Bayesian Linear Regression is a statistical technique incorporating Bayesian methods into linear regression. It differs from traditional linear regression by providing not only an estimate for the regression coefficients but also a probability distribution, which gives a range of values that the coefficients can take based on the data. This allows for a more comprehensive understanding of the uncertainty and variability associated with the model's predictions.
In building Bayesian linear regression skills, you need to understand the principles of Bayesian statistics, including concepts like prior and posterior distributions, likelihood, and conjugate priors. You should also be familiar with linear regression and how it models relationships between variables.
Skills in programming languages that support statistical modeling, such as Python or R, would be beneficial. You would also need to learn how to interpret the results of a Bayesian linear regression, including the posterior distributions of the coefficients, and how to use these results to make predictions.
Moreover, understanding how to choose appropriate priors and how to validate and compare models using techniques like cross-validation or Bayesian information criterion (BIC) would be crucial.
Overall, Bayesian Linear Regression offers a more nuanced and probabilistic approach to linear modeling, which can be particularly useful in situations where uncertainty needs to be quantified.‎