Sentence examples for multiplying the prior from inspiring English sources

A joint probability is then determined for each alternative outcome by multiplying the prior probability by all conditional probabilities.

Probative value of \(E = P(H | E) - P(H \) \(P H | E \) (the posterior probability) is derived by applying Bayes' theorem that is, by multiplying the prior probability by the likelihood ratio (see discussion in section 3.2.2 below).

On the Bayesian approach, the posterior odds are calculated by multiplying the prior odds (1 1) by the likelihood ratio (which, as we saw in section 2.1.2 above, is 2 1).

Formally, the posterior odds (i.e. in the light of the data) on the alternative hypothesis can be calculated by multiplying the prior odds (i.e. before the data) by the BF.

Due to the variable nature of student attendance in alternative high schools, the study participation rate was derived by multiplying the prior year's (2005-2006) attendance rate with the schools' 2006-2007 studenrollmentment [ 40], to give an estimated average adjusted enrollment of 68 students (range: 16 to 107).

The posterior distributions of the latent variables are derived from Bayes' theorem by multiplying the priors by the likelihood function.

The posterior joint distribution is obtained by simply multiplying the priors with the likelihood and it can be written as begin{array}rcl@ & & f (beta,alpha,gamma,theta_{i},M^{ast},D^{ast},cvert M,D,X,W) & propto & f (beta) f (alpha) f (gamma) f (c) prod limits_{i =1}^{N}f (M_{i},D_{i},M_{i}^{ast},D_{i}^{ast},theta_{i}vert X_{i},W_{i},alpha,beta,gamma,c) end{array}.

According to (3), if we multiply the prior stated in (5) by the likelihood function (4), we obtain the posterior pdf.

In order to calculate the prior density that is equivalent under a different parameterization, one needs to multiply the prior probability density by the absolute value of the determinant of the Jacobian of the transformation.

A weighted average prior probability was calculated by adding up the priors of the sub-studies, but multiplying the individual priors by the proportion of patients in the sub-study in relation to the total number of patients in all studies together, therefore, allowing larger studies to have more influence on the prior.

To illustrate the effects of multiplying the likelihood by a prior, suppose that in the 1 × 2 example in Figure 2 the likelihoods of the tiles in Hypothesis 5 and Hypothesis 6 were the same, both equal to some value l.