Multi-resolution modeling of discrete stochastic processes for computationally-efficient information search and retrieval

A method of information search and retrieval associated with a non-stationary discrete stochastic process, wherein events of interest occur within the process approximately independently at units i within a region R, and with an unknown rate λR that is approximately constant across R, wherein λR has an associated estimation uncertainty defined by a set of parameters that include expectation μR and variance σR2, comprising:

• responsive to a set of input data having a first dimensionality, training a model to predict rate parameters and their associated estimation uncertainty for each region R, the model being trained by:
• processing the input data through a first process that maps patterns in the input data to a reduced data set having a second dimensionality that is at least an order of magnitude less than the first dimensionality, and
• processing the reduced set through a second process to output a distribution of the events of interest and their associated estimation uncertainty for each region R;
• and
• receiving a query identifying a set of indexed positions of the stochastic process of interest;
• responsive to receipt of the query, and using a function computed in constant time, scaling the rate parameters and their associated estimation uncertainties from the trained model to obtain a response to the query, the response being a distribution of the events of interest and their associated estimation uncertainties for the set of indexed positions;
• and providing the response for use in a target application.