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Recently, I have been reading Reinforcement Learning: An Introduction by Sutton and Barto. In Chapter 3, Session 5, I found the derivation of the Bellman Equation hard to follow. I hope this supplementary note can provide information to the coming students. The section contains all the necessary information to understand

The example presented in Reinforcement Learning: An Introduction by Sutton and Barto. A possible value added by this article is to provide a more detailed explanation and code snippets to replicate the example. The 10-armed Bandit Example For readers not familiar with the k-armed bandit problem, you are repeatedly faced

TLDR For two groups with a two-sided alternative, normal distribution with homogeneous variances ( \(\sigma_0^2 = \sigma_1 ^2 = \sigma ^2)\) and equal sample sizes (\(n_0 = n_1 = n\)), \[n = \frac{16}{\Delta^2}\] where \[\Delta = \frac{\mu_0 - \mu_1}{\sigma} = \frac{\delta}{\sigma}\] Examples: What sample

Hello Brilliant Minds and Passionate Creators, Welcome to this week’s edition of our journey together in the fascinating world of "Building in Public." Each step we take is a story of discovery, learning, and sharing, aimed at not just building projects but fostering a vibrant community of

1. Fundamentals 1. The Art and Science of Decision-Making 2. Fundamentals of Interest and Loan 2. Key Objective and Its Components 1. The Balance Between Risk and Reward 2. Commonly Used Risk Metrics 3. Prediction into the Future 1. Estimating Long-term Impact from Short-term Outcome 2. Creating Models of the

This week, I compared the difference between the Potential Outcome Model (Rubin Casual Model) and Structural Causal Model frameworks for causal inference. I failed to grasp the difference between the two. Still, I can share some resources on this matter. While I believe the comparison has more of a theoretical