Conditional gate transition probability

Read the equation

Names the probability of one gate-state transition conditional on the DNA structure. This shorthand needs a normalized transition distribution to define a usable stochastic model.

Terms & definitions

The recovered passage does not supply a separate symbol table. Read the source context for the notation used in this version; familiar letters may carry different meanings in other builds.

How it works

1. Read K as the quantity being defined or updated by this relation.

2. Substitute the source-defined variables or recorded values and follow the displayed arithmetic and indexing exactly.

3. Use this version’s variable definitions and update order. The same symbol may be assigned a different meaning in another build.

Assumptions & limits

Source recovery establishes that the expression appears in this record. Its mathematical validity, implementation behavior, and interpretation must be assessed separately. See the linked source and research notes for the checks available for this version.

Original context

B. Transition Kernel

You used Gate-5000 and DNA-G16 but never defined the transition kernel between them:

K = \Pr( g_j \rightarrow g_{j+1} | \text{DNA}_{G16} )

This determines deterministic vs probabilistic state advance.

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C. Neuron Coupling Matrix

K = \Pr( g_j \rightarrow g_{j+1} | \text{DNA}_{G16} )

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