Questions tagged [terminology]
Usage and meaning of specific technical words/concepts in statistics.
2
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1answer
191 views
In statistics, is there a formal name to the two variables of a two-dimensional contingency table?
Is there a formal terminology for the variable in the columns and for the variable in the rows?
0
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2answers
32 views
What are the predictor variables in a neural network?
In a linear regression model, the predictor or independent (random) variables or regressors are often denoted by $X$. The related Wikipedia article does, IMHO, a good job at introducing linear ...
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0answers
10 views
structural break vs regime shift in simple language
I went through the wiki pages for Structural Break and Regime Shift. It seems to me that they are synonyms.
Is there a difference ?
0
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0answers
13 views
What does it mean for a variable to block a path between other two variables?
What does it mean for a variable $Z$ to "block" the path between variables $X$ and $Y$ in a causal model? What is the formal definition of a "block", and how can I intuitively understand this concept? ...
0
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0answers
18 views
time series terminology question
Suppose I have a time series $x_1, x_2, ..., x_{10}$. I apply the augmented Dickey Fuller test to my data an conclude the data are non-stationary with (say) lag = 2.
With lag = 2, the tested data ...
2
votes
1answer
75 views
One sided test $H_0:\mu=0$ or $H_0:\mu\leq 0$? [duplicate]
I want to test
$$H_0: \mu \leq 0 \,\,\,\,\,\, vs \,\,\,\,\,\, H_1: \mu > 0.$$
I am using a t test, so the statistic $T$ has $\nu$ degrees of freedom which depend on the sample size.
What is ...
5
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3answers
806 views
What is the precise definition of “performance” in machine learning?
In machine learning, people usually refer to the "performance of a model" or "performance of an optimizer". What is the exact definition of "performance"? What would be the "performance of an ...
0
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0answers
26 views
Term for “the things that determine the solution to an Machine Learning(ML) problem”?
A machine learning problem can be seen as a pair $(T,L)$: a set of training data $T$, and a loss function $L$ on the training data.
I am looking for a term that captures everything about how a ...
0
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0answers
19 views
Dot subscript summation notation used in design of experiments
Context
I couldn't find a clear explanation of this on this site, and
thought that it might be of use. I have provided part of the answer, which
anyone is welcome to build from. I'm not sure how to ...
10
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2answers
574 views
Why is the Neyman-Pearson lemma a lemma and not a theorem?
This is more of a history question than a technical question.
Why is the ``Neyman-Pearson lemma'' a Lemma and not a Theorem?
link to wiki: https://en.wikipedia.org/wiki/Neyman%E2%80%93Pearson_lemma
...
2
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1answer
16 views
Mutually exclusive events, pairwise mutually exclusive events and disjoint sets
I have been confused by the phrases: mutually exclusive events, pairwise mutually exclusive events and disjoint sets. My book uses these at different places. What is the difference between them?
0
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0answers
15 views
Terminology, meaning of “Concepts” in Statistical Learning Theory
I'm studying the definition of PAC learnable:
Let C be a concept class over X.
We say that C is PAC learnable if
there exists an algorithm A with the following property:
for every ...
8
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4answers
252 views
Is “random projection” strictly speaking not a projection?
Current implementations of the Random Projection algorithm reduce the dimensionality of data samples by mapping them from $\mathbb R^d$ to $\mathbb R^k$ using a $d\times k$ projection matrix $R$ whose ...
2
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2answers
100 views
What does aggregation mean in statistics?
Im a programmer who is currently studying to become a data scientist.
I was reading this https://seaborn.pydata.org/tutorial/relational.html#emphasizing-continuity-with-line-plots in this there is a ...
1
vote
1answer
18 views
Terminology for neural networks
The words “a neural network” in machine learning can refer to either of
The architectural design of a neural network (number of layers, etc)
The network with specific parameter values encoded in it.
...
11
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5answers
1k views
What precisely does it mean to borrow information?
I often people them talk about information borrowing or information sharing in Bayesian hierarchical models. I can't seem to get a straight answer about what this actually means and if it is unique to ...
1
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2answers
223 views
What is the name for the complement of accuracy?
I have a metric that is defined as $1 - Accuracy$ and I need a name for it. Is there a scientific name for the complement of accuracy?
0
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1answer
28 views
what is latent feature in collaborative filtering
What are latent features in collaborative filtering algorithms? I've been reading about it but don't really understand it. Are latent features the learned matrices from matrix factorization; the ...
1
vote
1answer
15 views
Word for loss function except weight regularization?
A typical loss function in machine learning is:
$$L(\theta,x) = \mathcal L(\theta,x) +\sum_{\theta} |\theta|$$
I typically use the word “loss function” both for $L(\theta,x)$ and for $\mathcal L(\...
0
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0answers
27 views
Terminology for a “studentized” random variable?
Let $X_{1}, \dots, X_{n}$ be i.i.d. ramdom variables having mean $\mu$ and standard deviation $\sigma$. I wonder if the "studentized" $X_{i}$, the sample version of standardized $X_{i}$ where $\mu$ is ...
0
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1answer
20 views
What is the definition of layer in neural network?
What is the precise definition of layer in neural network? Are things like concatenate functions, activations, batch normalizations, skip connections considered as layers?
0
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2answers
59 views
Is it still called Machine Learning when the model does not learn anymore? And how is that called?
Maybe the question is too theoretical or even philosophical, maybe it's even the wrong SE-community.
I am wondering how I would call a model which is no longer trained/maintained with new data. Do I ...
0
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0answers
24 views
Random variable concept and terminology [duplicate]
I am a programmer with little mathematical background who started to study statistics/ML recently. I quickly stumbled upon the random variable term and it was hard for me to understand why in ...
0
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0answers
15 views
One shot learning on the user level?
I've been working on a text classification problem, where for each user, there can be hundreds of documents. There are about 1000 users.
I was wondering how do I call the learning type, if I decide ...
2
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2answers
33 views
Decision Tree in layman terms
How can we describe decision tree in laymen's language and what are the major fields that require this?
0
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0answers
14 views
Is “bootstrapping” in statistics the same as “bootstrapping” in machine learning?
In this article on temporal difference learning (TDL), there is a link to bootstrapping. However, the bootstrapping article seems to focus on quite different things, and I find it hard to see the ...
1
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0answers
10 views
Is there an informative term for calling the random elements conditional on which a PDF of a random element is defined?
Let $X_{1}, \dots, X_{n}$ be i.i.d. random elements; suppose the conditional PDF $f_{X_{1} \mid X_{2} , \dots, X_{n}}$ exists. Then I wonder if there is already in literature an informative name for $...
0
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0answers
20 views
Random variable or sample space element?
The most common definition of a random variable goes something like "A random variable is a mapping $X:\Omega\to \mathbb{R}$ that assigns a real number $X(\omega)$ to each outcome $\omega$". So a ...
4
votes
1answer
51 views
Association, relationship and correlation
Are measures of association, relationship and correlation the same? Different textbooks uses those words interchangeably and Im wondering if the they are the simply the same...
9
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1answer
396 views
Does the function $e^x/(1+e^x)$ have a standard name?
Does a function in the form $e^x/(1+e^x)$ have a standard name? E.g. $y = a + bx$ is a linear function.
0
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0answers
19 views
$X$ independent of $Y$ conditional on $Y$ in some subset of the domain?
Let $X,Y,\epsilon:\Omega\to \mathbb R$ be random variables. Let's say that $X=\text{sign} (Y) +\epsilon$. Then $X$ is not independent of $Y$. However, we have all the information about $Y$ that we ...
0
votes
1answer
34 views
An arbitrary error function
I have read this paper Piece-wise quadratic approximations of arbitrary error functions for fast and robust machine learning
My question is
What does the arbitrary error functions mean?
0
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1answer
11 views
A generic terminology for a given object to be estimated?
If one has to suggest, what is it one would call a given object that is to be estimated?
We already have a generic term "estimator" on the one hand. When the context is clear, usually there is no ...
0
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1answer
54 views
what type of sample is this?
I have the folowing table:
according to this example, there are 40 observations distributed over 10 stores and 4 weeks of the month. Objective: to make a sample of 90%, 80%, 75% and 50% of the 40 ...
1
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3answers
51 views
Standard normal intuitive understanding
What does it mean for a standard normal to have mean 0 and standard deviation 1?
I'm having trouble understanding - what is a "normal variable"?
1
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0answers
28 views
Asymmetry effect vs leverage effect
Regarding GARCH models, many authors use the terms asymmetric effects and leverage effects interchangeably and they left me with a doubt on whether these two terms are synonymous.
I get that, for ...
1
vote
2answers
100 views
ELI5: What is a Discrete Distribution with finite support?
A list of discrete event distributions are labeled as either with or without finite support.
https://en.wikipedia.org/wiki/List_of_probability_distributions#Discrete_distributions
What does it mean ...
15
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1answer
989 views
Is supervised learning a subset of reinforcement learning?
It seems like the definition of supervised learning is a subset of reinforcement learning, with a particular type of reward function that is based on labelled data (as opposed to other information in ...
1
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0answers
55 views
Rotational invariance of PPCA
From here (slide 23) and here (page 5, 4th slide) I understand that it is said that PPCA (probabilistic PCA) is rotational invariant. It can be written as follows:
$$\text{PPCA}(X) = [\mu, W, \sigma^...
0
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0answers
51 views
monotonic vs non monotonic models
I am reading this paper and it mentions monotonic and non monotonic functions. I only only what is monotonic and non monotonic function is, I do not really understand it in the scope of their ...
1
vote
1answer
67 views
Factor-loadings vs Variable-loadings
In PCA and Factor Analysis, there is the term loadings, which refers to factor loadings (onto the original variable).
Does the term (original) variable loading (onto the latent factor) exist?
1
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2answers
45 views
What exactly is called “embedding” in dimensionality reduction?
In the following slide I do not understand the definition of the term embedding.
In the third paragraph, it says it is a mapping from low-dim. to high-dim, but in the last paragraph it suggests that ...
1
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0answers
41 views
What does the word “MNIST” stand for “MNIST database”? [closed]
I read about "MNIST database" on this Wikipedia page which says "MNIST" stands for "Modified National Institute of Standards and Technology". But I see someone uses "MNIST" as "MNIST database" in ...
2
votes
1answer
10 views
Term for one snapshot of dynamic data
I want to introduce the data I have taken from a dynamic dataset (GitHub repos) at 12pm today. I want to say the equivalent of "This is the data as of 12pm Sept 4" but am wondering if there is a ...
1
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1answer
32 views
Technical term for an assumption about probabilities?
There is a term of art that I either never learned or forgot, referring to an assumption in the context of estimating probabilities. I cannot find it in a quick search of Wiki on, for example, the ...
0
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0answers
20 views
In a distribution what do I call regions with high probability mass, that contain smaller peaks within them?
Suppose I have some multimodal distribution as shown below.
It has two regions of high probability, highlighted in purple.
And within those regions are smaller peaks, highlighted in green.
Is there ...
1
vote
1answer
44 views
Layman's explanation on stochastic and statistical models
What's the differences between stochastic models (process) and statistical model (analysis). As I understand, a stochastic model (process) simply means it involves random variables, which is basically ...
1
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0answers
23 views
optimizers in deep learning [duplicate]
I'm new to the deep learning field and I've a question that I didn't get for it a clear answer or explanation in the course, I understand the tensors and the idea behind batches but I don't really get ...
0
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0answers
24 views
Confusion over terminology for time series classification
This probably is due to my lack of a reference textbook for statistical modeling of time series, anyway I'm not sure which terms we use to distinguish between two different time series classification ...
0
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0answers
67 views
Are Bias-Variance trade-off and Mean Squared Prediction Error (MSPE) the same?
I was reading about the Bias-Variance trade-off in the textbook "Elements of statistical learning". Is the expected forecast error listed there the same as the MSPE?
