Many projects in Engineering and Computer Science involve global multivariate optimisation. Classical methods include genetic algorithms, simulated annealing and particle filters. One way to think about global optimization is as density estimation, where Markov chain Monte Carlo methods attempt to learn the properties of an optimisation surface as if it were a probability distribution. This project is about doing that surface characterisation in a new way. The idea is to begin local ascent algorithms from multiple start points and then use the information about distance and directions travelled in each case to estimate maxima density. From this, we would make principled choices about how many starts to attempt and stopping criteria for a high probability of finding the global maximum.