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Probability Theory (MATH-205) (Regular+Self)
Week 16: Markov Chain and continuous Markov Process.
Week 16: Markov Chain and continuous Markov Process.
Markov Chain and continuous Markov Process.
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markov-chain.pdf (0.91 MB )
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Course Material
Week 1: Basic concept of probability along with their definitions.
Week 2: Numerical problems for understanding the basic concepts of Probability.
Week 3: Rules of multiplications, combination and permutations of events.
Week 4: Random variable and random experiments along with their properties and examples.
Week 5:Mean, variance and standard deviation. Purpose and uses of these measures.
Week 6: Chebyshev's inequality its uses and applications
Week 7: Independence of random variables
Week 8: Multiplicative and additive properties of expectation and variances.
Week 9: Discrete and Continuous random variables.
Week 10: Uniform distribution, its properties and applications.
Week 11: Poisson and uniform distribution, its properties and applications.
Week 12: Exponential distribution with its properties and applications and Numerical Question
Week 13: Gamma distribution with its properties and applications.
Week 14: Normal distributions, its application and properties.
Week 15: Law of large numbers. Types of Law of large numbers and numerical problems
Week 16: Markov Chain and continuous Markov Process.
Chapters
16
Department
Mathematics
Teacher
Ahad Yasin