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First, assign any values to unknown parameters, {S<sub>m</sub>}. Then, compute association variables f(m|n),
:<big>f(m|n) = r(m) l(X(n)|m) / ∑<sub>m'=1..M</sub> r(m') l(X(n)|m').</big>
Equation for f(m|n) looks like the Bayes formula for a posteriori probabilities; if l(n|m) in the result of learning become conditional likelihoods, f(m|n) become Bayesian probabilities for signal n originating from object m. The dynamic logic of the Modeling Fields (MF) is defined as follows
:<big>df(m|n)/dt = f(m|n) ∑<sub>m'=1..M</sub> {[δ<sub>mm'</sub> - f(m'|n)] • [∂ln l(n|m')/∂M<sub>m'</sub>] ∂M<sub>m'</sub>/∂S<sub>m'</sub> dS<sub>m'</sub>/dt,</big>
:<big>dSm/dt = ∑<sub>n=1..N</sub> f(m|n)[∂ln l(n|m)/∂M<sub>m</sub>]∂M<sub>m</sub>/∂S<sub>m</sub>,</big>
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