Sentence examples for likelihood given from inspiring English sources

This is a strong likelihood given the weakness of the latter's position.

We believe that there's a greater likelihood, given the underlying allegations, that he would get a fairer trial there".

If he vetoes the bill, the Council could override the vote with the backing of 34 members -- a strong likelihood, given that yesterday's vote was 44 to 5, with one abstention.

If that also fails, the president appoints an interim government to hold new elections in 30 days — an increasing likelihood given the challenges the parties face finding common ground.

There is also the likelihood, given the undecided state of most voters, that the June legislative elections will not result in a further victory for the right, which will create a situation of "cohabitation" similar to the one that exists currently in the United States with George W. Bush in the White House and the Democrats controlling Congress.

The likelihood given by the above equation is stored in each leaf node.

Consider the expression for penalized log-likelihood given in Eq. (7).

Each model relies on log-likelihood maximization, with the log-likelihood given by ( LLleft(theta right)={displaystyle {sum}_{n=1}^N} ln{P}_nleft(theta right) ).

Specifically, let θ ( i − 1 ) be the current estimate of the parameter θ, then the E-step finds the conditional expectation E of the complete-data log-likelihood given θ ( i − 1 ) : q ( θ | θ ( i − 1 ) ) = E { log L ( θ, X N, Y N ) | Y N, θ ( i − 1 ) }. (18).

Once compiled, use within MATLAB is: where params is a vector of the model parameters [nu, mu, Q, tau], M is the number of phases, kh is a vector of count frequencies from 0 K, K is the maximum count, LL is the log-likelihood given the parameters and P is a the probability vector for the observed counts given the parameters.

The IRV is trained by gradient descent to minimize the error, equivalently, the negative log-likelihood given by [47] begin{aligned} - sum t({{mathcal {T}}})log left[ z({{mathcal {T}}})right] + (1-t({{mathcal {T}}}))log left[ 1-z({{mathcal {T}}})right], end{aligned} (6 where the summation is over the training instances, ({{mathcal {T}}}).