Distance measure for probability distribution function of...

Data processing: measuring – calibrating – or testing – Measurement system – Statistical measurement

Reexamination Certificate

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C702S069000, C702S110000, C702S189000, C706S012000, C706S014000, C706S015000, C703S002000, C703S005000, C704S200000, C704S240000

Reexamination Certificate

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06993452

ABSTRACT:
In accordance with our invention, for two mixture-type probability distribution functions (PDF's), G, H,G⁡(x)=∑i=1N⁢μi⁢gi⁡(x),⁢H⁡(x)=∑k=1K⁢γk⁢hk⁡(x),where G is a mixture of N component PDF's gi(x), H is a mixture of K component PDF's hk(x), μiand γkare corresponding weights that satisfy∑i=1N⁢μi=1⁢⁢and⁢⁢∑k=1K⁢γk=1;we define their distance, DM(G, H), asDM⁡(G,H)=minw=[ωik]⁢∑i=1N⁢∑k=1K⁢ωik⁢d⁡(gi,hk)where d(gI, hkis the element distance between component PDF's giand hkand w satisfiein-line-formulae description="In-line Formulae" end="lead"?ωik≧0, 1≦i≦N, 1≦k≦K;in-line-formulae description="In-line Formulae" end="tail"?and∑k=1K⁢ωik=μi,1≤i≤N,∑i=1N⁢ωik=γk,1≤k≤K.The application of this definition of distance to various sets of real world data is demonstrated.

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