Conditional Probability Density Function

Lecture Probability Theory - Lecture 4: Binomial Random Variable Applications, Conditional Probability Density Function and Stirling’s Formula includes the following topics: The Normal Approximation, The Poisson Approximation, Conditional Probability Density Function, Stirling’s Formula.

34p cucngoainhan0 10-05-2022 18 1   Download

Lecture Probability Theory - Lecture 11: Conditional Density Functions and Conditional Expected Values. In probability theory, the conditional expectation, conditional expected value, or conditional mean of a random variable is its expected value – the value it would take “on average” over an arbitrarily large number of occurrences – given that a certain set of "conditions" is known to occur. If the random variable can take on only a finite number of values, the “conditions” are that the variable can only take on a subset of those values.

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In this paper, two multivariate analysis methods (covariance and conditional subjective probability density function) were presented and applied to a simple PSS. The methods followed a generalized procedure for evaluating t-h reliability based on dependency consideration. A passively water-cooled steam generator was used to demonstrate the dependency of the identified key CPs using the methods.

9p minhxaminhyeu3 12-06-2019 9 1   Download