您可以使用

bootstrapping估計

confidence intervals.MATLAB在統(tǒng)計工具箱中提供

bootci功能.這是一個例子：

%# generate a random cell array of 400 sequences of varying length

%# each containing indices from 1 to 5 corresponding to ACGTE

sequences = arrayfun(@(~) randi([1 5], [1 randi([500 1000])]), 1:400, ...

'UniformOutput',false)';

%# compute transition matrix from all sequences

trans = countFcn(sequences);

%# number of bootstrap samples to draw

Nboot = 1000;

%# estimate 95% confidence interval using bootstrapping

ci = bootci(Nboot, {@countFcn, sequences}, 'alpha',0.05);

ci = permute(ci, [2 3 1]);

我們得到：

>> trans %# 5x5 transition matrix: P_hat

trans =

0.19747 0.2019 0.19849 0.2049 0.19724

0.20068 0.19959 0.19811 0.20233 0.19928

0.19841 0.19798 0.2021 0.2012 0.20031

0.20077 0.19926 0.20084 0.19988 0.19926

0.19895 0.19915 0.19963 0.20139 0.20088

和另外兩個類似的矩陣,包含置信區(qū)間的下限和上限：

>> ci(:,:,1) %# CI lower bound

>> ci(:,:,2) %# CI upper bound

我使用以下函數(shù)從一組序列計算轉(zhuǎn)換矩陣：

function trans = countFcn(seqs)

%# accumulate transition matrix from all sequences

trans = zeros(5,5);

for i=1:numel(seqs)

trans = trans + sparse(seqs{i}(1:end-1), seqs{i}(2:end), 1, 5,5);

end

%# normalize into proper probabilities

trans = bsxfun(@rdivide, trans, sum(trans,2));

end

作為獎勵,我們可以使用bootstrp函數(shù)來獲取從每個bootstrap樣本計算的統(tǒng)計量,我們使用它來顯示轉(zhuǎn)換矩陣中每個條目的直方圖：

%# compute multiple transition matrices using bootstrapping

stat = bootstrp(Nboot, @countFcn, sequences);

%# display histogram for each entry in the transition matrix

sub = reshape(1:5*5,5,5);

figure

for i=1:size(stat,2)

subplot(5,5,sub(i))

hist(stat(:,i))

end

總結(jié)

以上是生活随笔為你收集整理的matlab求马尔可夫转移矩阵,matlab – 估计马尔可夫转移矩阵的置信区间的全部內(nèi)容，希望文章能夠幫你解決所遇到的問題。

如果覺得生活随笔網(wǎng)站內(nèi)容還不錯，歡迎將生活随笔推薦給好友。