@online{huang2025bayesian,
  author       = {Huang, Xuanqiang Angelo},
  title        = {Bayesian Linear Regression},
  date         = {2025-01-15},
  organization = {Xuanqiang Angelo Huang's Blog},
  url          = {https://flecart.github.io/notes/bayesian-linear-regression/},
  langid       = {english},
  abstract     = {We have a prior p ( model ) , we have a posterior p ( model ∣ data ) , a likelihood p ( data ∣ model ) and p ( data ) is called the evidence . Classical Linear regression \# Let's start with a classical regression. In this setting we need to estimate a model that is generated from}
}