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University of California-San Diego
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BNFO 285 | Statistical Learning in Bioinformatics
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Popular Documents from University of California-San Diego
Lecture 02 Assumptions of Modern Computations o Suppose we have some function f ( x 1 ,…,x 10 ) If each x i has 10 4 possible values f will have ( 10 4 ) 10 possible inputs o If f was continuous, we could maximize by finding f ' ( x 1 ,…,x 10 )
Lecture 12 Review o A model of sequence evolution is “defined” by its rate matrix JC69: Non-diagonal entries are ¼ and diagonal are ¾ o P ( t ) = e Qt P ( 0 ) where P i ( t ) is the probability of being in state i after time t o Model-based calcu
Lecture 08 Calculate Area of Circle Using Monte Carlo o Say we have a square with side-length 2 r and we have a shape (circle) inside the square o Pick 2 random numbers from uniform distribution from 0 to 2 r , which will represent a random 2D poin
Lecture 05 Likelihood Function o L ( data , p ) = ∏ i = 1 n P ( x i ,p ) = L ( p ) p is the parameter o Given the Likelihood function, I can deterministically find the “best” parameter p that matches our data by choosing a value of p that maximiz
Lecture_16_-_Miscellaneous_Topics.docx-Lecture 16 Soft Computing o