Bayesian Estimation of Parameters
Joachim Köppen Strasbourg 2011
Short explanations:
- we compute the likelihood that an observed data set (a Gaussian curve of given
height, width, and position of the centre, subjected to
Gaussian noise with a RMS amplitude) is represented by
model parameters (Analysis: RMS noise, height, width, center
position). Each time, the Compute button is clicked, a new
data set is generated. It is shown in the top panel, if onely
one parameter is selected for Plot
- the results are shown as the distributions of the likelihood
for the parameters which had been selected for plotting: hence
it can be a curve in 1D or a false colour map in 2D.
- click on the lower left buttons to select what you want
to do:
- plot select for plotting. If more than 2 parameters
are selected, only the first 2 are plotted, while the other
ones are held fixed to the value currtly displayed in the
field!
- lock this parameter will have the same value
as given as taken for the observed data, in the upper
left section
- enter (grey color) this parameter will be fixed
to the value given in this field
- integ. the likelihood is integrated over the entire
range of values for this parameter. This parameter is treated
as a "nuisance parameter". As this computation may take some
time, the Compute button's magenta colour changes during
execution.
- the likelihood densities are shown between the maximum value
(normalized to 1) and a minimum value (Ymin givin in log10 value).
The button toggles between several values for Ymin.