bayesian Sentence Examples
- Bayesian probability is a method of statistical inference that uses Bayes' theorem to update beliefs as new evidence arises.
- Bayesian analysis incorporates prior knowledge or assumptions into the statistical model, which is not typically done in frequentist statistics.
- Bayesian inference relies on the probability distribution of the parameters of interest, leading to more nuanced and intuitive interpretations.
- Bayesian hierarchical modeling allows for the estimation of complex multivariate relationships with varying degrees of uncertainty.
- Bayesian approaches offer more flexibility in model specification and parameter estimation, particularly in cases of small sample sizes or missing data.
- Bayesian inference is commonly used in various fields, including machine learning, data science, and medical research.
- Bayesian networks provide a graphical representation of the probabilistic relationships between variables, facilitating the understanding of complex systems.
- Bayesian optimization combines Bayesian inference and sequential learning to optimize complex functions with minimal computational cost.
- Bayesian A/B testing allows for the efficient comparison of different interventions by incorporating prior knowledge and ongoing data to update beliefs.
- Bayesian methods continue to gain popularity due to their ability to handle uncertainty, incorporate prior information, and provide more robust and interpretable results.
bayesian Meaning
Wordnet
bayesian (a)
of or relating to statistical methods based on Bayes' theorem
Synonyms & Antonyms of bayesian
No Synonyms and anytonyms found
FAQs About the word bayesian
of or relating to statistical methods based on Bayes' theorem
No synonyms found.
No antonyms found.
Bayesian probability is a method of statistical inference that uses Bayes' theorem to update beliefs as new evidence arises.
Bayesian analysis incorporates prior knowledge or assumptions into the statistical model, which is not typically done in frequentist statistics.
Bayesian inference relies on the probability distribution of the parameters of interest, leading to more nuanced and intuitive interpretations.
Bayesian hierarchical modeling allows for the estimation of complex multivariate relationships with varying degrees of uncertainty.
