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Questions tagged [bayesian-statistics]

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9
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1answer
5k views

What is meant by the term “prior” in machine learning

I am new to machine learning. I have read several papers where they have employed deep learning for various applications and have used the term "prior" in most of the model design cases, say prior in ...
5
votes
1answer
72 views

Solomonoff's theory of induction, Kolmogorov complexity and Bayesian Inference

My motivations for asking this question are philosophical in nature. I'm by no means a computer scientist though, and I feel as though this question should be answered by someone who is since it's one ...
5
votes
3answers
317 views

Are the Confabulation Theories of Thaler and Hecht-Nielsen Isomorphic?

Both S. L. Thaler and R. Hecht-Nielsen have set forth neural-based theories of "confabulation" applicable to machine learning. The essential mathematics of Hecht-Nielsen is set forth in his paper "...
4
votes
1answer
53 views

Simple Bayesian classification with Laplace smoothing question

I'm having a hard time getting my head around smoothing, so I've got a very simple question about Laplace/Add-one smoothing based on a toy problem I've been working with. The problem is a simple ...
3
votes
1answer
1k views

Difference between Bayesian Networks and Dynamic Bayesian Networks

I'm studying Bayesian networks and want to clarify a couple of things with people who are more knowledgable in the area than me. As far as I understand it, a Bayesian network (BN) is a directed ...
3
votes
1answer
370 views

Approximate Bayesian Computation VS Monte Carlo Simulation

I am a little confused about the differences between Approximation Bayesian Computation (ABC) and Monte Carlo Methods (MCM). Citing from wikipedia: Approximate Bayesian computation (ABC) ...
3
votes
1answer
85 views

Is there any example of Regression Tree driven optimization (or active learning)?

Bayesian Optimization is the classic example of meta-model driven optimization where new observations are used to train a Gaussian process that provides a clue to where to optimize next. LEM (...
2
votes
1answer
34 views

How can a distributed system cooperate to determine rules of its environment?

I'm sorry if this question is silly or elementary. I'm not a computer scientist so I don't know the vocabulary to use to ask this question. Thus I've produced an analogy to explain the challenges I'm ...
2
votes
1answer
263 views

Convergence of Markov model

I was learning Hidden Markov model, and encountered this theory about convergence of Markov model. For example, consider a weather model, where on a first-day probability of weather being sunny was 0....
2
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0answers
37 views

Not grasping Bayesian Monte Carlo

I've read several sources of information that describe the process of Bayesian Monte Carlo Quadrature but am just not understanding the details enough to be able to implement it. For instance two ...
2
votes
1answer
345 views

Expectation Maximization Algorithm for simple naive Bayesian network

I am trying to understand the following network A has two children - B & C (aka common cause) All the variables are binary and can be either 0 or 1. In data values are missing only for some ...
1
vote
1answer
43 views

Can you use a Bayes classifier to determine if something is NOT in a defined class?

I know I can use a Bayes classifier to determine if something is one of N classes, but can I also determine if something is NOT in any of the predefined classes? Or will a Bayes classifier only find ...
1
vote
1answer
42 views

What ML methods exist to categorize signal from noise? Red noise? Spatially correlated noise?

Let's say we are given measurements of some sort. In many cases, it is safe to assume that noise is white noise, serially uncorrelated, and zero mean with some finite variance. But in other cases, ...
1
vote
1answer
249 views

Bayesian Nets & Markov Blanket

As i passed PHD entrance exam, some days ago, i want to find solutions for challenging problem. In Bayes network on X={X1,...Xn} each random variable has P parents and Q child's. for Xi we want to ...
1
vote
1answer
61 views

Bayesian updating for multivariate Gaussian

I am reading http://www.yisongyue.com/courses/cs159/lectures/LinUCB.pdf and come across this slide What has been confusing me boils down to showing that multivariate Gaussian is conjugate to itself ...
1
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0answers
312 views

How to choose value of additive smoothing in naive Bayes and why a higher value gives bad accuracy?

In Naive Bayes we often do additive smoothing as a fail safe. Consider the following expression: Lets say $$P(X_i) = \frac{count(X_i) + \alpha}{\sum_i^n count(X_i) + \alpha*total\_size}$$ How to tune ...
1
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0answers
29 views

Unsupervised learning: necessity of labels and dependency between features and labels?

I have logs of activities without labels, which describe whether an activity is normal or not. Assuming that normal behaviors will follow a Gaussian distribution, I fit Gaussian distributions on ...
1
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0answers
110 views

Variable elimination in Bayesian network

I'm trying to check if my understanding of variable elimination is correct. Assume the above Bayesian network is factorized as: $p(a,b,d,e,l,s,t,x) = p(a)p(t|a)p(e|t,l)p(x|e)p(l|s)p(b|s)p(d|b,e)p(s)$...
1
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0answers
50 views

Why naive Bayes performs better?

I have found that naive Bayesian classifier performs much better than classification using mixture of multivariate Gaussians. Here is the problem: I have a set of objects with attached features (10 ...
0
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1answer
33 views

Bayes nets - calculating probabilities

Given a Bayesian network, say a -> b -> c, all binary random variables (I won't show the CPTs, assume they are given). You are told b and c are true. How do you calculate the P(a=True)?
0
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0answers
40 views

Using counting to build a grid world

For this question, I have tried everything that I can think of, but cannot solve it. What I want to do is iterate over all possible values of $z_1$, but every method I use, it requires me to know ...