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Questions tagged [bayesian-statistics]

28 questions
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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 human body pose estimation. Can someone explain…
Amy
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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 thing to read about a subject second hand and…
Joe Lee-Doktor
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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 "Cogent Confabulation". Briefly it is an inversion…
James Bowery
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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 a class of computational methods rooted in…
Remi.b
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4
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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 Bayesian classifier for periods ending sentences vs.…
Tommy McGuire
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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 (Learnable Evolution Models) are evolutionary models where…
CarrKnight
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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 acyclic graph (DAG) that encodes conditional…
jonem
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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…
Alan Wolfe
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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 0.9 while that of being rainy - 0.1. The transition…
Mihir Thatte
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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: $p(a,b,d,e,l,s,t,x) = p(a)p(t|a)p(e|t,l)p(x|e)p(l|s)p(b|s)p(d|b,e)p(s)$ Suppose I want to find $p(e|s)$, this means…
beginner
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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 records. Only values that are missing are values A. How…
Tesla
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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 facing in my program. Please do point me in the…
MetaStack
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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 of them), part of the set is the training set with…
user43481
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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 find minimum number of variable that Xi independent…
user3661613
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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 given a Gaussian likelihood. I did quite a bit of…
LKSR
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