-1
votes
0answers
17 views

What machine learning tool is best suited for taking time series and descriptive and making a binomial classification. [migrated]

I have an interesting task of utilizing log data from computer servers in a server farm and predicting if a particular server is likely to fail in the next 24 hours. My data set will be comprised of ...
0
votes
1answer
21 views

What is this distribution formulated with w, m and sum sign?

I have a binary classification problem, part of which is defined as follows : p(x|y=1) $\sim w (m_1 , \sum_1$) and p(x|y=0) $\sim w (m_0 , \sum_0$) Where $\sum_1$ is a covariance matrix : $$ ...
1
vote
0answers
36 views

Estimating conditional probability as a function of time

My question relates to estimating from a time series a time dependent conditional probability without having a prior parametric model of anything. Suppose I have two variables: r and I, and each can ...
0
votes
0answers
17 views

Computing Object Classification with bayesian statistics

Say I want to know if there is a zebra $\theta$, in an image $x$. According to Bayes statistics applies to image recognition, I should be computing: ...
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
1answer
128 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
86 views

Why do Bayesian Networks use acyclicity assumption?

I am trying to gain an intuition about how Bayesian Networks are built for a stochastic process. I see how the conditional independence assumptions in a Bayesian Network makes probability calculations ...
2
votes
0answers
101 views

Building Bayesian Networks, Causality and Cyclic Reasoning

I am studying Bayesian Statistics and I am trying to get a good understanding on Bayesian Networks, which seems to be vital in order to make something useful in Machine Learning. Most of the texts I ...
0
votes
1answer
36 views

Bayes Learning - MAP hypotesis

Suppose I have a set of hypotesys $H = \{h_1, h_2\}$ mutual exclusive. For them $P(h_1) = 0.2$ and $P(h_2) = 0.3$ (prior distribution). Suppose we know also that $$P(Y=0 | h_1) = 0.2$$ $$P(Y=0 | h_2) ...
0
votes
0answers
43 views

Trouble reading multinomial naive bayes notation

$C_m$: m = most likely class (wanted to write C subscript MAP for "maximum a posteriori" but couldn't do MAP with MathJax) ...
0
votes
0answers
24 views

Naive Bayesian Classifier for Object with Variable attributes

Let say our objects are connected graphs. They are to be classified into two categories, say A and B. However, for our purpose attributes for each graph is equal to the number of vertex of the graph ...
1
vote
1answer
252 views

Stuck with handling of conditional probability in Bishop's “Pattern Recognition and Machine Learning” (1.66)

I've just started working through the book, and I'm stuck with how the author handles conditional probability in (1.66). The context is as follows. In this chapter we are working with a curve ...