Conditional Probability for an Exact, Noncategorized Initial Condition

IRVING I. GRINGORTEN Air Force Cambridge Research Laboratories, Bedford, Mass.

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Abstract

Previous models for estimating the conditional probability of an event have used, as the condition, an initial categorized event such as no rain or overcast at time zero. But initial conditions frequently are observed and known in greater detail, and these observed values can replace the categories in determining conditional probabilities. A model that has as its underlying assumption the “Ornstein-Uhlenbeck” process is applicable to this problem. It uses the antecedent quantitatively without loss of information and with surprising simplicity.

Abstract

Previous models for estimating the conditional probability of an event have used, as the condition, an initial categorized event such as no rain or overcast at time zero. But initial conditions frequently are observed and known in greater detail, and these observed values can replace the categories in determining conditional probabilities. A model that has as its underlying assumption the “Ornstein-Uhlenbeck” process is applicable to this problem. It uses the antecedent quantitatively without loss of information and with surprising simplicity.

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