Create a standard normal distribution object with the mean μ equal to 0 and the standard deviation Ï equal to 1.
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Example: creating a handle to an anonymous function with one vector-valued input variable bowlVec = @(x)x(1)^2+(x(2)-2)^2; Example: creating a handle to a named function with two scalar-valued input variables bowlVecNamed = @(x)bowlNamed(x(1),x(2)); ezsurf cannot accept handles with vector-valued arguments (stick with examples on previous pages).
Define the input vector x to contain the values at which to calculate the pdf.
Compute the pdf values for the standard normal distribution at the values in x.
Each value in y corresponds to a value in the input vector x. For example, at the value x equal to 1, the corresponding pdf value y is equal to 0.2420.
Alternatively, you can compute the same pdf values without creating a probability distribution object. Use the
pdf function, and specify a standard normal distribution using the same parameter values for μ and Ï.
The pdf values are the same as those computed using the probability distribution object.
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Create a Poisson distribution object with the rate parameter, λ, equal to 2.
Define the input vector x to contain the values at which to calculate the pdf.
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Compute the pdf values for the Poisson distribution at the values in x.
Each value in y corresponds to a value in the input vector x. For example, at the value x equal to 3, the corresponding pdf value in y is equal to 0.1804.
Alternatively, you can compute the same pdf values without creating a probability distribution object. Use the
pdf function, and specify a Poisson distribution using the same value for the rate parameter, λ.
The pdf values are the same as those computed using the probability distribution object.
Create a standard normal distribution object.
Specify the
x values and compute the pdf.
Plot the pdf.
Create a Weibull probability distribution object.
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Specify the
x values and compute the pdf.
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Plot the pdf.
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