Questions tagged [bayesian-statistics]
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27
questions
0
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1answer
31 views
SMO, Random forest and Bayes net algorithms: why does Random forest perform better?
I analyzed a dataset using those 3 different algorithms.
As I can see, Random forest performs better in most cases.
My dataset is composed of 4000 instances of two classes (class A 2000 elements, ...
0
votes
1answer
36 views
What is the time complexity of the SGVB estimator?
In the context of variational autoencoders, we want to maximize the evidence lower bound and this is typically done using Stochastic Gradient Variational Bayes (SGVB). I was curious if there is any ...
1
vote
1answer
52 views
Density of uniform distribution over two disjoint squares
A probability distribution $P$ over $X \times \{0, 1\}$. $P$ can be defined in term of its marginal distribution over $X$ , which we will denote by $P_X$ and the conditional labeling distribution, ...
0
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1answer
39 views
Gaussian distribution with condition?
What does this expression mean?
Normal distribution with condition
I am reading a research paper and found the following expression (Eq.28 in the paper below).
It means a Gaussian distribution, but ...
0
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0answers
65 views
how many parameters do we need to estimate for a general probabilistic model
Its a question from a test in machine learning.
I have 3 binary variables x1,x2 and x3 (which means that each one of them can be either 1 or 0), each one of them has a binary output y (can be 0 or 1).
...
1
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2answers
86 views
Bayes theorem probaility doesn't make sense
I try to use Bayes Theorem to calculate the probability of $P(A|B)$. I have $P(A)$ in column1, $P(B|A)$ in colmn2, $P(B)$ in column 3. I get the following:
my calculations were:
$$P(B/A) = 0.8\times ...
6
votes
1answer
117 views
Solomonoff's theory of induction, Kolmogorov complexity and Bayesian Inference
My motivations for asking this question are philosophical in nature. I'm by no means a computer scientist though, and I feel as though this question should be answered by someone who is since it's one ...
1
vote
1answer
81 views
Bayesian updating for multivariate Gaussian
I am reading http://www.yisongyue.com/courses/cs159/lectures/LinUCB.pdf and come across this slide
What has been confusing me boils down to showing that multivariate Gaussian is conjugate to itself ...
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0answers
459 views
How to choose value of additive smoothing in naive Bayes and why a higher value gives bad accuracy?
In Naive Bayes we often do additive smoothing as a fail safe. Consider the following expression:
Lets say
$$P(X_i) = \frac{count(X_i) + \alpha}{\sum_i^n count(X_i) + \alpha*total\_size}$$
How to tune ...
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 ...
1
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0answers
33 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 ...
13
votes
1answer
12k 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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0answers
48 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
votes
1answer
319 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
votes
1answer
92 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
166 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:
$p(a,b,d,e,l,s,t,x) = p(a)p(t|a)p(e|t,l)p(x|e)p(l|s)p(b|s)p(d|b,e)p(s)$...
5
votes
3answers
350 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 "...
1
vote
1answer
44 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
votes
1answer
383 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
votes
1answer
37 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 ...
4
votes
1answer
61 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 ...
1
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0answers
52 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 ...
3
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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 ...
4
votes
1answer
777 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) constitutes ...
0
votes
1answer
43 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)?
2
votes
1answer
315 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
45 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 ...
