How do I generate a pdf from some known percentile values
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Christopher Stokes
am 12 Okt. 2021
Kommentiert: Star Strider
am 13 Okt. 2021
Hi Matlab community,
I have percentile values that describe a distribution of possible sea level rise magnitudes. I would like to be able to generate a probability density function that closely approximates the actual distribution from which the percentile values were generated. Can anyone suggest how to achieve this please?
Example data:
prctiles = [5 10 30 33 50 67 70 90 95];
SLR = [3.2760 3.5265 4.1286 4.2013 4.5566 4.9151 4.9836 5.6045 5.9105];
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Star Strider
am 12 Okt. 2021
A slightly different approach —
prctiles = [5 10 30 33 50 67 70 90 95];
SLR = [3.2760 3.5265 4.1286 4.2013 4.5566 4.9151 4.9836 5.6045 5.9105];
B = fminsearch(@(b) norm(prctiles/100 - cdf('Normal',SLR,b(1),b(2))), [SLR(prctiles==50);rand])
SLRv = linspace(min(SLR), max(SLR));
yfit = cdf('Normal', SLRv, B(1), B(2));
figure
plot(SLR, prctiles/100, 'p')
hold on
plot(SLRv, yfit, '-r')
hold off
grid
title(sprintf('$p = N(%.2f, %.3f)$',B), 'Interpreter','latex')
legend('SLR','Fitted Noprmal Distribution', 'Location','NW')
Experiment to get different results.
.
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Image Analyst
am 12 Okt. 2021
Have you seen fitdist() in the Stats toolbox?
Of course you'd be better off with much more data.
1 Kommentar
Image Analyst
am 12 Okt. 2021
Bearbeitet: Image Analyst
am 12 Okt. 2021
Here's an example:
clc; % Clear the command window.
fprintf('Beginning to run %s.m ...\n', mfilename);
close all; % Close all figures (except those of imtool.)
clear; % Erase all existing variables. Or clearvars if you want.
workspace; % Make sure the workspace panel is showing.
format long g;
format compact;
fontSize = 17;
SLR = [3.2760 3.5265 4.1286 4.2013 4.5566 4.9151 4.9836 5.6045 5.9105];
% Plot data.
subplot(2, 1, 1);
bar(SLR)
grid on;
title('Original SLR Data', 'FontSize', fontSize);
xlabel('Index', 'FontSize', fontSize);
ylabel('SLR Value', 'FontSize', fontSize);
% Get distribution.
d = fitdist(SLR(:), 'Normal')
% Make curve, plot distribution.
% https://en.wikipedia.org/wiki/Normal_distribution
x = linspace(min(SLR), max(SLR), 1000);
amp = 1 / (d.sigma * sqrt(2*pi));
y = amp * exp(-(1/2) * ((x - d.mu) / d.sigma) .^ 2)
subplot(2, 1, 2);
plot(x, y, 'b-', 'LineWidth', 2);
grid on;
title('Estimated Distribution of SLR', 'FontSize', fontSize);
xlabel('SLR', 'FontSize', fontSize);
ylabel('PDF', 'FontSize', fontSize);
Pick the distribution that fits the theory of what the distribution should actually be. Hopefully you know this in advance. Actually you need to if you're going to model it. Otherwise just normalize your histogram and that is the actual PDF.
Siehe auch
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