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* [[(Attach:)IC566_Assignment5_2018.pdf | Assignment 5]] (due: June 7)
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Classical inference – III, course review
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*[May 24]
to:
*[May 24] No class ([[http://iset2018.iemek.org/ | ISET 2018 ]])
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* [[(Attach:)IC566_Assignment4_2018.pdf | Assignment 4]] (due: May 31)
May 15, 2018, at 11:06 AM EST by 125.143.67.201 -
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Classical inference – III, course review
* Final exam
to:
Classical inference – III, course review
May 15, 2018, at 11:06 AM EST by 125.143.67.201 -
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*[June 11] Final exam
May 15, 2018, at 11:06 AM EST by 125.143.67.201 -
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* The final exam is scheduled at 1:30 pm, June 11
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* [[(Attach:)IC566_Assignment3_2018.pdf | Assignment 3]] (due: %red%{-May 14-} May 17%black%)
to:
* [[(Attach:)IC566_Assignment3_2018.pdf | Assignment 3]] (due: {-May 14-} %red% May 17%black%)
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* [[(Attach:)IC566_Assignment1_2018.pdf | Assignment 1]] (due: %red%{-March 29-} April 2%black%)
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* [[(Attach:)IC566_Assignment1_2018.pdf | Assignment 1]] (due: {-March 29-} %red% April 2%black%)
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* [[(Attach:)IC566_Assignment1_2018.pdf | Assignment 1]] (due: %red%{-March 29-} April 2)
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* [[(Attach:)IC566_Assignment1_2018.pdf | Assignment 1]] (due: %red%{-March 29-} April 2%black%)
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* [[(Attach:)IC566_Assignment3_2018.pdf | Assignment 3]] (due: %red%{-May 14-} May 17)
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* [[(Attach:)IC566_Assignment3_2018.pdf | Assignment 3]] (due: %red%{-May 14-} May 17%black%)
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* [[(Attach:)IC566_Assignment3_2018.pdf | Assignment 3]] (due: May 14)
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* [[(Attach:)IC566_Assignment3_2018.pdf | Assignment 3]] (due: %red%{-May 14-} May 17)
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* [[(Attach:)IC566_Assignment3_2018.pdf | Assignment 3]] (due: May 13)
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* [[(Attach:)IC566_Assignment3_2018.pdf | Assignment 3]] (due: May 14)
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* [[(Attach:)IC566_Assignment3_2018.pdf | Assignment 3]] (due: May 13)
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*[May 7]
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*[May 7] No class (national holiday)
April 23, 2018, at 05:06 PM EST by 125.143.67.201 -
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*[Apr. 19] Bernoulli process
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*[Apr. 19] Bernoulli process (makeup class)
April 23, 2018, at 05:05 PM EST by 125.143.67.201 -
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*[Apr. 23] Poisson process – I
*[Apr. 26]
to:
*[Apr. 23] (%red%Class begins at noon%%) Poisson process – I
*[Apr. 26] No class ([[https://www.kics.or.kr/home/kor/Event/Contents.aspx?ContentUID=b433c874-047c-46bc-a42c-d45fe8fecc9a&EventUID=9923f0e9-8f77-455d-8274-956b0ac2a877 | 미래통신기술 워크샵 ]])
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*[Apr. 26] No class
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*[Apr. 26]
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*[May 7] No class
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*[May 7]
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*[May 24] ISET 2018
to:
*[May 24]
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*13. Bernoulli process
*14. Poisson process – I
*15. Poisson process – II
*16. Markov chainsI
*17. Markov chains – II
*18. Markov chains – III
*19. Weak law of large numbers
*20. Central limit theorem
*21. Bayesian statistical inference – I
*22. Bayesian statistical inference – II
*23. Classical inference – I
*24. Classical inference – II
*25.
Classical inference – III, course review
to:
*[Apr. 19] Bernoulli process
*[Apr. 23] Poisson process – I
*[Apr. 26] No class
*[Apr. 30] Poisson process II
*[May 3] Markov chains – I
*[May 7] No class
*[May 10] Markov chains – II
*[May 14] Markov chains – III
*[May 17] Weak law of large numbers
*[May 21] Central limit theorem
*[May 24] ISET 2018
*[May 28] Bayesian statistical inference – I
*[May 31] Bayesian statistical inference – II
*[June 4] Classical inference – I
*[June 7] Classical inference – II



Classical inference – III, course review
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*[Apr. 5] (%red%Class begins at noon) Convolution; covariance and correlation; Iterated expectations; sum of a random number of random variables (regular + makeup class)
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*[Apr. 5] (%red%Class begins at noon%%) Convolution; covariance and correlation; Iterated expectations; sum of a random number of random variables (regular + makeup class)
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*[Apr. 5] (%red%Class begins at ??? determined on Apr. 2%%) Convolution; covariance and correlation; Iterated expectations; sum of a random number of random variables (regular + makeup class)
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*[Apr. 5] (%red%Class begins at noon) Convolution; covariance and correlation; Iterated expectations; sum of a random number of random variables (regular + makeup class)
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* [[(Attach:)IC566_Assignment1_2018.pdf | Assignment 1]] (due: %red%{ March 29-} April 2)
to:
* [[(Attach:)IC566_Assignment1_2018.pdf | Assignment 1]] (due: %red%{-March 29-} April 2)
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* [[(Attach:)IC566_Assignment1_2018.pdf | Assignment 1]] (due: March 29)
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* [[(Attach:)IC566_Assignment1_2018.pdf | Assignment 1]] (due: %red%{ March 29-} April 2)
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*[Apr. 9 & 12] No class ([[https://cister.isep.ipp.pt/cpsweek2018/ | ACM/IEEE CPS Week 2018 ]])
to:
*[Apr. 9 & 12] No class ([[https://cister.isep.ipp.pt/cpsweek2018/ | CPS Week 2018 ]])
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*[Apr. 2] (%red%Class begins at 1 pm%%) Multiple continuous random variables; Continuous Bayes rule; derived distributions
*[Apr. 5] (%red%Class begins at ??? determined on Apr. 2%%) Convolution; covariance and correlation; Iterated expectations; sum of a random number of random variables
to:
*[Apr. 2] (%red%Class begins at 1 pm%%) Multiple continuous random variables; Continuous Bayes rule; derived distributions (regular + makeup class)
*[Apr. 5] (%red%Class begins at ??? determined on Apr. 2%%) Convolution
; covariance and correlation; Iterated expectations; sum of a random number of random variables (regular + makeup class)
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* [[(Attach:)IC566_Assignment2_2018.pdf | Assignment 2]] (due: April 9: The drop box is in front of E3-506)
to:
* [[(Attach:)IC566_Assignment2_2018.pdf | Assignment 2]] (due: April 9. The drop box is in front of E3-506)
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* [[(Attach:)IC566_Assignment2_2018.pdf | Assignment 2]] (due: April 9: The drop box is in front of the CSI Lab., E3-506)
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* [[(Attach:)IC566_Assignment2_2018.pdf | Assignment 2]] (due: April 9: The drop box is in front of E3-506)
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* [[(Attach:)IC566_Assignment2_2018.pdf | Assignment 2]] (due: April 9: The drop box is in front of the CSI Lab., E3-5 )
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* [[(Attach:)IC566_Assignment2_2018.pdf | Assignment 2]] (due: April 9: The drop box is in front of the CSI Lab., E3-506)
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*[Apr. 5] Convolution; covariance and correlation; Iterated expectations; sum of a random number of random variables
to:
*[Apr. 5] (%red%Class begins at ??? determined on Apr. 2%%) Convolution; covariance and correlation; Iterated expectations; sum of a random number of random variables
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* The midterm exam is scheduled at 1:30 pm, April 16
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*[Apr. 5] Derived distributions; convolution; covariance and correlation
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*[Apr. 5] Convolution; covariance and correlation; Iterated expectations; sum of a random number of random variables
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*12. Iterated expectations; sum of a random number of random variables
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* Midterm exam
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*[Apr. 16] Midterm exam
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*[Apr. 2] Multiple continuous random variables; Continuous Bayes rule; derived distributions
to:
*[Apr. 2] (%red%Class begins at 1 pm%%) Multiple continuous random variables; Continuous Bayes rule; derived distributions
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*[Mar. 26] Continuous random variables
*[Mar. 29] Multiple continuous random variables
*10. Continuous Bayes rule; derived distributions
*11. Derived distributions; convolution; covariance and correlation
to:
*[Mar. 26] Continuous random variables; Multiple continuous random variables
*[Mar. 29] No class
*[Apr. 2] Multiple continuous random variables; Continuous Bayes rule; derived distributions
*[Apr. 5] Derived distributions; convolution; covariance and correlation
*[Apr. 9 & 12] No class ([[https://cister.isep.ipp.pt/cpsweek2018/ | ACM/IEEE CPS Week 2018 ]])
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* [[(Attach:)IC566_Assignment2_2018.pdf | Assignment 2]] (due: April 5)
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* [[(Attach:)IC566_Assignment2_2018.pdf | Assignment 2]] (due: April 9: The drop box is in front of the CSI Lab., E3-5 )
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* [[(Attach:)IC566_Assignment2_2018.pdf | Assignment 1]] (due: April 5)
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* [[(Attach:)IC566_Assignment2_2018.pdf | Assignment 2]] (due: April 5)
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* [[(Attach:)IC566_Assignment2_2018.pdf | Assignment 1]] (due: April 5)
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* Midterm exam
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* Midterm exam
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*8. Continuous random variables
*9. Multiple continuous random variables
to:
*[Mar. 26] Continuous random variables
*[Mar. 29] Multiple continuous random variables
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*[Mar. 19] (%red% Class begins at noon%%) Discrete random variable examples; joint PMFs & Multiple discrete random variables (regular + makeup class)
to:
*[Mar. 19] (%red%Class begins at noon%%) Discrete random variable examples; joint PMFs & Multiple discrete random variables (regular + makeup class)
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*[Mar. 19] (%red% Class begins at noon%%) Discrete random variable examples; joint PMFs & Multiple discrete random variables: expectations, conditioning, independence
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*[Mar. 19] (%red% Class begins at noon%%) Discrete random variable examples; joint PMFs & Multiple discrete random variables (regular + makeup class)
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*[Mar. 19 (%red% Class begins at noon%%)] Discrete random variable examples; joint PMFs & Multiple discrete random variables: expectations, conditioning, independence
to:
*[Mar. 19] (%red% Class begins at noon%%) Discrete random variable examples; joint PMFs & Multiple discrete random variables: expectations, conditioning, independence
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*[Mar. 19 (%red% Class begins at noon%%] Discrete random variable examples; joint PMFs & Multiple discrete random variables: expectations, conditioning, independence
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*[Mar. 19 (%red% Class begins at noon%%)] Discrete random variable examples; joint PMFs & Multiple discrete random variables: expectations, conditioning, independence
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*[Mar. 19] Discrete random variable examples; joint PMFs & Multiple discrete random variables: expectations, conditioning, independence
to:
*[Mar. 19 (%red% Class begins at noon%%] Discrete random variable examples; joint PMFs & Multiple discrete random variables: expectations, conditioning, independence
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*[Mar. 22] No class ([[http://www.ictkorea.org/main/ (ICT Convergence Korea 2018)]])
to:
*[Mar. 22] No class ([[http://www.ictkorea.org/main/ | ICT Convergence Korea 2018 ]])
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*[Mar. 19] Discrete random variable examples; joint PMFs
*7. Multiple discrete random variables: expectations, conditioning, independence
to:
*[Mar. 19] Discrete random variable examples; joint PMFs & Multiple discrete random variables: expectations, conditioning, independence
*[Mar. 22] No class ([[http://www.ictkorea.org/main/ (ICT Convergence Korea 2018)]])
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[[(Attach:)queuing-theory-revised-kjp.ppt|here]]
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* (Attach:)IC566_Assignment1_2018.pdf | Assignment 1] (due: March 29)
to:
* [[(Attach:)IC566_Assignment1_2018.pdf | Assignment 1]] (due: March 29)
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* [Attach:IC566_Assignment1_2018.pdf | Assignment 1] (due: March 29)
to:
* (Attach:)IC566_Assignment1_2018.pdf | Assignment 1] (due: March 29)


[[(Attach:)queuing-theory-revised-kjp.ppt|here]]
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* Attach:IC566_Assignment1_2018.pdf | Assignment 1 (due: March 29)
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* [Attach:IC566_Assignment1_2018.pdf | Assignment 1] (due: March 29)
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* Attach:IC566_Assignment1 2018.pdf | Assignment 1 (due: March 29)
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* Attach:IC566_Assignment1_2018.pdf | Assignment 1 (due: March 29)
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* Attach:file.ext | Assignment 1 (due: March 29)
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* Attach:IC566_Assignment1 2018.pdf | Assignment 1 (due: March 29)
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* Attach:file.ext | Assignment 1 (due: March 29)
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*5. Discrete random variables; probability mass functions; expectations
*6. Discrete random variable examples; joint PMFs
to:
*[Mar. 15] Discrete random variables; probability mass functions; expectations
*[Mar. 19] Discrete random variable examples; joint PMFs
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*4. Counting
to:
*[Mar. 12] Counting
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to:
* Midterm exam
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*25. Classical inference – III, course review
to:
*25. Classical inference – III, course review
* Final exam
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*5. Discrete random variables
*6. Continuous random variables
*7. Continuous Bayes rule
*8. Derived distributions
; convolution; covariance and correlation
*9. Iterated
expectations
*10. Bernoulli process
*11. Poisson process
*12. Markov chains
*13. Weak law of large numbers
*14. Central limit theorem
*15. Bayesian statistical inference
*16. Classical inference
to:
*5. Discrete random variables; probability mass functions; expectations
*6. Discrete random variable examples; joint PMFs
*7. Multiple discrete random variables:
expectations, conditioning, independence
*8. Continuous random variables
*9. Multiple continuous random variables
*10. Continuous Bayes rule; derived distributions
*11. Derived distributions; convolution; covariance and correlation
*12. Iterated expectations; sum of a random number of random variables
*13. Bernoulli process

*14. Poisson process – I
*15. Poisson process – II
*16. Markov chains – I
*17. Markov chains – II
*18. Markov chains – III
*19. Weak law of large numbers
*20. Central limit theorem
*21. Bayesian statistical inference – I
*22. Bayesian statistical inference – II
*23. Classical inference – I
*24. Classical inference – II
*25. Classical inference – III, course review
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Kyung-Joon Park\\
Office hours: By appointment
, E3-513 DGIST
to:
Kyung-Joon Park (kjp@, x6314, and E3-513)\\
Office hours: Right after class or by appointment
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Office hours: By appointment, %red% E3-513 %% DGIST
to:
Office hours: By appointment, E3-513 DGIST
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MTh 13:30-15:00 pm, E3-113
to:
MTh 13:30-15:00 pm, %red% E3-113 %%
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Office hours: By appointment, E3-513 DGIST
to:
Office hours: By appointment, %red% E3-513 %% DGIST
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*[February 26] Probability models and axioms
*2. Conditioning and Bayes rule
*3. Independence
to:
*[Feb. 26] Probability models and axioms
*[Mar. 5] Conditioning and Bayes rule
*[Mar. 8] Independence
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'''Class hour and classroom: '''
to:
'''Class hour and classroom: '''\\
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'''Class hour and classroom: '''
MTh 13:30-15:00 pm, E3-113
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Office hours: By appointment, 3-309 DGIST
to:
Office hours: By appointment, E3-513 DGIST
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*1. Probability models and axioms
to:
*[February 26] Probability models and axioms
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(:Title Spring 2018:)



[++'''IC566: Random Signals and Random Processes (Spring 2018) '''++]

--------------------------------------------------------------------------------

'''Instructor: '''\\
Kyung-Joon Park\\
Office hours: By appointment, 3-309 DGIST

'''Textbooks: '''
* Introduction to Probability, Dimitri P. Bertsekas and John N. Tsitsiklis, Athena Scientific, 2nd edition, 2008

'''Course Description: ''' \\
This will be an introductory graduate level course on probability and random processes.
The classes will have three phases:
First, we will begin with the review of undergraduate level probability and random variables.
Second, we will proceed to cover the basic concepts and engineering examples of discrete and continuous random processes, such as Bernoulli, Poisson, and Markov.
Last, we will apply our understanding of probabilistic models to detection and estimation problems of random signals.
\\\

'''Grading policies:'''\\

Homework 30%, Midterm exam 30%, Final exam 40%\\\

'''Announcement:'''

'''Lectures:'''
*1. Probability models and axioms
*2. Conditioning and Bayes rule
*3. Independence
*4. Counting
*5. Discrete random variables
*6. Continuous random variables
*7. Continuous Bayes rule
*8. Derived distributions; convolution; covariance and correlation
*9. Iterated expectations
*10. Bernoulli process
*11. Poisson process
*12. Markov chains
*13. Weak law of large numbers
*14. Central limit theorem
*15. Bayesian statistical inference
*16. Classical inference
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Page last modified on June 05, 2018, at 12:24 PM EST