PostHog is an all-in-one, open-source platform providing web and product analytics, session recording, feature flagging, and A/B testing. It supports self-hosting and offers functionalities such as event-based analytics, user and group tracking, data visualizations, session replays, heatmaps, feature flags, experiments, surveys, and more.
A step-by-step guide to making data-driven decisions with practical Python examples, covering the process of hypothesis testing, different types of tests, understanding p-values, and interpreting the results of a hypothesis test.
- Contextual bandits, a dynamic approach to treatment personalization
- Differences between contextual bandits, multi-armed bandits, A/B testing, multiple MABs, multi-step reinforcement learning, and uplift modeling
- Exploration and exploitation strategies, including ε-greedy, upper confidence bound, and Thompson sampling
- Differences between contextual bandits, multi-armed bandits, A/B testing, multiple MABs, multi-step reinforcement learning, and uplift modeling
- Exploration and exploitation strategies, including ε-greedy, upper confidence bound, and Thompson sampling
