For idealized (spherical, smooth) dark matter halos described by single-parameter density profiles (such as the Navarro–Frenk–White profile), there exists a one-to-one mapping between energy of halo and scale radius its profile. The therefore uniquely determines concentration parameter such halos. We exploit this fact to predict concentrations via random walk in space. Given full merger tree fo...

We consider a natural framework of learning from correlated data, in which successive examples used for learning are generated according to a random walk over the space of possible examples. Previous research has suggested that the Random Walk model is more powerful than comparable standard models of learning from independent examples, by exhibiting learning algorithms in the Random Walk framew...

We propose a delayed random walk model of stochastic single neuron which has a delayed self{exciting feedback. Using computer simulation the model is studied to examine how the delay in the self{excitation aaects the bistable stochastic dynamics of the model neuron. It is found that progressive delay causes the width of two bimodal peaks of the stationary probability distribution to increase, a...