Pressing onwards with research after a short break to play with the Xeon Phi rack, I’ve been working on visualizations for Monte Carlo Simulations.
My aim is to have a clean and concise means of displaying (and therefore being able to infer relationships from) the data of higher dimensional probability distributions. The video below shows one such visualization where a 2D Gaussian PDF has been simulated using Hamiltonian Dynamics.
Hamiltonian Dynamics Monte Carlo
The two 3D meshes represent the reconstructed sample volume as a 2D histogram of values rescaled to the original function. The error between the reconstruction and the original curves is shown through the colour of the surface where hot spots denote areas of high error.
The mean sq error for the whole distribution is displayed in the top right on a logarithmic scale. An algorithm which converges in an ideal manner will graph it’s error as a straight line on the log scale.
The sample X and Y graphs in the bottom right allow us to visualize where the samples are being chosen as the simulation runs and infer about whether samples are being chosen independently of one another. The centre-bottom graph simply gives us a trajectory of samples over the course of the simulation. This allows us to additionally catch if an algorithm is prone to getting stuck in local maxima’s.
Metropolis Hastings Monte Carlo
The above video shows the same simulation run with a basic Metropolis Hastings algorithm. Here the proposal sample
y attributes are chosen independently of one another, meaning this is not a Gibbs Sampler. Although I intend to implement one within the test framework for comparisons sake soon.
A key difference between the two simulations here is to note the Path Space Trajectory shown in the bottom-centre graph and how it relates to the Sample Dimension Space graphs shown on the middle & bottom-right. Hamiltonian Dynamics chooses sets of variables where the
y elements are highly dependent on one another within a coordinate, yet almost entirely independent of other pairs of coordinates within the Path Space.
Metropolis Hastings on the other hand, chooses coordinate pairs entirely independently of one another, and values within each dimension of the Path Space that are highly dependent on one another. These characteristics of the Metropolis sampler are undesirable, which is why adding a dependency within coordinate pairs (Gibbs Sampling) helps to accelerate multi-dimensional Metropolis Samplers.