Bayesian inference is powerful for updating risk evaluations based on new evidence, yet it comes with challenges:
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Complexity - Bayesian models necessitate extensive statistical expertise and specific tools (such as Python, R, or Bayesian networks).
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Data Dependency - The accuracy of Bayesian inference is dependent on the quality and quantity of historical data available.
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Computationally Intensive - Large-scale Bayesian simulations can be resource-intensive and need significant processing resources.
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Subjectivity in Prior Probabilities - The initial probability assumptions (priors) might influence results, potentially leading to bias.
Despite these obstacles, Bayesian inference is effective for projects with dynamic risks that necessitate continual learning and modification.