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	3.5 Evidence Approximation - Introduction (Sina Tootoonian) View
	Roadmap to Section 3.5 Evidence Approximation - Pattern Recognition and Machine Learning (Sina Tootoonian) View
	3.5.1 Evaluation of the Evidence Function - Pattern Recognition and Machine Learning (Sina Tootoonian) View
	3.5.2 Maximizing the Evidence Function - Pattern Recognition and Machine Learning (Sina Tootoonian) View
	Chapter 3.5 - Exercise 3/4 - Hypothesis Test for P (Introduction to Statistics at SLCC) View
	Chapter 3.5 - Learning Objective 8 (Introduction to Statistics at SLCC) View
	Confidence Interval [Simply explained] (DATAtab) View
	3.5.3 Orders of Magnitude and Logarithmic Models (Justin Backeberg) View
	Ch. 3.5 - Practice 2 (Daniel Lee) View
	Equilibrium 3.5 Calculating K (David Perdian) View