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Details about the features ranked as first by the Forward algorithm are discussed afterward.
Table 9 The 40 first features ranked by the Forward algorithm for the F5 algorithm at 5% embedding rate.
Table 10 The 40 first features ranked by the Forward algorithm for the JPHS algorithm at 5% embedding rate.
Table 11 The 40 first features ranked by the Forward algorithm for the MBSteg algorithm at 5% embedding rate.
Table 13 The 40 first features ranked by the Forward algorithm for the Outguess algorithm at 5% embedding rate.
Table 14 The 40 first features ranked by the Forward algorithm for the Steghide algorithm at 5% embedding rate.
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It is apparent that by caching α values, the forward algorithm reduces the complexity of calculations involved to N 2 T rather than 2T N T. One may also define an analogous backwards algorithm which is the exact reverse of the forwards algorithm with the backwards variable: termination: beta_{t}(i =P(o_{t+1}o_{t+2}ldots o_{T}|q_{t}=s_{i},lambda) (14).
More specifically, we run a banded version of the forward algorithm by [ 13], considering all alignments in a band of size three around a guide alignment produced by BLASR.
It is well-known that we can obtain the above counts T i, j (X, Π s(X)) and E i (y, X, Π s(X)) for a given training sequence X, iteration q and a sampled state path Π s(X) by using a combination of the forward algorithm and stochastic back-tracing [ 13, 32].
And the data likelihood p(x|Φ) can be obtained by integrating over the states using the forward algorithm [24].
This is done by a dynamic programming algorithm called the forward algorithm.
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