Lecture 15 Parameter Estimation Using Sample Mean Last Time Sums of R. V.s Moment Generating...
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Transcript of Lecture 15 Parameter Estimation Using Sample Mean Last Time Sums of R. V.s Moment Generating...
Lecture 15 Parameter Estimation Using Sample Mean
Last Time Sums of R. V.s
Moment Generating Functions MGF of the Sum of Indep. R.Vs Sample Mean (7.1) Deviation of R. V. from the Expected Value (7.2) Law of Large Numbers (part of 7.3)
Probability & Stochastic ProcessesYates & Goodman (2nd Edition) NTUEE SCC_06_200915 - 1
Lecture 15: Parameter Estimation Using Sample Mean
Today Law of Large Numbers (Cont.) Central Limit Theorem (CLT) Application of CLT The Chernoff Bound Point Estimates of Model Parameters Confidence Intervals
Reading Assignment: 6.6 – 6.8, 7.3-7.4
Probability & Stochastic ProcessesYates & Goodman (2nd Edition) NTUEE SCC_06_200915 - 2
Lecture 15: Parameter Estimation Using Sample Mean
Next Time: Final Exam
Scope: Chapters 4 – 7 Time: 15:30 -17:30
Reading Assignment: 6.6 – 6.8, 7.3-7.4
Probability & Stochastic ProcessesYates & Goodman (2nd Edition) NTUEE SCC_06_200915 - 3
Law of Large Numbers: Strong and Weak Jakob Bernoulli, Swiss Mathematician, 1654-1705 [Ars Conjectandi, Basileae, Impensis
Thurnisiorum, Fratrum, 1713The Art of Conjecturing; Part Four showing The Use and Application of the Previous Doctrine to Civil, Moral and Economic Affairs Translated into English by Oscar Sheynin, Berlin 2005]
Bernoulli and Law of Large Number.pdf
S&WLLN.doc
Visualization of Law of Large Numbers
15 - 5
Who are they?
For Sum of iid Uniform RVs
For Sum of iid Binomial RVs
15 - 13
Table 1: Normal Distribution Table (from Ulberg, 1987)
Probability and Stochastic ProcessesA Friendly Introduction for Electrical and Computer EngineersSECOND EDITION
Roy D. Yates David J. Goodman
Chapter 7
Parameter Estimation Using the Sample Mean
12 - 43
Theorem 7.7
If X has finite variance, then the sample mean MN(X) is a sequence of consistent estimates of E[X].
Strong Law of Large Numbers Please refer to the supplementary material
12 - 50
Theorem 7.10
E[VN(X)] = (n-1)/n Var[X]
Application to Histogram Construction Application of P(A) estimation to historgram construction
Approximation of CDF
1. Discretization of RV Values
Lecture 15: Parameter Estimation Using Sample Mean
Next Time: Final Exam
Scope: Chapters 4 – 7 Time: 15:30 -17:30
Reading Assignment: 6.6 – 6.8, 7.3-7.4
Probability & Stochastic ProcessesYates & Goodman (2nd Edition) NTUEE SCC_06_200915 - 69