0
votes
1answer
19 views

Understand the English paragraph on association rule.

I am currently studying Association Rule Pattern Mining. I am reading the explanation on wikipedia about it. Somehow, I feel like I have a problem in understanding the paragraph below. Can somebody ...
0
votes
1answer
22 views

Is it a wrong expression for the local log-likelihood of logistic regression?

In page 206 of the book 'Elements of statistical learning', the author wrote: The local log-likelihood for this $J$ class model can be written $\sum_{i=1}^NK_\lambda (x_0, ...
0
votes
0answers
11 views

Is Expectation Propagation (EP) affected by the prior?

I understand EP by reading Minka's thesis: http://research.microsoft.com/en-us/um/people/minka/papers/ep/minka-ep-uai.pdf I'm trying to apply it to solve a Bayesian inference problem. However, I'm ...
2
votes
0answers
29 views

Normalizing multiple different features from unknown distributions

I'm doing some "exploratory" data analysis over a large set of classes/proteins, with a few hundred different features (I.E. Continuous variables) extracted from the data. The features are calculated ...
6
votes
1answer
98 views

What is the most general formalism for machine learning?

Most of the literature I can find in the field of machine learning is extremely practical, listing many techniques you can use like neural networks, SVMs, random forests, and so on. There are lots of ...
1
vote
0answers
25 views

Estimate distance between approximated posterior and true posterior

I'm working on a paper about using graphical models to do some prediction tasks with known observations. Since the model is complicated, finding the maximum a posteriori on the true posterior ...
0
votes
0answers
10 views

Loss specific inference in graphical models

As far as I have seen, in graphical models, the inference (for training or parameter estimation) is done via maximizing likelihood. While in many applications people need loss specific optimization of ...
0
votes
1answer
130 views

Gaussian with a linear combination random variable mean

A very simple (looks like...) statistical problem, however I don't even know how to name it in a formal way... Suppose in a Bayesian framework I have random variables $y, x_1,$ and $x_2$, $$f(x) = ...
0
votes
0answers
47 views

Integer programming with pairwise relaxations: optimality?

In David Sontag's thesis [1] (page 11, 3rd paragraph from the end), it is mentioned that "Most previous linear programming approaches to approximate inference optimize over the pairwise LP ...
1
vote
0answers
164 views

Classification: Why k-Nearest Neighbor method is more appropriate for a Mixture of Gaussians?

I'm reading a book named "The Elements of Statistical Learning" in which it states 2 scenarios when we are trying to predict the class label: Scenario 1: The training data in each class were ...