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    Introduction to Bayesian reasoning

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    • Introduction to Bayesian Reasoning
      • 1.1What is Bayesian Reasoning
      • 1.2Importance and Applications of Bayesian Reasoning in Decision Making
      • 1.3Fundamentals of Probability in Bayesian Reasoning
    • Historical Perspective of Bayesian Reasoning
      • 2.1Single Event Probabilities
      • 2.2From Classical to Bayesian Statistics
      • 2.3Bayes' Theorem – The Math Behind It
    • Understanding Priors
      • 3.1Importance of Priors
      • 3.2Setting your Own Priors
      • 3.3Pitfalls in Selection of Priors
    • Implementing Priors
      • 4.1Revision of Beliefs
      • 4.2Bayesian vs Frequentist Statistics
      • 4.3Introduction to Bayesian Inference
    • Advanced Bayesian Inference
      • 5.1Learning from Data
      • 5.2Hypothesis Testing and Model Selection
      • 5.3Prediction and Decision Making
    • Bayesian Networks
      • 6.1Basic Structure
      • 6.2Applications in Decision Making
      • 6.3Real-life examples of Bayesian Networks
    • Bayesian Data Analysis
      • 7.1Statistical Modelling
      • 7.2Predictive Inference
      • 7.3Bayesian Hierarchical Modelling
    • Introduction to Bayesian Software
      • 8.1Using R for Bayesian statistics
      • 8.2Bayesian statistical modelling using Python
      • 8.3Software Demonstration
    • Handling Complex Bayesian Models
      • 9.1Monte Carlo Simulations
      • 9.2Markov Chain Monte Carlo Methods
      • 9.3Sampling Methods and Convergence Diagnostics
    • Bayesian Perspective on Learning
      • 10.1Machine Learning with Bayesian Methods
      • 10.2Bayesian Deep Learning
      • 10.3Applying Bayesian Reasoning in AI
    • Case Study: Bayesian Methods in Finance
      • 11.1Risk Assessment
      • 11.2Market Prediction
      • 11.3Investment Decision Making
    • Case Study: Bayesian Methods in Healthcare
      • 12.1Clinical Trial Analysis
      • 12.2Making Treatment Decisions
      • 12.3Epidemic Modelling
    • Wrap Up & Real World Bayesian Applications
      • 13.1Review of Key Bayesian Concepts
      • 13.2Emerging Trends in Bayesian Reasoning
      • 13.3Bayesian Reasoning for Future Decision Making

    Introduction to Bayesian Reasoning

    Understanding Bayesian Reasoning

    British mathematician and Presbyterian minister (*1701 – †1761)

    British mathematician and Presbyterian minister (*1701 – †1761).

    Bayesian reasoning is a method of thought (named for Thomas Bayes) that deals with probability inference. It is a way of updating probabilities of hypotheses when given evidence. It follows the principle of the Bayes' theorem, which is fundamental to Bayesian statistics and Bayesian machine learning models.

    What is Bayesian Reasoning?

    At its core, Bayesian reasoning is a way of understanding the world and making decisions based on probabilistic inference. It is a method of statistical inference in which Bayes' theorem is used to update the probability for a hypothesis as more evidence or information becomes available.

    Bayesian reasoning is not just a statistical technique. It is a comprehensive philosophical approach to understanding uncertainty and making sound decisions under uncertainty. It is a way of approaching any problem that involves making a decision under uncertainty.

    Basic Principles of Bayesian Reasoning

    The basic principle of Bayesian reasoning is to adjust your existing beliefs about the world based on new evidence. This is done by applying Bayes' theorem, which mathematically describes how to update probabilities based on new data.

    In Bayesian reasoning, you start with a prior belief, which is your initial assumption about the world. Then, as you gather new evidence, you update your belief to form a posterior belief. The strength of your updated belief depends on the strength of your initial belief and the strength of the new evidence.

    Difference Between Bayesian Reasoning and Other Forms of Reasoning

    The key difference between Bayesian reasoning and other forms of reasoning lies in how they handle uncertainty. Traditional forms of reasoning, such as deductive or inductive reasoning, often deal with certainties. They start with premises and apply logical rules to arrive at a certain conclusion.

    On the other hand, Bayesian reasoning deals with probabilities. It acknowledges that the world is uncertain and that we can only make probabilistic statements about it. Instead of starting with premises and arriving at conclusions, Bayesian reasoning starts with an initial belief and updates that belief based on new evidence.

    In conclusion, Bayesian reasoning is a powerful tool for understanding the world and making decisions under uncertainty. It is a method of thought that deals with updating probabilities based on new evidence. By understanding Bayesian reasoning, we can make more informed and rational decisions in our everyday lives.

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