Lectures 17,18 – Boosting and Additive Trees Rice ECE697 Farinaz Koushanfar Fall 2006.
ELEC 303 – Random Signals Lecture 13 – Transforms Dr. Farinaz Koushanfar ECE Dept., Rice...
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Transcript of ELEC 303 – Random Signals Lecture 13 – Transforms Dr. Farinaz Koushanfar ECE Dept., Rice...
![Page 1: ELEC 303 – Random Signals Lecture 13 – Transforms Dr. Farinaz Koushanfar ECE Dept., Rice University Oct 15, 2009.](https://reader035.fdocuments.in/reader035/viewer/2022062304/56649e705503460f94b6e2ac/html5/thumbnails/1.jpg)
ELEC 303 – Random Signals
Lecture 13 – TransformsDr. Farinaz Koushanfar
ECE Dept., Rice UniversityOct 15, 2009
![Page 2: ELEC 303 – Random Signals Lecture 13 – Transforms Dr. Farinaz Koushanfar ECE Dept., Rice University Oct 15, 2009.](https://reader035.fdocuments.in/reader035/viewer/2022062304/56649e705503460f94b6e2ac/html5/thumbnails/2.jpg)
Lecture outline
• Reading: 4.4-4.5• Definition and usage of transform• Moment generating property• Inversion property• Examples• Sum of independent random variables
![Page 3: ELEC 303 – Random Signals Lecture 13 – Transforms Dr. Farinaz Koushanfar ECE Dept., Rice University Oct 15, 2009.](https://reader035.fdocuments.in/reader035/viewer/2022062304/56649e705503460f94b6e2ac/html5/thumbnails/3.jpg)
Transforms
• The transform for a RV X (a.k.a moment generating function) is a function MX(s) with parameter s defined by MX(s)=E[esX]
![Page 4: ELEC 303 – Random Signals Lecture 13 – Transforms Dr. Farinaz Koushanfar ECE Dept., Rice University Oct 15, 2009.](https://reader035.fdocuments.in/reader035/viewer/2022062304/56649e705503460f94b6e2ac/html5/thumbnails/4.jpg)
Why transforms?
• New representation• Has usages in – Calculations, e.g., moment generation– Theorem proving– Analytical derivations
![Page 5: ELEC 303 – Random Signals Lecture 13 – Transforms Dr. Farinaz Koushanfar ECE Dept., Rice University Oct 15, 2009.](https://reader035.fdocuments.in/reader035/viewer/2022062304/56649e705503460f94b6e2ac/html5/thumbnails/5.jpg)
Example(s)
• Find the transforms associated with– Poisson random variable– Exponential random variable– Normal random variable– Linear function of a random variable
![Page 6: ELEC 303 – Random Signals Lecture 13 – Transforms Dr. Farinaz Koushanfar ECE Dept., Rice University Oct 15, 2009.](https://reader035.fdocuments.in/reader035/viewer/2022062304/56649e705503460f94b6e2ac/html5/thumbnails/6.jpg)
Moment generating property
![Page 7: ELEC 303 – Random Signals Lecture 13 – Transforms Dr. Farinaz Koushanfar ECE Dept., Rice University Oct 15, 2009.](https://reader035.fdocuments.in/reader035/viewer/2022062304/56649e705503460f94b6e2ac/html5/thumbnails/7.jpg)
Example
• Use the moment generation property to find the mean and variance of exponential RV
![Page 8: ELEC 303 – Random Signals Lecture 13 – Transforms Dr. Farinaz Koushanfar ECE Dept., Rice University Oct 15, 2009.](https://reader035.fdocuments.in/reader035/viewer/2022062304/56649e705503460f94b6e2ac/html5/thumbnails/8.jpg)
Inverse of transforms
• Transform MX(s) is invertible – important
The transform MX(s) associated with a RV X uniquely determines the CDF of X, assuming that MX(s) is finite for all s in some interval [-a,a], a>0
• Explicit formulas that recover PDF/PMF from the associated transform are difficult to use
• In practice, transforms are often converted by pattern matching
![Page 9: ELEC 303 – Random Signals Lecture 13 – Transforms Dr. Farinaz Koushanfar ECE Dept., Rice University Oct 15, 2009.](https://reader035.fdocuments.in/reader035/viewer/2022062304/56649e705503460f94b6e2ac/html5/thumbnails/9.jpg)
Inverse transform – example 1
• The transform associated with a RV X is• M(s) =1/4 e-s + 1/2 + 1/8 e4s + 1/8 e5s
• We can compare this with the general formula• M(s) = x esx pX(x)• The values of X: -1,0,4,5• The probability of each value is its coefficient• PMF: P(X=-1)=1/4; P(X=0)=1/2;
P(X=4)=1/8; P(X=5)=1/8;
![Page 10: ELEC 303 – Random Signals Lecture 13 – Transforms Dr. Farinaz Koushanfar ECE Dept., Rice University Oct 15, 2009.](https://reader035.fdocuments.in/reader035/viewer/2022062304/56649e705503460f94b6e2ac/html5/thumbnails/10.jpg)
Inverse transform – example 2
![Page 11: ELEC 303 – Random Signals Lecture 13 – Transforms Dr. Farinaz Koushanfar ECE Dept., Rice University Oct 15, 2009.](https://reader035.fdocuments.in/reader035/viewer/2022062304/56649e705503460f94b6e2ac/html5/thumbnails/11.jpg)
Mixture of two distributions
• Example: fX(x)=2/3. 6e-6x + 1/3. 4e-4x, x0
• More generally, let X1,…,Xn be continuous RV with PDFs fX1, fX2,…,fXn
• Values of RV Y are generated as follows– Index i is chosen with corresponding prob pi
– The value y is taken to be equal to Xi
fY(y)= p1 fX1(y) + p2 fX2(y) + … + pn fXn(y)
MY(s)= p1 MX1(s) + p2 MX2(s) + … + pn MXn(s)
• The steps in the problem can be reversed then
![Page 12: ELEC 303 – Random Signals Lecture 13 – Transforms Dr. Farinaz Koushanfar ECE Dept., Rice University Oct 15, 2009.](https://reader035.fdocuments.in/reader035/viewer/2022062304/56649e705503460f94b6e2ac/html5/thumbnails/12.jpg)
Sum of independent RVs
• X and Y independent RVs, Z=X+Y• MZ(s) = E[esZ] = E[es(X+Y)] = E[esXesY]
= E[esX]E[esY] = MX(s) MY(s)
• Similarly, for Z=X1+X2+…+Xn,
MZ(s) = MX1(s) MX2(s) … MXn(s)
![Page 13: ELEC 303 – Random Signals Lecture 13 – Transforms Dr. Farinaz Koushanfar ECE Dept., Rice University Oct 15, 2009.](https://reader035.fdocuments.in/reader035/viewer/2022062304/56649e705503460f94b6e2ac/html5/thumbnails/13.jpg)
Sum of independent RVs – example 1
• X1,X2,…,Xn independent Bernouli RVs with parameter p
• Find the transform of Z=X1+X2+…+Xn
![Page 14: ELEC 303 – Random Signals Lecture 13 – Transforms Dr. Farinaz Koushanfar ECE Dept., Rice University Oct 15, 2009.](https://reader035.fdocuments.in/reader035/viewer/2022062304/56649e705503460f94b6e2ac/html5/thumbnails/14.jpg)
Sum of independent RVs – example 2
• X and Y independent Poisson RVs with means and respectively
• Find the transform for Z=X+Y• Distribution of Z?
![Page 15: ELEC 303 – Random Signals Lecture 13 – Transforms Dr. Farinaz Koushanfar ECE Dept., Rice University Oct 15, 2009.](https://reader035.fdocuments.in/reader035/viewer/2022062304/56649e705503460f94b6e2ac/html5/thumbnails/15.jpg)
Sum of independent RVs – example 3
• X and Y independent normal RVs• X ~ N(x,x
2), and Y ~ N(y,y2)
• Find the transform for Z=X+Y• Distribution of Z?
![Page 16: ELEC 303 – Random Signals Lecture 13 – Transforms Dr. Farinaz Koushanfar ECE Dept., Rice University Oct 15, 2009.](https://reader035.fdocuments.in/reader035/viewer/2022062304/56649e705503460f94b6e2ac/html5/thumbnails/16.jpg)
Review of transforms so far
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Bookstore example (1)
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Sum of random number of independent RVs
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Bookstore example (2)
![Page 20: ELEC 303 – Random Signals Lecture 13 – Transforms Dr. Farinaz Koushanfar ECE Dept., Rice University Oct 15, 2009.](https://reader035.fdocuments.in/reader035/viewer/2022062304/56649e705503460f94b6e2ac/html5/thumbnails/20.jpg)
Transform of random sum
![Page 21: ELEC 303 – Random Signals Lecture 13 – Transforms Dr. Farinaz Koushanfar ECE Dept., Rice University Oct 15, 2009.](https://reader035.fdocuments.in/reader035/viewer/2022062304/56649e705503460f94b6e2ac/html5/thumbnails/21.jpg)
Bookstore example (3)
![Page 22: ELEC 303 – Random Signals Lecture 13 – Transforms Dr. Farinaz Koushanfar ECE Dept., Rice University Oct 15, 2009.](https://reader035.fdocuments.in/reader035/viewer/2022062304/56649e705503460f94b6e2ac/html5/thumbnails/22.jpg)
More examples…
• A village with 3 gas station, each open daily with an independent probability 0.5
• The amount of gas in each is ~U[0,1000]• Characterize the probability law of the total
amount of available gas in the village
![Page 23: ELEC 303 – Random Signals Lecture 13 – Transforms Dr. Farinaz Koushanfar ECE Dept., Rice University Oct 15, 2009.](https://reader035.fdocuments.in/reader035/viewer/2022062304/56649e705503460f94b6e2ac/html5/thumbnails/23.jpg)
More examples…
• Let N be Geometric with parameter p• Let Xi’s Geometric with common parameter q• Find the distribution of Y = X1+ X2+ …+ Xn