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   1. A multi-dimensional probability function preferably a function that specific types functions as parameters e.g. p(x_u(t),x_l(t),y_u(t),y_l(t)[,z_u(t),z_l(t)])    1. A multi-dimensional probability function preferably a function that specific types functions as parameters e.g. $p(x_u(t),x_l(t),y_u(t),y_l(t)[,z_u(t),z_l(t)])$

Dynamic Max Count

This contains the ideas and notes for a Dynamic Max Count or Max-in-time agregate operator

Concept

Instead of using Hyper-buckets that have descrete boundaries and densities which can not be updated resonably using the MaxCountProgramNotes ideas, we propose a probabalistic method where by we put probability densities in space. Each probability density will need the following properties:

  1. Parameters that define the distribution e.g.
    1. Center location
    2. Spatial size
    3. Standard deviation
    4. A measure of symetry or skew
  2. A multi-dimensional probability function preferably a function that specific types functions as parameters e.g. $p(x_u(t),x_l(t),y_u(t),y_l(t)[,z_u(t),z_l(t)])$

DynamicMaxCount (last edited 2020-01-23 22:27:01 by 68)