# Linked Questions

11answers
54k 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.
3answers
5k views

### Do Bayesian priors become irrelevant with large sample size?

When performing Bayesian inference, we operate by maximizing our likelihood function in combination with the priors we have about the parameters. Because the log-likelihood is more convenient, we ...
3answers
14k views

### Bayesian updating with new data

How do we go about calculating a posterior with a prior N~(a, b) after observing n data points? I assume that we have to calculate the sample mean and variance of the data points and do some sort of ...
2answers
16k views

### Bayesian logit model - intuitive explanation?

I must confess that I previously haven't heard of that term in any of my classes, undergrad or grad. What does it mean for a logistic regression to be Bayesian? I'm looking for an explanation with a ...
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 ...
1answer
4k 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
2answers
4k 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 ...
1answer
887 views

### What exactly is a hyperparameter?

Title says it all. I have seen both "the hyperparameter of the Dirichlet distribution" and "the parameter of the Dirichlet distribution" What are the differences?
1answer
1k 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 ...
1answer
805 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 ...
1answer
941 views

1answer
44 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 ...
1answer
41 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 \$\...
1answer
17 views

### Placing constraints on linear model coefficients

I have this bit of data : ...