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Questions tagged [bayesian-networks]

For questions related to Bayesian networks, which are e.g. used to study causality (or causation) in AI.

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Which is the probability of JohnCalls given Burglary? Why?

Questions: P (JohnCalls|Burglary) ? Why? Source of the image: Artificial Intelligence: A Modern Approach - Third Edition, by Stuart Russell and Peter Norvig. What do you know about Bayesian networks?...
BsAxUbx5KoQDEpCAqSffwGy554PSah's user avatar
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What model is good for learning both within and across categories?

How can I incorporate both general trends and subcategory-specific trends into a model? Let's say I am predicting factors that affect import volume, for example. There are many industries which have ...
BigMistake's user avatar
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Requesting resources on causal networks for 2D strategy game

I am requesting research, articles, abstracts or interesting opinions that will help me create a complex causal neural network. There are many detailed resources on causal discovery, image recognition,...
Mitsuformation's user avatar
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1 answer

What is the difference between an input and observed data in a Bayesian neural network?

I'm new to the Bayesian perspective and would appreciate clarity on this. In a few resources concerning Bayesian deep learning (such as this one), I see this notation: $p(y|x, D) = \int p(y|x, \theta)...
Seo's user avatar
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Calculation of the CPTs in a Dynamic Bayesian Network

I am trying to figure out how to build my DBN (with pyAgrum), and I am a bit confused. Let us say I have the network on the next figure. I am interested in the variable $B$. At each time $t$, I am ...
Dark Patate's user avatar
1 vote
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variational inference but with a weighted loglikelihood

I would like to know if it's correct if I substitute in the ELBO formula a weighted sum of the loglikelihood $$\sum E_{q_{\theta}(w)}[w_i \ln{p(y_i|f^{w}(x_i))}]$$ in place of the traditional sum. ...
Alucard's user avatar
  • 111
-1 votes
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Given A and B, C are independent of each other. Given A, B and C, D and E are independent of each other. What is the minimal number of parameters?

Assuming all variables $A, B, C, D,$ and $E$ are random binary variables. I come up with Bayes net: $D \rightarrow B \rightarrow A \leftarrow C \leftarrow E$ which has the minimal number of parameters ...
BOB's user avatar
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1 answer

Why are Directed Graphical Models considered ML methods?

Consider the following problem. The probability of being born in countries [1,2,3,4] is given by [a, b, c, d] respectively. This is a categorical problem. Now, assume that the height of a person ...
user1029384756's user avatar
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What makes Sequential Bayesian Filtering and Smoothing tractable?

I'm currently diving into the Bayesian world and I find it pretty fascinating. I've so far understood that applying the Bayes' Rule, i.e. $$\text{posterior} = \frac{\text{likelihood}\times \text{prior}...
igorTh2's user avatar
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1 answer

What does all the formula and pictures mean? I am a medical school graduate and I really want to learn AI/ML for computer-aided diagnosis. I was building a symptom checker and I found the ...
pullidea-dev's user avatar
1 vote
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In the original GAN paper, why is it mentioned that you can sample deep directed graphical models without a Markov chain?

In the original GAN paper (table 2), why is it mentioned that you can sample deep directed graphical models without a Markov chain (well, they say without difficulties, but others list MCMC as a ...
beelze-b's user avatar
2 votes
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What are the practical problems where full bayesian treatment is affordable?

Suppose, I have a problem, where there is rather a small number of training samples, and transfer learning from ImageNet or some huge NLP dataset is not relevant for this task. Due to the small number ...
spiridon_the_sun_rotator's user avatar
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1 answer

What Constitutes Messages in Junction Tree Algorithm?

I'm currently studying the Junction Tree Algorithm: I'm referring to the process of transforming a Bayesian Network into a Junction Tree in order to apply inference. I understand how you build the ...
Genoma's user avatar
  • 25
2 votes
1 answer

In Probabilistic Graphical Model (written by Daphne Koller), what's the meaning of "parameter" in representation of the distribution?

I just started to read the PGM book written by Daphne Koller. In the chapter of Bayesian Network Representation(Chapter 3), there are some descriptions about the standard parameterization of the joint ...
Hongmin Yang's user avatar
3 votes
1 answer

How are the "Link Strength true", "Link Strength blind" and "Mutual Information" calculated in this report on Bayesian networks?

I'm trying to understand how to calculate the strength of every arc in a Bayesian Network. I came across this report Measuring Connection Strengths and Link Strengths in Discrete Bayesian Networks, ...
qillbel's user avatar
  • 31
2 votes
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Deriving hyperparameter updates in Online Interactive Collaborative Filtering

I've been going through "Online Interactive Collaborative Filtering Using Multi-Armed Bandit with Dependent Arms" by Wang et al. and am unable to understand how the update equations for the ...
Shashank Gupta's user avatar
6 votes
2 answers

What is the difference between a Bayesian Network and a Markov Chain?

I am trying to understand the difference between a Bayesian Network and a Markov Chain. When I search for this one the web, the unanimous solution seems to be that a Bayesian Network is directional (...
Newskooler's user avatar
4 votes
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How can I draw a Bayesian network for this problem with birds?

I am working on the following problem to gain an understanding of Bayesian networks and I need help drawing it: Birds frequently appear in the tree outside of your window in the morning and ...
Jake's user avatar
  • 41
3 votes
1 answer

Why do Bayesian algorithms work well with small datasets?

I read very often that Bayesian algorithms work well on small datasets. Why is that? I think it is because they might generalize more, but why is that? See also Investigating the use of Bayesian ...
jennifer ruurs's user avatar
3 votes
1 answer

How do I determine the most appropriate classifier for a certain problem?

Consider a Bayesian classifier used in spam e-mail filtering. It converts an e-mail to a vector, most of the time using the bag-of-words method. Although it learns first before getting employed, it ...
user avatar
1 vote
2 answers

Can maximum likelihood be used as a classifier?

I am confused in understanding the maximum likelihood as a classifier. I know what is Bayesian network and I know that ML is used for estimating the parameters of models. Also, I read that there are ...
Atena's user avatar
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1 vote
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What is the point of converting conditional probability to factor for Variable Elimination?

I have this slide from my AI class on using a Bayes network to compute a conditional probability. I don't really understand the point of converting the conditional probabilities to factors (besides ...
Gooby's user avatar
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1 vote
3 answers

Is it possible to compute $P( F \mid S )$ given $P(F \mid S,A)$ and $P(F \mid S, \lnot A)$ in Bayesian network?

I have a bayesian network, which has the following data: $P(S) = 0.07$ $P(A) = 0.01$ $P(F \mid S,A) = 1.0$ $P(F \mid S, \lnot A) = 0.7$ $P(F \mid \lnot S, A) = 0.9$ $P(F \mid \lnot S, \lnot A) =...
Diego C. 's user avatar
1 vote
1 answer

Problem with Proposition 1 of Google Deepmind's 'Weight uncertainty in Neural Networks'

I'm going through the paper Weight Uncertainty in Neural Networks by Google Deepmind. In the final line of the proof of proposition 1, the integral and the derivative are swapped. Then the derivative ...
TimvanSch's user avatar
8 votes
1 answer

What are the main benefits of using Bayesian networks?

I have some trouble understanding the benefits of Bayesian networks. Am I correct that the key benefit of the network is that one does not need to use the chain rule of probability in order to ...
Sebastian Dine's user avatar
1 vote
2 answers

Can we derive the distribution of a random variable based on a dependent random variable's distribution?

In the diagram below, there are three variables: X3 is a function of (depends on) X1 and X2, ...
user297850's user avatar
1 vote
1 answer

How to perform structure learning for Bayesian network given already partially constructed Bayesian network?

Let's assume that we have a dataset of variables (random events)I apriori would like to set dependency conditions between some of them and perform structure learning to figure out the rest of the ...
Sultan1991's user avatar
2 votes
3 answers

What is the definition of a heuristic function in the BayesChess paper?

I am reading BayesChess: A computer chess program based on Bayesian networks (Fernandez, Salmeron; 2008) It is a chess-playing engine using Bayesian networks. The following is mentioned about the ...
satya's user avatar
  • 187
1 vote
1 answer

What does a hybrid Bayesian network contain?

The field of artificial intelligence is so vast. There are many methodologies for handling continuous data, and I have just read about the hybrid Bayesian network. I just want to know that what a ...
saiqa jabeen's user avatar
2 votes
1 answer

Why is exact inference in a Bayesian network both NP-hard and P-hard?

I should show that exact inference in a Bayesian network (BN) is NP-hard and P-hard by using a 3-SAT problem. So, I did formulate a 3-SAT problem by defining 3-CNF: $$(x_1 \lor x_2) \land (\neg x_3 \...
xava's user avatar
  • 423
5 votes
2 answers

Are Bayesian networks important to learn in 2018?

I study AI by myself with the book "Artificial Intelligence: A Modern Approach". I've just finished the chapters about the Bayesian network and probabilities, and I found them very interesting. Now, I ...
kaizokun's user avatar
  • 173