MHAlogrithmuni: Metropolis-Hastings Algorithm to sample data from univariate...
In wzh96/STAT600RPackage: Implementing Metropolis-Hastings Algorithm
Description
Usage
Arguments
Value
View source: R/MHAlogrithmUni.R
Description
Metropolis-Hastings Algorithm to sample data from univariate distribution
Usage
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MHAlogrithmuni(
TargetDensity,
CGDensity = "Normal",
xinit = 0,
sigma = 1,
niter = 1000
)
Arguments
TargetDensity
A function: The PDF of target density function from which the users want to sample. This should be as a function of x.
CGDensity
Candidate Generating Density function (default is normal distribution)
xinit
A scalar: The starting point of the algorithm
sigma
A scalar: The standard deviation of the Candidate Generating Density
niter
A scalar: The number of data to sample
Value
Return the data collection sampled from the MH Algorithm
wzh96/STAT600RPackage documentation built on Dec. 23, 2021, 6:15 p.m.
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Description Usage Arguments Value
View source: R/MHAlogrithmUni.R
Description
Metropolis-Hastings Algorithm to sample data from univariate distribution
Usage
1 2 3 4 5 6 7 | MHAlogrithmuni(
TargetDensity,
CGDensity = "Normal",
xinit = 0,
sigma = 1,
niter = 1000
)
|
Arguments
TargetDensity |
A function: The PDF of target density function from which the users want to sample. This should be as a function of x. |
CGDensity |
Candidate Generating Density function (default is normal distribution) |
xinit |
A scalar: The starting point of the algorithm |
sigma |
A scalar: The standard deviation of the Candidate Generating Density |
niter |
A scalar: The number of data to sample |
Value
Return the data collection sampled from the MH Algorithm
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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