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| Instead of using Hyper-buckets that have descrete boundaries and densities which can not be updated resonably using the MaxCount ideas, we propose a probabalistic method where by we put probability densities in space. Each probability density will need the following properties: | 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: |
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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:
- Parameters that define the distribution e.g.
- Center location
- Spatial size
- Standard deviation
- A measure of symetry or skew
- 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)])