Systems and methods for optimal bidding in a business to business environment

Abstract

The present invention relates to systems and methods for optimizing bidding in a business-to-business environment. Initially the observed outcomes for n deals are received, and the belief parameters for these n deals are calculated. The Bayes-greedy price is then calculated and presented to a buyer. The buyer's response is collected and an optimal variance parameter based on the buyer's response is generated. The belief parameters for these n+1 deals are also updated. This process may be repeated for additional deals.

1 . A method for reducing computations of non-normal data with a processor, comprising:
 receiving, at a processor, features of n events;   receiving, at the processor, observed outcomes of the n events;   calculating, via the processor, a normal distribution of the observed outcomes based on the features; and   when a new event occurs:
 calculating a posterior distribution representing regression coefficients of the observed outcomes with the new event, the posterior distribution being a non-normal distribution; 
 replacing, within memory associated with the processor, the posterior distribution with a normal approximated distribution of the posterior distribution; 
 calculating a projected outcome of the new event using the normal approximated distribution with the formula: p k−1 =p k −a k ∇ p     k   R(p k :x, β); and 
 outputting the projected outcome to a display.