This preview has intentionally blurred sections. Sign up to view the full version.
View Full DocumentThis preview has intentionally blurred sections. Sign up to view the full version.
View Full DocumentThis preview has intentionally blurred sections. Sign up to view the full version.
View Full DocumentThis preview has intentionally blurred sections. Sign up to view the full version.
View Full Document
Unformatted text preview: v i ) = 1. I Mean/Expected Value: E [ V ] = ∑ n i =1 v i p ( v i ). I Conditional Probability. .. Obara (UCLA) Introduction January 5, 2009 6 / 8 Required Math Probability Continuous Case I Random Variable: V take one of the value from [ v , v ]. I Cumulative Probability: F ( v ) = Pr( V ≤ v ). I Density Function: f ( v ) = F ( v ). So F ( v ) = R v v f ( x ) dx . I Expected Value: E [ V ] = R v v xf ( x ) dx . I Conditional Expectation. .. Obara (UCLA) Introduction January 5, 2009 7 / 8 Required Math Vector and Matrix Vector and Matrix Operations a 1 , 1 a 1 , 2 a 2 , 1 a 2 , 2 x 1 x 2 = a 1 , 1 x 1 + a 1 , 2 x 2 a 2 , 1 x 1 + a 2 , 2 x 2 . Inner Product: x · y = x 1 y 1 + x 2 y 2 = k x kk y k cos θ . Inverse: A1 A = I . etc. .. Obara (UCLA) Introduction January 5, 2009 8 / 8...
View
Full Document
 Winter '10
 Obara
 Probability theory, Ichiro Obara, (UCLA)

Click to edit the document details