Periodic probability learning

Estimating Conditional Probability Densities for Periodic Variables

Most of the common techniques for estimating conditional probability densities are inappropriate for applications involving periodic variables. In this paper we introduce three novel techniques for tackling such problems, and investigate their performance using synthetic data. We then apply these techniques to the problem of extracting the distribution of wind vector directions from radar scatt...

Minimum Probability Flow Learning

Learning Probability Distributions

Learning Probability Distributions

Minimum Probability Flow Learning

Fitting probabilistic models to data is often difficult, due to the general intractability of the partition function and its derivatives. Here we propose a new parameter estimation technique that does not require computing an intractable normalization factor or sampling from the equilibrium distribution of the model. This is achieved by establishing dynamics that would transform the observed da...

Probability learning in 1000 trials.

This paper reports a simple two-alternative noncontingent probability learning experiment with an uncon-ventional feature: each 5 made 1000 consecutive predictions, making possible very detailed analysis of responses which occurred after learning was essentially completed. Some abbreviations will be useful. The 5 1 predicts either L or R; after each prediction he observes either 1 or r. The pro...