Questions tagged [bayesian-statistics]
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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 ...
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1answer
59 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 ...
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304 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 ...
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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 ...
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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 ...
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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 ...
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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 ...
2
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1answer
259 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....
3
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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 (...
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0answers
107 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:
$p(a,b,d,e,l,s,t,x) = p(a)p(t|a)p(e|t,l)p(x|e)p(l|s)p(b|s)p(d|b,e)p(s)$...
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3answers
315 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 "...
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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, ...
2
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1answer
340 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 ...
2
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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 ...
5
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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 ...
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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 ...
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1answer
994 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
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1answer
355 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) ...
0
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1answer
32 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)?
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1answer
248 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 ...
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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 ...
