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Suppose that we have data y = (y₁, ... ,yn). Each data-point is assumed to be generated by a distribution with the following probability density function: p(y¡ | 4) = 24y¡ exp(−¥y?), y¡ ≥ 0, i = 1,...,n. The unknown parameter is , with > 0. Write down the likelihood for given y. Find an expression for the maximum likelihood estimate (MLE) .

Suppose that we have data y = (y₁, ... ,yn). Each data-point is assumed to be generated by a distribution with the following probability density function: p(y¡ | 4) = 24y¡ exp(−¥y?), y¡ ≥ 0, i = 1,...,n. The unknown parameter is , with > 0. Write down the likelihood for given y. Find an expression for the maximum likelihood estimate (MLE) .

Calculus For The Life Sciences
Calculus For The Life Sciences
2nd Edition
ISBN:9780321964038
Author:GREENWELL, Raymond N., RITCHEY, Nathan P., Lial, Margaret L.
Publisher:GREENWELL, Raymond N., RITCHEY, Nathan P., Lial, Margaret L.
Chapter13: Probability And Calculus
Section13.1: Continuous Probability Models
Problem 8E
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Suppose that we have data y = (y₁,..., yn). Each data-point is assumed to be generated by a distribution with the following probability density function: p(v¡ | 4) = 24y; exp(-4y?), y¡ ≥ 0, i = 1,...,n. The unknown parameter is , with > 0. Write down the likelihood for given y. Find an expression for the maximum likelihood estimate (MLE) .
Transcribed Image Text:Suppose that we have data y = (y₁,..., yn). Each data-point is assumed to be generated by a distribution with the following probability density function: p(v¡ | 4) = 24y; exp(-4y?), y¡ ≥ 0, i = 1,...,n. The unknown parameter is , with > 0. Write down the likelihood for given y. Find an expression for the maximum likelihood estimate (MLE) .
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