Prior, Likelihood, and Posterior: Core Bayesian Concepts - Deepstash

Prior, Likelihood, and Posterior: Core Bayesian Concepts

  • Bayesian reasoning uses prior knowledge, likelihood (data evidence), and posterior beliefs to refine probability estimates
  • Understanding these elements helps quantify belief systems and adjust them with each new piece of information

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gbiondizoccai

Giuseppe Biondi-Zoccai is a renowned expert in cardiology, medical research methodology and evidence synthesis

Will Kurt's Bayesian Statistics the Fun Way introduces Bayesian thinking as a method to update beliefs based on new data. It contrasts Bayesian probability with frequentist methods, emphasizing subjective belief and its evolution. The book is important for its practical, accessible approach to using Bayesian reasoning in everyday decision-making and data analysis

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