For questions related to conditional probability e.g. in the context of Bayesian inference or networks.
Questions tagged [conditional-probability]
14 questions
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What is "conditioning" on a feature?
On page 98 of Jet Substructure at the Large Hadron Collider: A Review of Recent Advances in Theory and Machine Learning the author writes;
Redacted phase space: Studying the distribution of inputs and the
network performance after conditioning on…
Clumsy cat
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Why isn't conditional probability sufficient to describe causality?
I read these comments from Judea Pearl saying we don't have causality, physical equations are symmetric, etc. But the conditional probability is clearly not symmetric and captures directed relationships.
How would Pearl respond to someone saying…
user3180
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How to update the observation probabilities in a POMDP?
How can I update the observation probability for a POMDP (or HMM), in order to have a more accurate prediction model?
The POMDP relies on observation probabilities that match an observation to a state. This poses an issue as the probabilities are…
Pluxyy
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Doesn't every single machine learning classifier use conditional probability/Bayes in its underlying assumptions?
I'm reading about how Conditional Probability/ Bayes Theorem is used in Naive Bayes in Intro to Statistical Learning, but it seems like it isn't that "groundbreaking" as it is described?
If I'm not mistaken doesn't every single ML classifier use…
Katsu
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How the proof of the contraction of variance for distributional Bellman operator follows
I am stuck at the proof of the contraction of variance for distributional Bellman operator from the paper, in which it is defined as
and the proof is stated as
In its second part, how is the variance of the target distribution equal to the…
Magi Feeney
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How is per-decision importance sampling derived in Sutton & Barto's book?
In per-decison importance sampling given in Sutton & Barto's book:
Eq 5.12 $\rho_{t:T-1}R_{t+k} =…
ZERO NULLS
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What to do when PDFs are not Gaussian/Normal in Naive Bayes Classifier
While analyzing the data for a given problem set, I came across a few distributions which are not Gaussian in nature. They are not even uniform or Gamma distributions(so that I can write a function, plug the parameters and calculate the "Likelihood…
Soumee
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What is x, y, p(x), p(y) in generative model domain?
Background
Generative modeling
Generative modeling aims to model the probability of observing an observation x.
$$
p(x) = \frac{p(y\cap x)}{p(y|x)}
$$
Representation Learning
Instead of trying to model the high-dimensional sample space directly, we…
Prakhar
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I am confused of derivation steps of MAP for linear regression
I am taking ML course and I am confused about some derivations of math
Could you explain the two steps I marked on the slides? For the first step, I thought $P(beta|X,y) = \frac{P(X,y|beta)P(beta)}{P(X,y)}$ but I don't know the further steps to…
tesio
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Is it possible for PixelCNN to tell us what it generates?
I coded PixelCNN with the help of Keras official website. Also, I read the paper. I can use PixelCNN, similar to a decoder or generator (to generate samples). My question is, "is it possible to train PixelCNN to tell us what is predicted?".
For…
Pouyan
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How do I sample conditionally from deep belief networks?
Deep belief networks (DBNs) are generative models, where, usually, you sample by thermalising the deepest layer (as it's a restricted Boltzmann machine), and then forward propagating a sample towards the visible layer to get a sample from the…
Abelaer
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Solving the supervised learning problem of learning $p(y \vert \mathbf{x})$ by using traditional unsupervised technologies to learn $p(\mathbf{x}, y)$
I am currently studying Deep Learning by Goodfellow, Bengio, and Courville. In chapter 5.1.2 The Performance Measure, $P$, the authors say the following:
Unsupervised learning and supervised learning are not formally defined terms. The lines…
The Pointer
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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 the fact that it looks weird to marginalize or…
Gooby
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Is there any difference between 'input' and 'conditional input' in the case of neural networks?
In the research paper titled Conditional Generative Adversarial Nets by Mehdi Mirza and Simon Osindero, there is a notion of conditioning a neural network on a class label.
It is mentioned in the abstract that we need to simply feed extra input $y$…
hanugm
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