# Simple Example

We now validate the proposition above, that solving for the maximum entropy multivariate truncated exponential (MaxEnt-MTE) distribution approximates the asymptotic distribution of MCMC-UMS for doubly-bounded data. In this experiment, we used a data size of . The matrix in (5.1) computed the first Coefficients of the length- DCT of . We created a data sample using a raised sine-wave plus Gaussian noise, clipped it to the interval [0,1], then computed the feature . The original was then discarded. We then got a starting point for using a linear programming solver as previously explained, with the lower and upper bounds of 0 and . We then maximized (6.6) over subject to (5.12) using the method above. Figure 6.2 shows the results. See software/test_lin01.m.
The sample-mean of MCMC-UMS after 10000 samples matches the Maximum entropy mean solution as close as could be determined.

Baggenstoss 2017-05-19