HMM : Viterbi algorithm - a toy example
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0.5
H
0.5
A 0.2
C 0.3
G 0.3
T 0.2
0.5
0.5
0.4
L
A 0.3
C 0.2
G 0.2
T 0.3
0.6
Let's consider the following simple HMM. This model is composed
of 2 states, H (high GC content) and L (low GC content). We can
for
Some Famous Coin Flipping Random Variables
Counting Successes:
Bernoulli(p): Conduct a single success/failure trial that has probability of success 19. Let X : 1 if the result is
asuccsss, andX:0ifthe1multisafa.ilure.
f0?) = l w) ELK) = :0 Vax) = 13(1 P)
Some Famous Normal Theory Random Variables
Normal (GaussianXpL, 02 ): The central limit theorem says (loosely) that the sum of a bunch of random variables
is normally distributed, as long as no small subset of those random variable dominates the others. I