Which mathematical approach is most commonly used in modern dynamic pricing algorithms that helped Amazon increase its profit margin by 25% through strategic price optimization?

Dynamic pricing strategies have revolutionized modern retail by applying sophisticated mathematical models to maximize profits. These algorithms process vast amounts of data including competitor prices, customer behavior patterns, inventory levels, and market demand to determine optimal price points in real-time. How well do you understand the mathematics behind today's most successful retail pricing models?

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Bayesian inference models that update pricing probabilities based on real-time consumer behavior patterns
Reinforcement learning algorithms that balance exploration and exploitation to maximize revenue over time
Monte Carlo simulations that predict optimal price points based on thousands of randomized market scenarios
Euclidean distance calculations that cluster similar products for standardized pricing strategies