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### 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". ...
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### 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?
1 vote
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### 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 ...
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1 vote
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### 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 $\prod_{i=1}^{m}p(y|x;\theta)$...
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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 ...
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### What is maximum likelihood estimation in logistic regression? [duplicate]

Can you please explain in simple way. Is it so important in logistic regression?
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### 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 ...
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### 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 ...
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