Follow-up editorial pass on two lectures.

bayes_intro.md

• Add an overview flowchart showing prior belief + data → updated belief, drawn inline with matplotlib in a hide-input cell (no new dependencies, renders at build time).
• Note that the technique is named after Thomas Bayes (with link).
• Tighten the prose around the prior and the normalizing constant: define $c(y)$ explicitly and present the trapezoidal rule as a numbered list.
• Fix a missing period and some trailing whitespace.

prob_dist.md

• Rename the lecture from "Distributions and Probabilities" to "Common Distributions" (title + index entry).

Verified the Bayesian lecture executes end-to-end via jupytext --to py.

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