LECTURE 10 The Bayes variations Continuous Bayes rule; Derived distributions pX|Y (x | y) = pX,Y (x, y) pY (y) � pY (y) = • Readings: Section 3.6; start Section 4.1 x = pX (x)pY |X (y | x) pY (y) pX (x)pY |X (y | x) Example: Review pX (x) pX |Y (x | y) = pX (x) = � y pX,Y (x, y) pX,Y (x, y) pY (y) pX,Y (x, y) • X = 1, 0: airplane present/not present • Y = 1, 0: something did/did not register on radar fX (x) fX,Y (x, y) fX|Y (x | y) = fX (x) = � ∞ −∞ fX,Y (x, y) Continuous counterpart fY (y) fX,Y (x, y) dy fX|Y (x | y) = fX,Y (x, y) fY (y) = FX (x) = P(X ≤ x) Discrete X, Continuous Y fY (y ) = � x fY (y) y f X ,Y(y,x)=1 1 1 Continuous X, Discrete Y � x fX (x)fY |X (y | x) dx • It is a PMF or PDF of a function of one or more random variables with known probability law. E.g.: pX (x)fY |X (y | x) Example: • X: a discrete signal; “prior” pX (x) • Y : noisy version of X • fY |X (y | x): continuous noise model pY (y) = fY (y) What is a derived distribution pX (x)fY |X (y | x) fX |Y (x | y) = x fX (x)fY |X (y | x) Example: X: some signal; “prior” fX (x) Y : noisy version of X fY |X (y | x): model of the noise E[X], var(X) pX |Y (x | y) = fY (y) � = x – Obtaining the PDF for fX (x)pY |X (y | x) g(X, Y ) = Y /X pY (y) involves deriving a distribution. Note: g(X, Y ) is a random variable fX (x)pY |X (y | x) dx Example: • X: a continuous signal; “prior” fX (x) (e.g., intensity of light beam); • Y : discrete r.v. affected by X (e.g., photon count) • pY |X (y | x): model of the discrete r.v. When not to find them • Don’t need PDF for g(X, Y ) if only want to compute expected value: E[g(X, Y )] = 1 � � g(x, y)fX,Y (x, y) dx dy How to find them The continuous case • Discrete case • Two-step procedure: – Obtain probability mass for each possible value of Y = g(X) – Get CDF of Y : FY (y) = P(Y ≤ y) – Differentiate to get pY (y) = P(g(X) = y) � pX (x) = fY (y) = x: g(x)=y x dFY (y) dy y g(x) . . . . . . . Example . . . . . . . • X: uniform on [0,2] • Find PDF of Y = X 3 • Solution: FY (y) = P(Y ≤ y) = P(X 3 ≤ y) 1 = P(X ≤ y 1/3) = y 1/3 2 fY (y) = Example dFY 1 (y) = dy 6y 2/3 The pdf of Y=aX+b • Joan is driving from Boston to New York. Her speed is uniformly distributed between 30 and 60 mph. What is the distribution of the duration of the trip? Y = 2X + 5: fX faX faX+b 200 • Let T (V ) = . V • Find fT (t) -2 -1 2 3 4 9 f v(v0 ) 1/30 fY (y) = 30 60 v0 � 1 y−b fX a |a| � • Use this to check that if X is normal, then Y = aX + b is also normal. 2 MIT OpenCourseWare http://ocw.mit.edu 6.041SC Probabilistic Systems Analysis and Applied Probability Fall 2013 For information about citing these materials or our Terms of Use, visit: http://ocw.mit.edu/terms.