Artificial Intelligence Stack Exchange
Artificial Intelligence Stack Exchange

Questions tagged [statistical-ai]

For questions about the applications/clarifications/intuitions/proofs behind the use of statistical methods in AI/ML programs.

Two main statistical methods are used in data analysis: descriptive statistics, which summarize data from a sample using indexes such as the mean or standard deviation, and inferential statistics, which draw conclusions from data that are subject to random variation (e.g., observational errors, sampling variation).

Statistical Methods - Wikipedia

Statistics - Wikipedia

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How does noise affect generalization?

Does increasing the noise in data help to improve the learning ability of a network? Does it make any difference or does it depend on the problem being solved? How is it affect the generalization process overall?
kenorb
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Why exactly do neural networks require i.i.d. data?

In reinforcement learning, successive states (actions and rewards) can be correlated. An experience replay buffer was used, in the DQN architecture, to avoid training the neural network (NN), which represents the $Q$ function, with correlated (or…
nbro
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Is Nassim Taleb right about AI not being able to accurately predict certain types of distributions?

So Taleb has two heuristics to generally describe data distributions. One is Mediocristan, which basically means things that are on a Gaussian distribution such as height and/or weight of people. The other is called Extremistan, which describes a…
Josiah Swaim
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What are some examples of Statistical AI applications?

I believe that statistical AI uses inductive thought processes. For example, deducing a trend from a pattern, after training. What are some examples of successfully applied Statistical AI to real-world problems?
WilliamKF
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What are the differences in scope between statistical AI and classical AI?

What are the differences in scope between statistical AI and classical AI? Real-world examples would be appreciated.
dua fatima
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Are neural networks statistical models?

By reading the abstract of Neural Networks and Statistical Models paper it would seem that ANNs are statistical models. In contrast Machine Learning is not just glorified Statistics. I am looking for a more concise/summarized answer with focus on…
Leo Gallucci
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What is Statistical relational learning?

I have gone through the wikipedia explanation of SRL. But, it only confused me more: Statistical relational learning (SRL) is a subdiscipline of artificial intelligence and machine learning that is concerned with domain models that exhibit both…
Dawny33
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How to figure out which words have the same meaning in two different languages?

Imagine two languages that have only these words: Man = 1, deer = 2, eat = 3, grass = 4 And you would form all sentences possible from these words: Man eats deer. Deer eats grass. Man eats. Deer eats. German: Mensch = 5, Gras = 6, isst = 7,…
Tone Škoda
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Is traditional machine learning obsolete given that neural networks typically outperform them?

I have been coming across visualizations showing that the neural nets tend to perform better as compared to the traditional machine learning algorithms (Linear regression, Log regression, etc.) Assuming that we have sufficient data to train…
Prometheus
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What's going on in the equation of the variational lower bound?

I don't really understand what this equation is saying or what the purpose of the ELBO is. How does it help us find the true posterior distribution?
Gooby
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Is there any measure of separability of classes?

I want to know if there is a measure of how well two classes in Y are separable (linearly or not) based on their features in X. Easiest way of explaining this is to compare it to correlation coefficients, the higher the correlation the higher…
GKozinski
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What makes a machine learning algorithm a low variance one or a high variance one?

Some examples of low-variance machine learning algorithms include linear regression, linear discriminant analysis, and logistic regression. Examples of high-variance machine learning algorithms include decision trees, k-nearest neighbors, and…
Posi2
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Reinforcement learning objective as conditional expectations

In one of his lectures Levine describes the objective of reinforcement learning as: $$J(\tau) = E_{\tau\sim p_\theta(\tau)}[r(\tau)]$$ where $\tau$ refers to a single trajectory and $p_\theta(\tau)$ is the probability of having taken that trajectory…
aprospero
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Finding the right questions to increase accuracy in classification

Lets say I have a list of 100k medical cases from my hospital, each row = patient with symptoms (such as fever , funny smell, pain etc.. ) and my labels are medical conditions such as Head trauma, cancer , etc.. The patient come and say "I have…
Latent
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Is there a way of computing a prominence score based on the prevalence of features in an image?

Is there any previous work on computing some sort of prominence score based on the prevalence of features in an image? For example, let's say I am classifying images based on whether or not they have dogs in them. Is there a way to compute how…
hisairnessag3
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