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The concept of IID is fundamental in statistical analysis and machine learning models. This post has reviewed the IID in three different contexts: sampling, modelling, and predictability in time series analysis.
I am trying to understand the difference between iid and non-iid data. Let's consider a given time series, and say it's reasonable to assume that at each time point the random variable $X_t$ depends on $X_{t-1}$. Now say someone gives me the dataset $ D = \{ ( t_i, x_i ) \} $ for $i=1 \dots n$.
This property is usually abbreviated as i.i.d., iid, or IID. IID was first defined in statistics and finds application in different fields such as data mining and signal processing.
17 lis 2020 · A random variable is variable which contains the probability of all possible events in a scenario. For example, lets create a random variable which represents the number of heads in 100 coin tosses. The random variable will contain the probability of getting 1 heads, 2 heads, 3 heads.....all the way to 100 heads.
There are basically two approaches to database design: (a) top-down (i.e from conceptual entity to attribute), and (b) bottom-up (i.e from attribute to logical entity).
8 lis 2023 · Identifying the purpose and scope of the database – what data needs to be stored and why. Determining what applications will use the database – this helps anticipate future data access needs. Interviewing stakeholders and users to understand reporting, analysis, and other requirements.
18 wrz 2020 · Once we see images as vector random variables, “iid” means the same as always. Yes, there are relationships between the pixels of an individual image, same as there are relationships between the components of a multivariate normal distribution with a non-diagonal covariance matrix.
