Questions tagged [statistical-ai]

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

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2
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0answers
13 views

Mapping given probabilities to empirical probabilities

Consider following problem statement: You have given $n$ actions. You can perform any of them. Each action gives you success with some probability. The challenge is to perform given finite number of ...
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21 views

Trying to proof off policy TD Learning formula

I was reading the book "Introduction to Reinforcement Learning" by Richard Sutton In section 7.3 he write the formula for n-step off-policy TD as:. $$V(S_t) = V(S_{t-1}) + \alpha \rho_{t:t+n-...
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19 views

Methods in training models to minimize distance between statistical summaries of data and samples from model, to get a better approximation function

Introduction: A big problem with deep learning methods involving neural networks is that they tend to do really poorly outside the boundaries of the approximation it has learned from the data it is ...
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44 views

How to calculate the data noise variance for a prediction interval?

I have a neural network that connects $N$ input variables to $M$ output variables (qoi). By default, neural networks just give out point estimations. Now, I want to plot some of the quantity of ...
3
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61 views

Simplification of expected reward under the limit in continuous tasks

I was reading the average reward setting for continuous tasks from rich sutton's book (page 202, 2nd edition). There he perform a simplification over the expected reward under the limit approaching to ...
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26 views

How can I find the correlation between the input and output of a neural network?

I'm trying to get a value for a correlation between a function input and its output. One brute force way to get this is to sample the entire space and find the standard deviation of the resulting ...
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0answers
25 views

Do the variance and bias belong to the policy or value functions?

Recently, I read many papers on variance and bias. But I am still confused by the two notions, the variance or bias belongs to who? Policy or value? If the variance or bias is large or low, what ...
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1answer
58 views

Is there any way to apply linear transformations on a vector other than matrix multiplication?

I am trying to optimize the cost function calculation in regression analysis using a non-matrix multiplication based approach. More specifically, I have a point $x = (1, 1, 2, 3)$, to which I want to ...
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40 views

Is maximum likelihood estimation meaningless for a dataset of only outliers?

From my understanding, maximum likelihood estimation chooses the set of parameters for the estimator that maximizes likelihood with the ground truth distribution. I always interpreted it as the ...
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0answers
27 views

What is the difference between an generalised estimating equation and a recurrent neural network?

What is the difference between a generalised estimating equation (GEE) model and a recurrent neural network (RNN) model, in terms of what these two models are doing? Apart from the differences in the ...
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1answer
59 views

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.) ...
2
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1answer
94 views

How can supervised learning be viewed as a conditional probability of the labels given the inputs?

In the literature and textbooks, one often sees supervised learning expressed as a conditional probability, e.g., $$\rho(\vec{y}|\vec{x},\vec{\theta})$$ where $\vec{\theta}$ denotes a learned set of ...
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21 views

Keras correlation coefficient as network metric in R

does anyone know how to use the correlation coefficient or squared correlation coefficient as a metric in keras in R (although other languages may provide clues). This is for a CNN that functions ...
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2answers
182 views

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?
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1answer
47 views

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 ...
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1answer
42 views

How to calculate the false positives and negatives?

I have a huge amount of data and I want to calculate my false positive and false negative. Is there a software that can help me determine it?
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78 views

How to build naive bayes graph from data

For an university assignment I have to use the HuginLite software to do some probabilistic inferences with different algorithms. One of these algorithms is Naive Bayes but its graph is not built ...
2
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1answer
26 views

When a linear discriminant could give excellent or possibly even the optimal classification accurcy?

I am actually reading the linear classification. There is a question in the question set behind the chapter in the book as follows: Sketch two multimodal distributions for which a linear discriminant ...
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1answer
53 views

Auto-regression - Reduce error in prediction

I am trying to develop a time series model using autoregression. The data set is like as follows ...
7
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2answers
2k views

Why exactly do neural networks require i.i.d. data?

In reinforcement learning, in general, successive states (actions and rewards) are highly correlated. An "experience replay" buffer was used, in the DQN architecture, to avoid training the neural ...
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2answers
59 views

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, ...
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2answers
2k views

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 ...
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2answers
181 views

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 ...
2
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1answer
205 views

Does the correlation between inputs affect the model performance?

I'm currently working on a regression problem and I have 10 inputs/attributes. What should I do if there are correlations between different features of the input data? Does the correlation between ...
4
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1answer
113 views

Methodology bias in AI textbooks [closed]

Let me compare two textbooks: (1) "Artificial Intelligence: A Modern Approach" by Stuart J. Russell and Peter Norvig and (2) "Artificial Intelligence: Structures and Strategies for Complex Problem ...
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1answer
450 views

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.
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2answers
143 views

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 $p_\theta(\tau)$ ...
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1answer
2k views

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 ...
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1answer
489 views

Why do we need Upsampling and Downsampling in Progressive Growing of Gans

I was working recently on Progressive Growing of GANs (aka PGGANs). I have implemented the whole architecture, but the problem that was ticking my mind is that in simple GANs, like DCGAN, PIX2PIX, we ...
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0answers
81 views

Confidence interval around a DNN prediction

I am facing a problem and do not know whether it is even solvable: I want to predict the behaviour of a system using a DNN, say a CNN, in the sense that I want to predict the time and intensity of a ...
3
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2answers
90 views

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, ...
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1answer
393 views

Are standard deviation, variance, skew good features for ML?

Pretty simple question here: Is it useful to use the standard deviation, skew, kurtosis, or any other extrapolatory stats as features, and if so in which problem sets? In this case, I am talking ...
3
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1answer
84 views

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 ...
4
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4answers
152 views

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: ...
3
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1answer
143 views

With gradient descent w/MSE on a regression, must/should every Epoch use the exact same training samples?

Let's say I've got a training sample set of 1 million records, which I pull batches of 100 from to train a basic regression model using gradient descent and MSE as a loss function. Assume test and ...
5
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1answer
194 views

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 ...
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4answers
1k views

What are some examples of statistical AI applied to real-world problems?

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-...
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3answers
661 views

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 ...