Tagged Questions

1
vote
2answers
101 views

Computing the likelihood gradient on a simple directed graphical model with hidden unit

SHORT VERSION: We have a ('visible') random variable $X$ and a ('hidden') random variable $Z$. We have chosen appropriate distributions $P(X|Z)$ and $P(Z;w)$ where $w$ is the parameter of the model. ...
2
votes
0answers
99 views

How to choose the distribution and parameters for continuous probability density functions in naive Bayes using maximum likelihood?

Let's assume I want to train a binary naive Bayes classifier, with classes $y_0, y_1$ and $n$-dimensional data. For this one needs to calculate the conditional probabilities $P(x_i | y_j) $ for all ...
4
votes
1answer
114 views

What are speed differences beetwen ML implementations in different languages?

I am trying to write my own ML library. For speed reasons I started out writing things in C using BLAS, but then I learned that NumPy and Theano also use BLAS. I am wondering if there are huge speed ...
4
votes
2answers
164 views

List of likelihood-based classification techniques

This is a basic statistical pattern recognition question. I'm aware of LDA classification, Naive Bayes Classification techniques which give output as a likelihood (of data belonging to a certain ...
2
votes
1answer
51 views

Adjusting existing algorithm - likelihood for presence-only data

Logistic regression fits a model that predicts a binary variable whilst performing a logit transformation of the linear combination (LC) of predictors: 1/1 + exp(-LC). I have a working machine ...
2
votes
1answer
224 views

Maximum Likelihood Estimation question - minimum log likelihood

I know the formula for the likelihood of some parameters given the data. The result has to be maximised and I can avoid multiplication using the log. How can I make this a minimisation problem (i.e. ...
2
votes
0answers
68 views

Paired multiarm bandit

I have a set of independent experiments with different distributions and I'm trying to determine which has the highest mean payoff. I would like to treat this as a multi-arm bandit problem, but the ...
0
votes
0answers
662 views

How to convert log likelihoods into scores in Naive Bayes?

I am currently implementing a text classification program with Naive Bayes. I produce two multinominal models in my training function: p(w|nonSPAM) and p(w|SPAM)) as well as a prior probability P(S). ...
7
votes
5answers
765 views

What is the 'fundamental' idea of machine learning for estimating parameters?

The 'fundamental' idea of statistics for estimating parameters is maximum likelihood. I am wondering what is the corresponding idea in machine learning. Qn 1. Would it be fair to say that the ...