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### Is Bayesian Ridge Regression another name of Bayesian Linear Regression?

I searched about Bayesian Ridge Regression on Internet but most of the result I got is about Bayesian Linear Regression. I wonder if it's both the same things because the formula look quite similar
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### Using prior knowledge about correlated variable in ridge regression

I am wondering what methods are available for incorporating prior knowledge of some variable that is correlated with the unknown regression coefficients in a ridge regression. I have a sparse matrix ...
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### Implementing Bayesian Linear Regression using PyMC3

I am learning a Bayesian Approach towards implementing Linear Regression. The motivation is that Bayesian Approach gives you a range on predictions which might be useful when investing money in ...
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### Is possible to perform a linear regression using log-normal priors?

I am trying to do a Bayesian linear regression. Since my data cannot be negative a gave them a log-Normal distribution, but I am not sure if the priors should be positive also. If I write my model ...
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### What are the assumptions in bayesian statistics?

So, for OLS there are 3 assumptions regarding the DGP, which are (from Stock & Watson): Independence of error terms (+ Homoskedasticity?) IID of variables Large outliers are unlikely, meaning non-...
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### Placing constraints on linear model coefficients

I have this bit of data : ...
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### What is the real-life benefit and application of Bayesian regression [closed]

Question What is the real-life example of the benefit and application of the benefit of Bayesian regression? Having read the items and it looks having the range of inference (possible values and ...
61k views

### Maximum Likelihood Estimation (MLE) in layman terms

Could anyone explain to me in detail about maximum likelihood estimation (MLE) in layman's terms? I would like to know the underlying concept before going into mathematical derivation or equation.
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### Why I should use Bayesian inference with uninformative prior? [duplicate]

I am a Ph.D. student and currently I am studying Bayesian inference concerning vector autoregressive models. A lot of researchers when talking about uninformative prior, conclude that the results of ...
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### Posterior vs conditional probability

When talking about events, there is the following formula called Bayes' rule, where $A$ and $B$ are random events: $$P(A|B)=\frac{P(B|A)P(A)}{P(B)}$$ Now let's say that for now only $A$ happened. I ...
My question is about how to actually do this both rigorously and practically. Allow me to elaborate. Suppose that we have data $(x_1,y_1),...,(x_N,y_N) \in \mathbb{R}^p \times \{0,...,k-1 \}$. I'd ...