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**Maximum Likelihood — formal statement.** - **Assumption:** data for each class is **normally distributed** (Gaussian) in feature space. - Let **C = (C₁, C₂, …, C_nc)** be the set of *nc* classes. - For a pixel with gray-level vector **x**, compute posterior probability **P(Cᵢ | x)** for every class. - Assign the pixel to **Cᵢ** if `P(Cᵢ | x) ≥ P(Cⱼ | x)` for all *j ≠ i* — i.e., the class with the highest posterior wins. - Reference: Gong lecture notes, UC Berkeley (nature.berkeley.edu).

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