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    공학 >전기ㆍ전자 >전기공학
  • 강의학기
    2016년 1학기
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강의계획서
강의계획서
This course provides a broad introduction to probability theory and random processes and their applications to engineering problems.

차시별 강의

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1. 1. Sample Space and Probability Set theory, Sample Space, Probability Axioms, Some Consequences of the Axioms. URL
2. 2. Discrete Random Variables Discrete Random Variables: PMF, CDF, Expectation, Variance and Standard Deviation, Joint PMF, Conditioning, Independence. URL
3. 3. Continuous Random Variables General Random Variables: CDF, PDF, and Gaussian RV's, Joint PDF, Continuous Conditioning. URL
4. 4. Further Topics on Random Variables Further Topics on Random Variables: Derived Random Variables, Covariance and Correlation, Conditional Expectation and Variance, Transforms, Sums of Independent Random Variables. URL
5. 5. Limit Theorems Limit Theorems: Markov and Chebyshev Inequalities, The Weak Law of Large Numbers, Convergence in Probability, The Central Limit Theorem. URL
6. 6. The Bernoulli and Poisson Processes Stochastic Processes: Bernoulli and Poisson Random Processes. URL
7. 7. Bayesian Statistical Inference Bayesian Statistical Inference: Point Estimation, Hyothesis Testing, MAP, Least Mean Square Estimation. URL
8. 8. Classical Statistical Inference Non-Bayesian Statistical Inference: Linear Regression, Binary Hypothesis Testing, Significance Testing. URL
9. 9. Classical Statistical Inference Non-Bayesian Statistical Inference: Linear Regression, Binary Hypothesis Testing, Significance Testing. URL

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