Questions tagged [maximum-likelihood]

For questions related to maximum likelihood estimation (MLE), which is a frequentist approach for estimating the parameters of an assumed probability distribution given some observed data. This is done by maximizing a likelihood function so that, under the assumed statistical model, the observed data is most probable. The resulting estimate is known as the maximum likelihood estimate.

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Optimize parametric Log-Likelihood with a Decision Tree

Suppose there are some objects with features, and the target is parametric density estimation. Density estimation is model-based. Parameters are obtained by maximizing log-likelihood. $LL = \sum_{i \...
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22 views

Self sufficient material(s) on maximum likelihood estimation

While studying techniques related to word embeddings, I came across an objective function named maximum likelihood. Word embeddings can be estimated using maximum likelihood as an objective function. ...
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25 views

Why can't recurrent neural network handle large corpus for obtaining embeddings?

In order to learn the embeddings, we need to train a model based on some objective function. The model can be an RNN and the objective function can be the likelihood. We learn the embeddings by ...
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1answer
57 views

Does the Bayesian MAP give a probability distribution over unseen t*?

I'm working my way through the Bayesian world. So far, I've understood that the MLE or the MPA are point estimates, therefore using such models just output one specific value and not a distribution. ...
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1answer
205 views

How can a probability density value be used for the likelihood calculation?

Consider our parametric model $p_\theta$ for an underlying probabilistic distribution $p_{data}$. Now, the likelihood of an observation $x$ is generally defined as $L(\theta|x) = p_{\theta}(x)$. The ...
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1answer
66 views

What is emperical distribution in MLE?

I was reading the book Deep Learning by Ian Goodfellow. I had a doubt in the Maximum likelihood estimation section (Pg 131). I understand till the Eq 5.58 which describes what is being maximized in ...
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47 views

Estimating $\sigma_i$ according to maximum likelihood method

Let be a Bayesian multivariate normal distribution classifier with distinct covariance matrices for each class and isotropic, i.e. with equal values over the entire diagonal and zero otherwise, $\...
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44 views

Is maximum likelihood estimation meaningless for a dataset of only outliers?

From my understanding, maximum likelihood estimation chooses the set of parameters for the estimator that maximizes likelihood with the ground truth distribution. I always interpreted it as the ...
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71 views

Can the cross-entropy loss be used for a NLP task with LSTM?

I am trying to build an LSTM model to generate Shakspeare-like poems. I have training set $\{s_1,s_2, \dots,s_m\}$, which are sentences of Shakespeare poems, and each sentence contains words $\{w_1,...
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2answers
108 views

Can maximum likelihood be used as a classifier?

I am confused in understanding the maximum likelihood as a classifier. I know what is Bayesian network and I know that ML is used for estimating the parameters of models. Also, I read that there are ...
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1answer
159 views

What is the relationship between MLE and naive Bayes?

I have found various references describing Naive Bayes and they all demonstrated that it used MLE for the calculation. However, this is my understanding: $P(y=c|x)$ $\propto$ $P(x|y=c)P(y=c)$ with $...
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1answer
95 views

Understanding the math behind using maximum likelihood for linear regression

I understand both terms, linear regression and maximum likelihood, but, when it comes to the math, I am totally lost. So I am reading this article The Principle of Maximum Likelihood (by Suriyadeepan ...