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In the geometric model, the total sample space is represented as a large square. The prior probability $P(H)$ determines the width of a vertical strip representing the hypothesis, and the likelihood $P(E|H)$ determines the height of the shaded region within that strip where the evidence occurs.

Conditions: Total sample space is normalized to unit area; Events H and E are treated as measurable subsets

In the geometric model, the total sample space is represented as a large square. The prior probability $P(H)$ determines the width of a vertical strip representing the hypothesis, and the likelihood $P(E|H)$ determines the height of the shaded region within that strip where the evidence occurs.

Conditions: Total sample space is normalized to unit area; Events H and E are treated as measurable subsets