probability

Preposition: of

• occurrence: And those payoffs are not strictly aligned with their probabilities of occurrence.

Converse of object

• calculate: Intuitively, there would appear to be no problem in calculating these probabilities: Consider the possible events in a given time interval.
• assign: Therefore what we need are statistical techniques that allow us to assign a probability to the outcome of an experiment.
• compute: Finally we may simply observe the system as a " black box " where we compute the probability of failure from past experience.
• predict: The census data center the predicted probability to face some.

Adjective modifier

• posterior: What conclusions do you draw from the computation of the posterior probability?
• conditional: Statistical classifiers, for example, require data to estimate conditional probabilities.
• pre-test: The pre-test probability of pulmonary embolic disease should be determined.
• inverse: Note The passage on inverse probability is reproduced in Likelihood and Probability in R. A. Fisher's Statistical Methods for Research Workers.
• unconditional: We consider networks with heterogeneous a-priori conditional and unconditional failure probability.
• binomial: By now our pupils are calculating binomial probabilities almost without realizing it.

Modifies a noun

• density: The first is a full Bayes solution where the full probability density function of the model outputs is produced.
• distribution: The posterior probability distribution explains what is known about a parameter given observed data.
• estimator: This is achieved through a statistical interpretation of connectionist networks as probability estimators.
• p: Each person in every layer dies with probability p at each iteration.
• amplitude: The final probability of an event is then obtained by the sum of the squares of the two numbers describing the resultant probability amplitude.

Noun used with modifier

• priori: The specification of different a priori probabilities can greatly affect the accuracy of the prediction.
• disabilitythe: Set of interactions of workers a disabilitythe probability the health insurance.
• non-zero: All children born as members of the MCS population should have a known and non-zero probability of being included in the selected sample.
• transition: In a formal argument we claim the transition probability of OK to ERROR is zero.