Lecture 4-Probability Slides

# A common case is when we have dierent probability

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Unformatted text preview: random variables. A common case is when we have diﬀerent probability densities for diﬀerent classes (ωj ) P (ωj |x) = p(x|ωj )P (ωj ) p (x) • P (ωj ) = prior probability of ωj • p(x) = evidence • P (ωj |x) = posterior probability of ωj • p(x|ωj ) = likelihood of ωj with respect to x 40 Expected Value Expected value or mean E (X ) = xp(x) What is the expected value of playing a game where there is a 10% chance of winning \$10 and a 80% chance of losing \$1 and a 10% chance of losing \$2? 41 Expected Value Expected value or mean E (X ) = xp(x) What is the expected value of playing a game where there is a 10% chance of winning \$10 and a 80% chance of losing \$1 and a 10% chance of losing \$2? E (X ) = .1 ∗ 10 − .8 ∗ 1 − .1 ∗ 2 = 0 The expected value (expected payoﬀ) is \$0. 42 Expected Value Expected value or mean E (X ) = E (X ) = xp(x) xp(x)dx 43 Expected Value Expected value or mean E (X ) = E (X ) = xp(x) xp(x)dx What is the expected value (mean) of a uniform distribution from 0 to 2. First note a uniform distribution from 0 to 2 would have p(x)=0.5 for x ∈ [0, 2] 2 thus E (X ) = 0 x(0.5)dx = 0.25x2|2 = 1 0 44 45 Variance V ar(X ) = E (X − E (X ))2 = V ar(X ) = P (X )(X − E (X ))2 (x − E (x))2p(x)dx • It has the maximum entropy of all distributions with a given mean and variance • It’s been well studied • It’s analytically tractable! • Central Limit Theorem: sum of a large number of independent random variables is normally distributed applet Pattern Densities are commonly modeled by Normal Densities for several reasons Ú Ù ÎØ × Ö Ë Ô Ó É ÒÑ %QfÎg`Õp`QQÐTiÌv¼ÈigÅÃ Ï Î Í ËÊ É ÇÆÆÄ Â  § ¦ `Qa½`¼º¸FQQ``gQff²`Q°a`Qb`¦ ¨ ¡ p `¦ Á À ¿¾ » ¹ · ® ¶³« ª µ ´³ ² ª± ¯ ®­ ¬«« ª © ¥  ald~g     `Q¡   ¤ £ ¢ %~ Q`m` Dea 0Q        ¡        } { n xz  y   ¦gvQ~|Q`¦`eiQbQaQfQpnQg%`pmljDggfd`g%D  i gus x wuvs u t es r q n o n e k i h e        d w U XY d uc UV X d X sc  f %`HbQH`aY`vQQVrHQ%Sav`XgQQcQaQ£QVr`XiaYgeQ`ba`0W TR   y xp u w fY s u U fV S tVYc s q p h f U dc XVY X V U S \$ %"# !   6 )0(& ' I IG EC A 8 PQHFDB@ 97 532 %4(1 ¥¦¤ ¢£¡ ¦¤ £¡ ¥¢ § ©  ¨ The Normal Density 46 Univariate normal density (x−µ)2 1 − p (x) = √ e 2σ 2 2πσ has mean = µ variance = σ 2 has roughly 95% of its area within 2 standard deviations on either side of the mean (this is relevant for t-tests). 47 Standard Normal Gaussian with mean 0 and variance 1 (µ = 1, σ 2 = 1) 1 − x2 √ e2 2π 48 Univariate normal/Gaussian density What is ? ∞ 1 −(x−...
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## This note was uploaded on 02/09/2014 for the course COGS 109 taught by Professor Staff during the Fall '08 term at UCSD.

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