Questions tagged [bayesian-statistics]

For questions related to Bayesian statistics in the context of artificial intelligence. Bayesian statistics is a system for describing uncertainty using the mathematical language of probability. Bayesian statistical methods start with existing "prior" beliefs, and update these using data to give "posterior" beliefs.

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DDPM - why after adding gaussian noise to image, we assume that new image is from normal distribution?

I have a question about forward process in DDPM. It is described as we sample our image from some distribution: $x_0\sim{q(x)}$ then in each time stamp $T$ we are applying gaussian noise $\epsilon\sim\...
cvzx's user avatar
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Optimizing Stop Loss Percentage for a Specific Model Based on Stock Price to Maximize Expected Value

I'm fine-tuning a specific trading model, and a crucial parameter I'm keen on optimizing is the stop loss percentage. The primary objective is to maximize the Expected Value (EV), formulated as: $$EV =...
David's user avatar
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Is there an entry level textbook on Bayesian Inference that is a nice blend of theory and applications?

I am looking for a textbook that is a nice entry level to Bayesian Inference. I was hoping that there is a nice blend of theory and applications (data sets) on how concepts are applied. Programming ...
cgo's user avatar
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Thompson sampling with Bernoulli prior and non-binary reward update

I am solving a problem for which I have to select the best possible servers (level 1) to hit for a given data. These servers (level 1) in turn hit some other servers (level 2) to complete the request. ...
PUNEET AGARWAL's user avatar
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What does the notation $\mathcal{N}(z; \mu, \sigma)$ stand for in statistics?

I know that the notation $\mathcal{N}(\mu, \sigma)$ stands for a normal distribution. But I'm reading the book "An Introduction to Variational Autoencoders" and in it, there is this notation:...
Peyman's user avatar
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Will adding memory to a supervised learning system makes it into a Bayesian learning system?

Seung recently published GameGAN paper, GameGAN learned and stored the whole Pacman game and was able to reproduce it without a game engine. The uniqueness of GameGAN is that it had added memory ...
Eka's user avatar
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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
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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 ...
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