文件名称:fit_ML_maxwell
- 所属分类：
- matlab例程
- 资源属性：
- [Matlab] [源码]
- 上传时间：
- 2012-11-26
- 文件大小：
- 1kb
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- 0次
- 提 供 者：
- resid*****
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fit_ML_normal - Maximum Likelihood fit of the log-normal distribution of i.i.d. samples!.
Given the samples of a log-normal distribution, the PDF parameter is found
fits data to the probability of the form:
p(x) = sqrt(1/(2*pi))/(s*x)*exp(- (log(x-m)^2)/(2*s^2))
with parameters: m,s
format: result = fit_ML_log_normal( x,hAx )
input: x - vector, samples with log-normal distribution to be parameterized
hAx - handle of an axis, on which the fitted distribution is plotted
if h is given empty, a figure is created.
output: result - structure with the fields
m,s - fitted parameters
CRB_m,CRB_s - Cram?r-Rao Bound for the estimator value
RMS - RMS error of the estimation
type - ML - fit_ML_normal - Maximum Likelihood fit of the log-normal distribution of i.i.d. samples!.
Given the samples of a log-normal distribution, the PDF parameter is found
fits data to the probability of the form:
p(x) = sqrt(1/(2*pi))/(s*x)*exp(- (log(x-m)^2)/(2*s^2))
with parameters: m,s
format: result = fit_ML_log_normal( x,hAx )
input: x - vector, samples with log-normal distribution to be parameterized
hAx - handle of an axis, on which the fitted distribution is plotted
if h is given empty, a figure is created.
output: result - structure with the fields
m,s - fitted parameters
CRB_m,CRB_s - Cram?r-Rao Bound for the estimator value
RMS - RMS error of the estimation
type - ML
Given the samples of a log-normal distribution, the PDF parameter is found
fits data to the probability of the form:
p(x) = sqrt(1/(2*pi))/(s*x)*exp(- (log(x-m)^2)/(2*s^2))
with parameters: m,s
format: result = fit_ML_log_normal( x,hAx )
input: x - vector, samples with log-normal distribution to be parameterized
hAx - handle of an axis, on which the fitted distribution is plotted
if h is given empty, a figure is created.
output: result - structure with the fields
m,s - fitted parameters
CRB_m,CRB_s - Cram?r-Rao Bound for the estimator value
RMS - RMS error of the estimation
type - ML - fit_ML_normal - Maximum Likelihood fit of the log-normal distribution of i.i.d. samples!.
Given the samples of a log-normal distribution, the PDF parameter is found
fits data to the probability of the form:
p(x) = sqrt(1/(2*pi))/(s*x)*exp(- (log(x-m)^2)/(2*s^2))
with parameters: m,s
format: result = fit_ML_log_normal( x,hAx )
input: x - vector, samples with log-normal distribution to be parameterized
hAx - handle of an axis, on which the fitted distribution is plotted
if h is given empty, a figure is created.
output: result - structure with the fields
m,s - fitted parameters
CRB_m,CRB_s - Cram?r-Rao Bound for the estimator value
RMS - RMS error of the estimation
type - ML
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fit_ML_maxwell.m