Questions tagged [terminology]

Usage and meaning of specific technical words/concepts in statistics.

Filter by
Sorted by
Tagged with
0
votes
0answers
12 views

What should I be careful when using the word “supervised” in paper writing?

I am a biologist using machine learning tool for my research. I modified matrix decomposition ($V \approx WH$) to fit my data and wanted to describe about that in my paper. If I fixed one matrix ...
2
votes
0answers
26 views

What are “absolute ECDFs” called (if anybody uses them)?

By absolute ECDF, I am referring to an ECDF that shows the absolute number of samples above some value as opposed to the fraction of such samples. For example, the bottom plot below displays such an ...
4
votes
1answer
173 views

What is an induced probability function?

My textbook defined the probability function of a random variable as: the function $P_X$ is an induced probability function on $X(\Omega)$, defined in terms of the original function P. In other ...
1
vote
1answer
37 views

How to depict a histogram with a wide range of negative/positive data? Is this logarithmic?

I want to show some data on a range from -50 to 20 and I especially want to show the details as values approach 0. The graph of the data is shown below. The problem is what do you call such a scale. ...
0
votes
1answer
22 views

Does a high-dimensionality of a time series refer to its length, number of variables or both?

I'd like to cluster some time series that describe a flow of a variable (say, temperature) throughout a day. Measurements are made every 5 minutes so each time series has 288 values. Are we talking ...
0
votes
0answers
18 views

What do you call a weighted summary statisitc?

I want to present a summary statistic of 20+ simultaneous classes where each class has an AUC-statistic that I would like to describe in a single statistic, e.g.: ...
1
vote
1answer
13 views

Data points and other supportive points

In the database, we have data fields that fetch the real data points like student names and other supportive fields like primary_key, foreign_key, created_at and update_at which are used for fetching ...
0
votes
0answers
11 views

Reinforcement Learning: How to write Value Function with deterministic transition

Following notation of http://www.incompleteideas.net/book/RLbook2018.pdf Let a transition from s to s' deterministic and denote $\mathcal{R}^a_{ss'} = E[R_{t+1} | S_t = s, S_{t+1}=s', A_t=a]$ The ...
0
votes
3answers
44 views

General mathematical definition of a score

I understand what scores are in PCA, in particular this answer gives a good mathematical formulation: (Scores) are projections of the centred data in the linear space defined by the eigenvectors. ...
0
votes
1answer
21 views

Probability as the Correct Term for Classifier Scores

This is more of a semantic question, but is the term probability, in the strictest sense, the correct term to use when describing the output of a predictive model that outputs values between 0 and 1? ...
2
votes
1answer
71 views

Is there a word for percentages, like “median” is for 50%? [duplicate]

Wikipedia article Median says: In statistics and probability theory, the median is the value separating the higher half from the lower half of a data sample, a population or a probability ...
0
votes
1answer
29 views

Can a variable be both a moderator and predictor?

I have dependent variable Y and independent variables X and Z. I am not sure at all, if it is allowed or if it makes sense to state the following hypotheses: H1: Variables X and Z correlate ...
1
vote
1answer
40 views

Is there a term for an event and it's associated probability?

I understand that an event is 1 or more outcomes and has a probability associated with it; however, I am wondering if there is a term for an event and it's associated probability. Any help would be ...
0
votes
1answer
25 views

Q-Learning [Sutton]: why random variable in formula

Sutton et al. use throughout their book Reinforcement Learning capital letters to describe random variables. At page 131 they introduce Q-Learning. $Q(S_t,A_t)\leftarrow Q(S_t,A_t) + \alpha [R_{t+1} ...
7
votes
2answers
122 views

Multivariable vs multivariate regression [duplicate]

I am a little unsure about the semantics in this regard, and was hoping someone could cast some light on this. As far as I understand, multivariable is basically one dependent variable and several ...
10
votes
2answers
2k views

What does it mean for a statistical test to have a power of 0.8?

The power is a continuous function of the parameter. If a statistical test has a power of 0.8, does that mean the power function is 0.8 at all parameters except the null hypothesis? Or does it mean ...
3
votes
1answer
79 views

Plausibility, Possibility, and Probability

When we talk about probability, are we referring to the Plausibility in term of numbers? Or are we referring to the possibility in term of numbers? In statistics (or Mathematics), is there a ...
5
votes
1answer
37 views

Spurious relationships: flavours, terminology

The following types of relationships come to my mind when I think of the term "spurious": A statistical relationship specific to a sample but not the population / the data generating process (DGP). ...
1
vote
1answer
37 views

Subscript in expected value notation [duplicate]

I am a bit confused about the terminology when using subscripts in expectations and probabilities. I was reading about the reparameterization trick from the following link: "gregorygundersen - Link" ...
3
votes
3answers
129 views

Is there a difference between $\beta$ and $\theta$?

I've seen both $\beta$ and $\theta$ used to indicate model parameters in different publications. For example, Andrew Ng uses $\theta$ in his ML course and Gareth James et al use $\beta$ in ISLR. My ...
2
votes
1answer
37 views

What is it called to classify easy inputs before hard ones?

For classifying a lot of inputs, it may be useful to handle the more unambiguous cases first and learn from them before tackling the harder ones. This is certainly how I personally grade student work; ...
1
vote
1answer
38 views

What is it called to cluster some inputs, then classify other inputs into those clusters?

I am learning about the problem of whole-book recognition, which is tangential to optical character recognition. Some of the strategies used to identify printed characters rely on first unsupervised ...
4
votes
2answers
97 views

What does a data-generating process (DGP) actually mean?

I am having some trouble understanding exactly what is meant by a DGP. Let's say it is stated that "the DGP is given as $y=a+bx+e$ where the error term fulfills all the OLS assumptions. Does this mean ...
0
votes
1answer
79 views

Are Machine Learning models probabilistic models? [closed]

In terms of Machine Learning models, as far as I know, there are two kinds of models in Machine Learning. One kind are discriminative models and the other kind are generative models. But I wonder if ...
3
votes
4answers
333 views

Machine Learning VS Statistical Learning vs Statistics [duplicate]

I have seen posts about the difference between ML and Statistics. And I have also seen posts explaining that Statistical Learning is a statistical approach to ML. But then, this is confusing because ...
21
votes
6answers
2k views

Recommended terminology 'statistically significant'

Following the recent ASA and other comments on p-values and not using the term "statistically significant" what is the recommendation for presenting the results of an analysis? For example if I ...
9
votes
1answer
727 views

Is there a difference between LASSO regularisation and LASSO penalisation?

I've seen the terms LASSO regularisation and LASSO penalisation used interchangeably? Is this correct, are they the same thing or what are the differences? Thanks!
1
vote
2answers
11 views

Talking about percents of durations

I'm translating an academic paper from German into English, and am having trouble finding a noun to describe a "portion of the total duration expressed as a percent." The objects investigated in this ...
0
votes
0answers
31 views

Are concept and hypothesis the same in the context of concept learning?

The lecturer is computing the size of concept space, regarding the EnjoySport example in Tom M. Mitchell. Machine Learning (free) ...
3
votes
1answer
21 views

Does candidate feature means the point in feature space?

The CMU professor is using the term "attribute" here They are 1 2 3 4 5 6 attributes to describe TABLE 2.1 in Tom M. Mitchell. Machine Learning (free) usually, those 6 columns are also called ...
3
votes
1answer
42 views

What is “selection modeling”?

Gelman & Zelizer (2014) write: five other methods used for causal inference in observational studies: simple regression, matching, selection modeling, difference in differences, and ...
1
vote
0answers
21 views

Coverage probability and the nominal coverage probability frequently

What is the difference between coverage probability and the nominal coverage probability frequently?
3
votes
2answers
59 views

Terminology question: distinguishing two meanings of “loss function”?

I've heard people use "loss function" to refer to 2 different functions, with different type signatures: 1) A real-valued function of a label, $y$, and a prediction $\hat{y}$. 2) A real-valued ...
1
vote
0answers
17 views

What does a decision tree hypothesis look like?

I am learning Tom M. Mitchell. Machine Learning (free). in Candidate-Elimination algorithm, a single hypothesis looks like $<Sunny, ?, ?, ?, ?, ?>$ there are three hypotheses in FIGURE 2.1 in ...
0
votes
0answers
10 views

How is this form of experimental statistical design called in English?

In my textbook there is a statistical design called "Полностью случайные модели", that can be literally translated as "Completely random models". But I'm looking for existing terminology, so I could ...
0
votes
0answers
30 views

What does it mean that random variables are “drawn from the same distribution”?

In the second bullet point, what does it mean that "$X_1,X_2,...X_n$ are drawn from a common distribution"? Does it simply mean they all have the same type of distribution (e.g. they are all normally ...
1
vote
0answers
26 views

What does “completely expressive hypothesis space” mean in machine learning?

I understand the term "hypothesis space". A classifier must be represented in some formal language that the computer can handle. Conversely, choosing a representation for a learner is tantamount to ...
3
votes
1answer
48 views

Is a regression using a categorical variable a multiple regression by definition?

When running a regression with a categorical variable as the independent variable, the regression essentially picks one of the levels to leave out and runs all the other levels together. There is no ...
9
votes
0answers
85 views

What is the “direct likelihood” point of view in statistics?

I am reading a Springer title from 1997 called Applied Generalized Linear Models by James K. Lindsey. In the preface, Lindsey writes For this text, the reader is assumed to have knowledge of basic ...
3
votes
0answers
22 views

Is there a name for the probability distribution arising from the Hockey-stick Identity?

The Hockey-stick Identity: ${n \choose k} = \sum_{r=k}^n {r-1 \choose k-1}$ This naturally gives rise to a discrete probability distribution with parameters n and k, where the probability mass ...
0
votes
1answer
26 views

What does “the magnitude of the coefficients” mean here, and how to compute this quantity based on this table?

This is Table 1.1 in pattern recognition and machine learning (free) We can gain some insight into the problem by examining the values of the coefficients $w^*$ obtained from polynomials of ...
0
votes
2answers
20 views

What does residual mean in the context of minimizing a function?

equation 1.2 in PRML: pattern recognition and machine learning denotes the sum of the squares of the errors between the predictions $y(x_n,w)$ and the corresponding target values $t_n$. $w^*$ ...
0
votes
0answers
13 views

could someone please give more explanation the difference on sequential mode and batch mode in the context of naive Bayes?

in Neural networks, there are 2 concepts, batch learning and sequential learning. Page 75 of "Kevin Patrick Murphy. Machine Learning: A Probabilistic Perspective." uses these terms in naive Bayes ...
1
vote
2answers
48 views

What is a “symbolically nested” model?

survey::anova.svyglm is a method of anova() that does model comparison for survey data. The details section of the documentation ...
0
votes
1answer
17 views

could someone please double check my understanding of 2 forms that represent Machine Learning model?

This CMU Machine Learning Text Book says when we are interested in learning some target function $f : X → Y$, we can more generally learn the probabilistic function $P(Y|X)$. there are 2 forms ...
0
votes
1answer
17 views

Could someone please double check my understanding about “label space”

iris dataset contains 3 classes. So, the set {'setosa', 'versicolor', 'virginica'} is a "label space", where each example (flowers) lives in. "label space" is associated with a specific dataset and a ...
0
votes
1answer
26 views

to construct a feature space, do I have to involve all the features?

In pattern recognition and machine learning, a feature vector is an n-dimensional vector of numerical features that represent some object. The vector space associated with these vectors is often ...
1
vote
0answers
32 views

error vs. deviation vs. difference

What exactly is the difference between the way these terms are used in statistics? As far as I can tell they mean the same thing mathematically, but it's unclear to me whether and how usage depends ...
3
votes
2answers
66 views

Learning from multiple very varied data sets?

Suppose we have a set of objects $X$ (e.g. individual humans). Suppose also that humans can be described by a set of (potentially very high-dimensional) variables $V_i$, (e.g. $V_1$ is a picture of ...
1
vote
1answer
61 views

Learning problem when we have data from distributions $(p_i)$ when we care about (known) distribution $p^*$?

Suppose we have a dataset $D$ or multiple datasets $(D_i)$, with distributions $p_i:X\to \mathbb R$. Suppose there is another distribution $p^*$. All distributions are known, including $p^*$, but the $...

1 2 3 4 5 25