
<br>    Stochastic Modeling Analysis and Simulation by Barry L. Nelson, 2002.
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<br>四、教學方式(Teaching Method)
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<br>    Lecture
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<br>五、教學進度(Syllabus)
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<br>1. Random variables and conditional probability
<br>2. Discrete-time Markov Chain (DTMC)
<br>3. Poisson Process
<br>4. Continuous-time Markov Chain (CTMC)
<br>5. Renewal Theory and its applications
<br>6. Queueing Theory and its applications
<br>7. Additional topics (Brownian Motion, Martingale etc)
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<br>六、成績考核(Evaluation)
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<br>Midterm 20%, 
<br>Final 35%, 
<br>Project and Presentation 25%, 
<br>Homework 20%.
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<br>七、可連結之網頁位址