Vite Rbi Training
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Transcript of Vite Rbi Training
![Page 1: Vite Rbi Training](https://reader036.fdocuments.net/reader036/viewer/2022082822/5695cfa01a28ab9b028edbb2/html5/thumbnails/1.jpg)
Viterbi training
Initialize emission and transition probabilities to random numbers.
while (true)Do Viterbi decoding using current parametersSave current parameters as previous parameters.Re-estimate emission and transition parameters from the state path
decoded by Viterbi. (add pseduocounts, see next page).if sum of absolute difference between current and previous parameters
is tiny (e.g., < 0.00001), break;
endprint current parameter and P(sequence, viterbi path)Repeat the above procedure several times (with different
random seed), and compare P(sequence, viterbi path). Report the parameters learned that give the largest P.
![Page 2: Vite Rbi Training](https://reader036.fdocuments.net/reader036/viewer/2022082822/5695cfa01a28ab9b028edbb2/html5/thumbnails/2.jpg)
Re-estimate parameters with pseudocounts
• Count number of transitions, n_xy, where x, y = {a, b}
• t_xy = (n_xy+c) / sum_x(n_xy+c)– e.g. t_ab = (n_ab +1) / (n_ab + n_aa + 2)
• Count number of symbols in each state, N_aX and N_bX, where X = A, C, G, T
• e_aX = (N_aX + 1) / (sum_X N_aX + 4)
• e_bX = (N_bX + 1) / (sum_X N_bX + 4)
Pseudocount
![Page 3: Vite Rbi Training](https://reader036.fdocuments.net/reader036/viewer/2022082822/5695cfa01a28ab9b028edbb2/html5/thumbnails/3.jpg)
Backward-Forward algorithm:Compute sum of probabilities in log space
• Two probabilities x and y, x < y• lx = log(x), ly = log(y), (lx < ly)• z = x + y = y (1 + x/y)
lz = log(z) = log(x+y)= log(y) + log(1 + x/y)= ly + log(1 + exp(log(x)-log(y))= ly + log(1 + exp(lx – ly))
Also see page 4 in this doc: http://cs.utsa.edu/~jruan/teaching/cs5263_fall_2007/proj1.pdf
and page 77 of the handouts.