#1. EM algorithm allows us to maximize log L(ly) by working with only log[(y,x) and the conditional density k(xly,0), de

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1 Em Algorithm Allows Us To Maximize Log L Ly By Working With Only Log Y X And The Conditional Density K Xly 0 De 1 (26.46 KiB) Viewed 30 times

#1. EM algorithm allows us to maximize log L(ly) by working with only log[(y,x) and the conditional density k(xly,0), defined by L(0\y,x)=f(y,x10) L(@ly)=g(yl) and k(x\y,0) = f(x,y10) ggle) Then log L(Oly) = log L(Oly,x)-logk(xly,0). Note that is missing data. Based on that, we create the new identity log L(Oly) = E[log L(0\y,x)\0',y] - Ellogk(x|y,0)[0',y]. Let (r) be the value that maximizes Elog L(0\y,z)| (-¹),y]. Then show that Ellog L(+1) y, z),y] ≥ Elog L(6)y,z)),y] Ellogk(xy,+1))),y] ≤ E[logk(xy,))),y]. and