Disadvantages of Using Bayesian Statistics
- Computational Complexity: Bayesian methods can be computationally intensive, especially for complex models or large datasets, requiring specialized software and computational resources.
- Subjectivity in Prior Selection: The choice of prior distribution can influence the results of Bayesian analysis, and selecting an appropriate prior can be subjective and challenging.
- Interpretation Complexity: Bayesian inference can be more challenging to interpret than traditional frequentist methods, particularly for individuals unfamiliar with Bayesian concepts such as posterior distributions and credible intervals.
Frequentist vs Bayesian Approaches in Machine Learning
Frequentist and Bayesian approaches are two fundamental methodologies in machine learning and statistics, each with distinct principles and interpretations. Here, we will see how these two approaches differ.
Table of Content
- Frequentist vs. Bayesian Approach
- What is the Frequentist Approach?
- What are the Tests Frequentists Use?
- Advantages of Using Frequentist Statistics
- Disadvantages of Using Frequentist Statistics
- What is the Bayesian Approach?
- What are the Tests Bayesian Use?
- Advantages of Using Bayesian Statistics
- Disadvantages of Using Bayesian Statistics
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