0
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0answers
7 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: ...
5
votes
0answers
68 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
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0answers
22 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
97 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
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0answers
71 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
94 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
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1answer
34 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
41 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
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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
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1answer
224 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 ...