2k views

### Relation between MAP, EM and Max Likelihood

I am a beginner in ML. I can do programming fine but the theory confuses me a lot of the times. What is the relation between Max likelihood algorithm, Maximum A posteriori Method and Expectation ...
3k views

### Expectation-Maximization Algorithm for Binomial

I have a multinomial distribution with four outcomes, with a pdf: $$p(x_1,x_2,x_3,x_4)=\frac{n!}{x_1!x_2!x_3!x_4!}p_1^{x_1}p_2^{x_2}p_3^{x_3}p_4^{x_4}, \sum_{i=1}^4x_i=n, \sum_{i=1}^4p_i=1$$ The ...
1k views

### EM-algorithm and missing data

Does EM-algorithm only work with missing data? If not, what is the idea to assume that we have missing data?
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### Expectation-Maximization with a MLE package in R

As a follow up to one answer of the topic Expectation-Maximization with a coin toss: One of the user posted an R-code with MLE example almost a year ago (and his last online time here was 3 months ago,...
562 views

### baum-welch parameter estimation numeric example

I have an HMM (picture below), with a single parameter $\theta$ I want to estimate using Baum-Welch. I have a single training example X="HHT", and I start with an ...
153 views

### Real time example: Estimation for incomplete data

Following is from Csiszar and Shields' FnT monograph "Information Theory and Statistics": The expectation–maximization or EM algorithm is an iterative method frequently used in statistics to ...
63 views

### Are there any clustering algorithms that do not exclude/impute missing data?

From my understanding, clustering algorithms require complete data. Based on this, if there are missing values in my dataset I have two options: Impute missing information using some sort of ...
48 views

### Linear regression where some known records have a measurement error in dependent variable

I am modelling data where the dependent variable is the number of units of a certain product sold each month in each area. In all areas, the product is sold by a chain of shops 'A' and we have exact ...
42 views

### mixed noise and gaussian

I have a large number of data sets. Each data set has something 200K data points lying in a square times a circle. The square is solid $I\times I$. The circle $S^1$ is hollow (dim 1). By reasoning ...