That's 47 men who, in all likelihood, can no longer function sexually or stay out of the bathroom for long.
Such likelihood can be maximized using an alternating optimization method.
Therefore, the above utterance likelihood can be rewritten as (2).
The gain terms that maximize the likelihood can be computed as in [26].
The likelihood can be divided by a normalization term without affecting the classification to obtain.
Suppose the RSS measurements from different APs are independent, the observation likelihood can be expressed as (16).
The probabilistic likelihood can be modeled by Histogram [12], Gaussian [11], Log-normal [13], or Kernel [12].
At that stage, this likelihood can be estimated using the internal quality attributes of a class, which include cohesion, coupling, and size.
For example, By increasing the sample size the bound on the likelihood can be made as close to 1 as we want, for any margin q we choose.
Then, the likelihood can be expanded as p F | ω c = ∑ hϵH p F, h | ω c = ∑ hϵH p F | h, ω c p h | ω c, (16).
The coefficient likelihood can be obtained using the bit likelihoods similar to (28) as P left(mathbf{Y}_{ell}^{text{re}}(k) left|vphantom{frac{1}{2}}right.
