Linked Questions

0
votes
1answer
52 views

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 ...
6
votes
2answers
437 views

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-...
0
votes
1answer
18 views

Placing constraints on linear model coefficients

I have this bit of data : ...
3
votes
1answer
982 views

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 ...
94
votes
12answers
58k 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.
12
votes
4answers
2k views

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 ...
3
votes
1answer
2k views

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 ...
4
votes
1answer
807 views

Questions on Bayesian Softmax Regression [closed]

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 ...
3
votes
1answer
1k views

Estimation of Bayesian Ridge Regression

According to scikit-learn, by using a probabilistic model : $p(y|X,\omega,\alpha) = \mathcal{N}(y|X\omega,\alpha)$ with $\omega$ given by a spherical Gaussian: $p(\omega|\lambda) = \mathcal{N}(\...
9
votes
1answer
5k views

Is Bayesian Ridge Regression another name of Bayesian Linear Regression?

I searched about Bayesian Ridge Regression on Internet but most of the result i became is about Bayesian Linear Regression. I wonder if it's both the same things because the formula look quite similar
2
votes
2answers
5k views

Sum of squared difference and Gaussian noise model

I have been reading that when the underlying error is distributed normally, then minimising the sum of squared difference between the observed data and the model is the appropriate cost function to do ...
0
votes
1answer
464 views

Bayesian smoothing using Dirichlet prior : why not MAP?

I am reading about smoothing methods for language model ( I am working on unigram model). If you are not familiar with unigram model, it is closely related to multinomial distribution (with the ...
2
votes
1answer
155 views

Intuition on simple linear regression signal plus noise model

I'm currently studying linear regression on this book "F.M. Dekking - A Modern Introduction to Probability and Statistics: Understanding Why and How" where the signal+noise model is presented: $Y_i =...
0
votes
1answer
44 views

what is the posterior in the case of regression

I am having trouble "mapping" the variables in the Bayes equation onto the case of regression. As notation, say $$ P(\theta|D) = \frac{P(D|\theta) P(\theta)}{ P(D) } $$ I have come to think of $\...
1
vote
1answer
59 views

Extra information at prediction time when using a Bayesian logistic regression vs. normal

I have a binary classification problem (i.e. is observation positive or negative) and I'm interested in what information I can obtain about observations in my test set. I don't care about the model ...

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