Linked Questions

4 votes
1 answer
4k views

why maximize likelihood, rather than maximizing the inverse of the likelihood? [duplicate]

Let $X$ be a vector of sample data, and $W$ is a vector of parameters of a model based on that data, and we want to estimate a vector $W^*$ that is, informally speaking, "as close to $W$ as possible". ...
user56834's user avatar
  • 2,859
2 votes
3 answers
2k views

Why maximize the likelihood? [duplicate]

What properties of the MLE makes it so useful for picking parameters? Why would we want to maximize the likelihood? Why not maximize $P(\text{Parameter} \mid \text{Data})$ or anything else?
user avatar
1 vote
1 answer
482 views

Parameter estimation problem: maximum likelihood [duplicate]

Suppose I have some observations $x_{1}, x_{2}, \dots, x_{n}$. I also have a probability density function with one unknown parameter $\theta$. I would like to find such $\theta$, which would give the ...
emml's user avatar
  • 21
1 vote
1 answer
358 views

What is the meaning of maximum likelihood estimation? [duplicate]

From what I understand, MLE for a model helps us find out what parameters in the model will suit the data most. Thus, in linear regression, we try to find $L(\theta)$ as $\prod_{i=1}^{m}p(y|x;\theta)$...
skyquake's user avatar
  • 105
4 votes
0 answers
489 views

Maximum Likelihood Estimation [duplicate]

Can someone please explain Maximum Likelihood Estimation in very lay-man terms? I understand that the Likelihood function is obtained by multiplying all the probabilities in the distribution. Or if I ...
user1343318's user avatar
  • 1,351
0 votes
0 answers
157 views

What is maximum likelihood estimation in logistic regression? [duplicate]

Can you please explain in simple way. Is it so important in logistic regression?
user avatar
0 votes
0 answers
40 views

Maximum likelihood estimation of parameters [duplicate]

This is more of a question about my understanding of the concept; suppose we have a normal linear model $Y = X\boldsymbol{\beta}+\epsilon$, where $\epsilon \sim N(0,\sigma^{2}I_{n})$ and $\boldsymbol{\...
python_learner's user avatar
1 vote
0 answers
27 views

Intuition Maximum likelihood [duplicate]

Can someone describe in simple words what the Intuition behind maximum likelihood estimation is and why it is so commonly used in statistics? Certainly, there are many other ways to estimate ...
James 's user avatar
  • 19
0 votes
0 answers
10 views

Is my explanation of how MLE works is correct? [duplicate]

The likelihood of observing the dataset we have (for some mean and variance) can be written as the product of the likelihood of observing each data point (since all the rows are independent). Now, ...
user avatar
72 votes
18 answers
90k views

Statistics interview questions

I am looking for some statistics (and probability, I guess) interview questions, from the most basic through the more advanced. Answers are not necessary (although links to specific questions on this ...
66 votes
14 answers
23k views

If we fail to reject the null hypothesis in a large study, isn't it evidence for the null?

A basic limitation of null hypothesis significance testing is that it does not allow a researcher to gather evidence in favor of the null (Source) I see this claim repeated in multiple places, but I ...
bkoodaa's user avatar
  • 1,299
86 votes
2 answers
47k views

Bayes regression: how is it done in comparison to standard regression?

I got some questions about the Bayesian regression: Given a standard regression as $y = \beta_0 + \beta_1 x + \varepsilon$. If I want to change this into a Bayesian regression, do I need prior ...
TinglTanglBob's user avatar
34 votes
8 answers
4k views

Should I teach Bayesian or frequentist statistics first?

I am helping my boys, currently in high school, understanding statistics, and I am considering beginning with some simple examples without disregarding some glimpses to theory. My goal would be to ...
Giuseppe Biondi-Zoccai's user avatar
36 votes
5 answers
3k views

Wikipedia entry on likelihood seems ambiguous

I have a simple question regarding "conditional probability" and "Likelihood". (I have already surveyed this question here but to no avail.) It starts from the Wikipedia page on likelihood. They say ...
Creatron's user avatar
  • 1,665
30 votes
5 answers
33k views

Why is everything based on likelihoods even though likelihoods are so small?

Suppose I generate some random numbers from a specific normal distribution in R: set.seed(123) random_numbers <- rnorm(50, mean = 5, sd = 5) These numbers look ...
Uk rain troll's user avatar

15 30 50 per page
1
2 3 4 5
7