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Questions tagged [intuition]

Questions that seek a conceptual or non-mathematical understanding of statistics.

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0answers
22 views

Why is degree of freedom so important? [duplicate]

As far as I'm concerned, the degree of freedom is simply the number of linear equations need to be satisfied. However, it seems closely related to the statistical deduction. For example Dividing by ...
8
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13answers
4k views

If 'B is more likely given A', then 'A is more likely given B'

I am trying to get a clearer intuition behind: "If $A$ makes $B$ more likely then $B$ makes $A$ more likely" i.e Let $n(S)$ denote the size of the space in which $A$ and $B$ are, then Claim: $...
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2answers
63 views

Intuitive explanation of how UMAP works, compared to t-SNE

I have a PhD in molecular biology. My studies recently started to involve high dimensional data analysis. I got the idea of how t-SNE works (thanks to a StatQuest video on YouTube) but can't seem to ...
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0answers
22 views

Intuition behind MA(q) (moving average) time series forecasting model (i.e. 'MA' part of ARIMA) and implementation

The $AR(n)$ part of ARIMA makes sense to me. If $$x_{t+1}=\sum_{i=0}^n a_ix_{t-i}$$ then we are making the intuitive assumption that the next time step will somehow depend on the previous time ...
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2answers
86 views

Gaussian Processes: A Crucial Assumption?

I'm reading this paper, and I've come to what seems to be a pretty crucial assumption: Now, the n observations in an arbitrary data set, y = {y1, . . . , yn}, can always be imagined as a single ...
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0answers
17 views

Is it fair to consider rolling regression a form of bootstrapping?

The context is time series analysis. A few similarities between rolling regression and boostrapping jump out at me, in that both re-use observations to form new subsamples for estimation. However, ...
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0answers
22 views

What does this mean? Can you explain it to me in laymans term? [duplicate]

How to interpret this? What does the ARIMA(0,0,0)(0,1,0)[12] means in laymans term?
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3answers
112 views

Why ARIMA is prefered over any other time series analysis method

I am new to time series analysis, and I am self learner. I am using R language to learn how to do time series analysis. I started by studying the concepts and the theory behind such analysis, however ...
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1answer
451 views

Evidence for man-made global warming hits 'gold standard': how did they do this?

How should we interpret the $5\sigma$ threshold in this research on climate change? This message in a Reuter's article from 25 february is currently all over the news: They said confidence that ...
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0answers
19 views

What scenario corresponds to choosing the “true distribution” $p$ in $\textsf{KL}(p\parallel q)$?

I understand that when you think about changing $q$ in the Kullback-Leibler divergence $\textsf{KL}(p\parallel q)$, this corresponds to trying to find the distribution that minimizes information loss ...
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0answers
32 views

Which properties yield the exponential family of distributions?

It seems like every resource that discusses exponential families simply defines the family of distributions, explains why it's useful and then derives some of its properties. I have only seen one ...
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1answer
51 views

Intuition behind gradient of expected value and logarithm of probabilities

I recently came across the following curious identity: $$\nabla_\theta \mathbb{E}_{x \sim D_\theta}[f(x)] = \mathbb{E}_{x \sim D_\theta} [ \nabla_\theta \log(D_\theta(x)) f(x)],$$ where $D_\theta$ ...
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0answers
25 views

Intuition of variance (in the context of linear regression)

I was studying linear regression lately and checking the assumptions for Ordinary Least Squares method for the regression problem. I was not sure about the intuition behind the difference of squares ...
1
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4answers
56 views

How do I intuitively understand that independence is always symmetric?

Independence between two events, $A$ and $B$, is a symmetric relation, that is, if $P(A \mid B) = P(A)$, then $P(B \mid A) = P(B)$. The proof is very simple and can be found at the ProofWiki. ...
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2answers
86 views

Understanding the parameters needed for a distribution in Bayes networks?

Since I have a discriminative mindset hardly can I intuit the so-called parameters needed to specify a distribution in a generative Bayesian Network. I'd like to borrow an example from this blog. If ...
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0answers
8 views

What's a simple explanation for risk and its formula in survival analysis, weibull regression

I have that if the model is $\ln(\mu_i) = \beta_0 + \beta_1 x_1$ where $x_1 \in \{0,1\}$ and represents tired (or anything suitable, sex, etc). The model also has a shape parameter, $\gamma$. ...
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0answers
30 views

Idea behind change of basis and how it relates to projecting your points onto principal components

I would like to clarify if my understanding is correct. In the traditional X-Y coordinate system, our choice of basis vectors are $\vec{i} = (1, 0)$ and $\vec{j} = (0, 1)$ and when you I have a point $...
2
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1answer
35 views

Explaining modelling in simple terms

One of the most challenging ideas to describe to non-technical/mathematical people is "modelling". I've used quite a few explanations so far but all of them were around specific modelling techniques. ...
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0answers
19 views

Anyone can explain simply targeted learninig? [duplicate]

I am trying to understand Targeted Learning (Mark van der Laan), can anyone explain this method simply, please?
1
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1answer
44 views

Intuition behind the no convergence of the variance of sum of random variables

$$Var[\bar{X}] = \sigma^2/n $$ $$Var [\sum{X}_i] = n\sigma^2$$ $$lim_{n \to \infty} Var[\bar{X}] = 0 $$ wich means at $\infty$ we will always get the same $\bar{X}$ after every simulation. I ...
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0answers
33 views

Intuition behind White's Estimators/ Heteroscedasticity-consistent Standard Errors

For a medical study I am trying to understand the intuition behind heteroscedasticity-consistent standard errors. I know that it can be used, when in OLS regression residuals are heteroscedastic. By ...
2
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0answers
36 views

intuitive explanation for expected value of the square of a uniform variable

I'm confused about something that should be simple. Suppose I have a random uniform variable $X$ on $[0,1]$. It's fairly clear that the expected value of $X$ is 1/2. By integrating $x^2$ on $[0,1]$, I ...
4
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3answers
401 views

Explaining multicollinearity in layman's terms

Say we have a study where we want to run a logistic regression on a group of people, and we want to find out whether one attribute of a person makes them more likely to be a smoker. So we have smoker ...
2
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2answers
108 views

Intuitive understanding of variance of sum vs variance of difference

$\newcommand{\Var}{\operatorname{Var}}\newcommand{Cov}{\operatorname{Cov}}$Mathematically, $\Var(X + Y) = \Var(X) + \Var(Y) + 2\Cov(X,Y)$ and $\Var(X - Y) = \Var(X) + \Var(Y) - 2\Cov(X,Y)$ This ...
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0answers
83 views

Is there any intuition behind the writing Jensen-Shannon divergence based on the entropy?

I know that Jensen-Shannon divergence between two distribution $P$ and $Q$ can be written as follow: $JS(P||Q) = H(\frac{P+Q}{2}) - \frac{H(P)}{2} - \frac{H(Q)}{2}$ But is there any intuition behind ...
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0answers
22 views

How to Calculate Percent of Sample mean falls within Population Mean? [closed]

Following screenshot is from Udacity Statistics Tutorial. Klout score which is Social Media popularity score is distributed as shown in picture. Population mean is 37.72 and Standard Deviation is ...
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0answers
130 views

What guarantees the existence of a finite representation of the Wold decomposition? Mechanics and Intuition

Every covariance stationary process can be written as a linear, infinite distributed lag of white noise. In other words, every covariance stationary process has a Wold representation. Then we go on to ...
3
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2answers
78 views

What does it mean to model data as binomial?

On Wikipedia it says [T]he binomial distribution with parameters $n$ and $p$ is the discrete probability distribution of the number of successes in a sequence of n independent experiments, ...
7
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2answers
279 views

Uncorrelatedness + Joint Normality = Independence. Why? Intuition and mechanics

Two variables that are uncorrelated are not necessarily independent, as is simply exemplified by the fact that $X$ and $X^2$ are uncorrelated but not independent. However, two variables that are ...
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2answers
2k views

Is Cauchy distribution somehow an “unpredictable” distribution?

Is Cauchy distribution somehow an "unpredictable" distribution? I tried doing cs <- function(n) { return(rcauchy(n,0,1)) } in R for a multitude of n values ...
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0answers
27 views

Gaussianity and Whitening in ICA - The feeling and intuition behind it

I understand what ICA does at a high level but in the cocktail party problem context. All the examples, articles I have read take a similar problem to explain ICA where the aim is to derive the ...
0
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1answer
25 views

When and when not to use activation function between input layer and hidden layer? [duplicate]

I am beginner to Neural Network and this question might be very basic and stupid. Expecting some intuitive answer.
0
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1answer
80 views

What to conclude for the data-set when the variance for principal components is too low or too high?

I am working on analysing and visualizing a dataset having 12 features and came across PCA. I reduced the dataset to 2 principal components which together explain a variance of 18%. I was able to plot ...
1
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1answer
39 views

Sampling from a finite sequence without replacement yields exchangeable sequences?

I have read (and re-read) the wikipedia article on Exchangeability https://en.wikipedia.org/wiki/Exchangeable_random_variables . The disconnect for me is that : after having sampled without ...
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0answers
402 views

Interpreting Root Mean square Error (RMSE )when dependent variable is log transformed

So I have developed a model to predict the house prices in a county.I log transformed the target variable (home price).I split the data into training and testing sets.I trained the model on the ...
2
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0answers
44 views

Why truncated SVD can denoise images

There are a lot of empirical results about that truncated SVD (TSVD) can help denoise the noises of images, but I wonder what is the theoretical support behind that? We know that TSVD is the best low-...
5
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1answer
72 views

multivariate Student's t distribution: intuition for non-independence?

Consider a multivariate Student's t distribution, with parameters $\nu$ (d.f.), $\mu$ (location) and $\Sigma$ (shape). Does anyone have a good intuition for the individual components not being ...
6
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2answers
131 views

Can an optimal weighted average ever have negative weights?

I've got a few measurements $\vec{x}$ for some real-world value $\hat{x}$. These measurements have some uncertainty, and are correlated. Given these estimates, and their covariances, I want to take ...
2
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1answer
143 views

Memoryless property of exponential

Let $X$ be an exponential random variable. I have been asked to evaluate whether each of the following is true or false and am seeking some insight about a solution that was offered. $(a) E(X^2 |X>...
0
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1answer
53 views

Why, *intuitively*, in regular parametric problems, does uncertainty go down at a $\sqrt{ n }$ rate on the SE/posterior SD scale?

consider the simplest regular statistical inference problem: $( y_1, \dots, y_n | F ) \sim$ $\text{IID}$ from a cumulative distribution function $F$ on $\mathbb{ R }$ with mean $\mu$ and finite ...
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1answer
35 views

Can someone explain variance and bias of data in natural language (not mathematically)

I read many tutorial but until now i don't understand what the variance and bias means. So can someone explain me what these words means clearly, not in mathematical language.
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3answers
559 views

Intuition behind $(X^TX)^{-1}$ in closed form of w in Linear Regression

The closed form of w in Linear regression can be written as $\hat{w}=(X^TX)^{-1}X^Ty$ How can we intuitively explain the role of $(X^TX)^{-1}$ in this equation?
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1answer
49 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 ...
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0answers
69 views

Intuitive explanation of the Wald Chi-Squared Test

I am having trouble understanding what the Wald Chi Squared test is used for intuitively. SPSS uses the Wald Test (type III) in the "Tests of Model Effects" box when I run my GEE models, but I don't ...
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2answers
243 views

True probability vs estimated probability

Is it correct to think that the true probability of an event cannot be ever known? When studying probability, in the first lectures, there are those typical exercises which start with sentences like: ...
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0answers
20 views

How can we best utilize the knowledge of P(y=1) in classification? [duplicate]

Premise I saw an interesting example of a machine learning logistic classifier for modeling/predicting sentiment for customer reviews. One of the first things in the example was a note on ...
7
votes
2answers
166 views

Clarification of the intuition behind backpropagation

I've been taking some time to try and understand the computations and mechanics of the machine learning algorithms I use in my day to day life. Studying the backpropagation literature on the CS231n ...
8
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1answer
135 views

Intuition (geometric or other) of $Var(X) = Var(E[X|Y]) + E[Var(X|Y)]$

In another installment of intuitions for identities in probability, consider the elementary identity Law of Total Variance $$ \begin{eqnarray} \rm{Var}(X) &=&\rm{E}[\rm{Var}(X|Y)] + \rm{Var}(...
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2answers
42 views

My regression/ML algorithm has completed. Can linear algebra still play a part? [closed]

Premise It's a non-argument that linear algebra is a fundamental tool for many fields that use statistics. That being said, how fully one needs to grasp it often varies. Computers are very good at ...
8
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1answer
165 views

Intuitive explanation/motivation of stationary distribution of a process

Often, in literature, authors have been interested in finding the stationary distribution of a time-series process. For example, consider the following simple AR($1$) process $\{X_t\}$: $$X_t = \alpha ...