continuous random variableの例文

例文

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1. In the special case that it is absolutely continuous, its distribution can be described by a probability density function, which assigns probabilities to intervals; in particular, each individual point must necessarily have probability zero for an absolutely continuous random variable.
2. For a continuous random variable \ theta _ i distributed about the unit circle, the Von Mises distribution maximizes the entropy when the real and imaginary parts of the first circular moment are specified or, equivalently, the circular mean and circular variance are specified.
3. For continuous random variables " X " 1, &, " X n ", it is also possible to define a probability density function associated to the set as a whole, often called "'joint probability density function " '.
4. In other words, while the " absolute likelihood " for a continuous random variable to take on any particular value is 0 ( since there are an infinite set of possible values to begin with ), the value of the PDF at two different samples can be used to infer that, in any particular draw of the random variable, how much more likely it is that the random variable would equal one sample compared to the other sample.
5. In probability theory, a "'probability density function "'( "'PDF "'), or "'density "'of a continuous random variable, is a function, whose value at any given sample ( or point ) in the sample space ( the set of possible values taken by the random variable ) can be interpreted as providing a " relative likelihood " that the value of the random variable would equal that sample.