A short length of stay, as observed in this study for the R-coded patients, might result in less opportunity for investigation and diagnosis, leading to a less informative discharge summary from the physician and less likelihood of a defined cause for admission at coding.
We make an assumption that defines the likelihood of a set of hypotheses as the maximum likelihood over the elements of the set.
9 Briefly, we defined the likelihood of the data through a negative binomial observation process accounting for over-dispersion in the reporting of cases (the mean reporting rate was fixed at 60% and the dispersion parameter was inferred).
Furthermore, there are fewer safety concerns associated with a defined tumor vaccine, because the likelihood of eliciting an immune response to irrelevant proteins is decreased.
The significance is the significance A P-value as determined by the Perseus software, defining the likelihood of a protein being enriched compared to the background distribution.
The significance is the significance A P-value as determined by the Perseus software, defining the likelihood of a protein being enriched compared to the background distribution [ 112].
Models that emit probabilities are more effective than true/false alerts and allow the use of math to combine multiple pieces of evidence across different data sets to define the likelihood of a user account having been compromised or engaged in illicit activities.
We define the likelihood of observing a count V in dependence of the unknown expected count as follows where is the probability under the Poisson distribution with mean λ, and is the likelihood under the negative binomial distribution with mean and variance.
The model is constructed to provide, for a defined population undergoing several intervention scenarios, the likelihood of a variety of outcomes important to decision makers: death, ESRD, MIs, strokes, bone disease, and rate of CKD progression.
We adopt a probabilistic approach for door detection, by defining the likelihood of various features for generated door hypotheses.
Based on this model, we can define the likelihood of the data and use a Markov Chain Monte Carlo (MCMC) approach to sample from the posterior distribution of the parameters to obtain the optimal clustering.
