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Model-Free Approximate Bayesian Learning for Large-Scale Conversion Funnel Optimization
We model consumer behavior by a conversion funnel that captures the state of each consumer (e. g., interaction history with the firm) and allows the consumer behavior to vary as a function of both her state and firm's sequential interventions.
Counterfactual Analysis in Dynamic Latent State Models
We provide an optimization-based framework to perform counterfactual analysis in a dynamic model with hidden states.
A Finite Time Analysis of Temporal Difference Learning With Linear Function Approximation
Temporal difference learning (TD) is a simple iterative algorithm used to estimate the value function corresponding to a given policy in a Markov decision process.
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