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- Dictionarylikely/ˈlʌɪkli/
adjective
- 1. such as well might happen or be true; probable: "speculation on the likely effect of opting out" Similar Opposite
- 2. apparently suitable; promising: "a likely-looking spot" Similar
adverb
- 1. probably: "we will most likely go to a bar"
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Unless you quantify what 'likely' means and what 'high value' means, it can not be used in any quantitative analysis. You can define your response to be 'customers with annual revenue exceeding $1,000'. You need to ask yourself the question: if I send this to a team in India, will they define 'likely high value customer' as was intended."
Mar 5, 2012 · The wikipedia page claims that likelihood and probability are distinct concepts.. In non-technical parlance, "likelihood" is usually a synonym for "probability," but in statistical usage there is a clear distinction in perspective: the number that is the probability of some observed outcomes given a set of parameter values is regarded as the likelihood of the set of parameter values given the observed outcomes.
Aug 14, 2016 · The difference is easiest to see with discrete distributions: Consider two sets of values where each number is equally likely to be drawn: {1,2,2,2,10} and {1,2,2,2,3}. Both have the same mode (2), but the expected values differ. Expected value puts extra weight on large values while the mode simply looks for what value occurs frequently.
Sep 16, 2021 · From this perspective, A A is less likely to occur. So, in a single trial A A is more likely to occur than others, but from the perspective of multiple trials, A A is less likely to occur. An example is an event of 100 100 coin tosses. The probability of getting 50 50 heads is roughly 0.08 0.08. This is more than any other probability.
Jul 22, 2016 · 18. It's not true in general that the expected value is the most likely outcome. Even for binomial distributions. For example, say we flip a fair coin 5 times. The number of heads has a binomial distribution with expected value 2.5. It's not even possible to obtain this outcome (similar to what Zen mentioned in the comments about rolling a die).
Mar 1, 2016 · But we do need to define sigma-algebras for larger sample spaces, such as the real line, so that we can avoid pathological subsets that break down our measures. In order to achieve consistency in the theoretical framework of probability, we require that finite sample spaces also form sigma algebras, where only in which is the probability measure defined.
Sep 15, 2018 · 1. Usually "estimation" is reserved for parameters and the "predicition" is for values. However, sometimes the distinction gets blurred, e.g. you may have seen something like "estimate the value tomorrow" instead of "predict the value tomorrow." The value-at-risk (VaR) is an interesting case.
8. Feature space just refers to the collections of features that are used to characterize your data. For example, if your data is about people, your feature space might be (Gender, Height, Weight, Age). In a SVM, we might want to consider a different set of characteristics to describe the data, such as (Gender, Height, Weight, Age^2, Height ...
Mar 7, 2016 · The dashed line is the median. The dotted line is the mode. The mean represents the positions of the data points along the x axis, while the median reflects only the number of data points on either side. The mode is just the point of greatest probability, which is different from both the mean and the median. R code:
Dec 13, 2011 · 2. Time Series is about analysing the way values of a series are dependent on previous values. As SRKX suggested one can difference or de-trend or de-mean a non-stationary series but not unnecessarily!) to create a stationary series. ARMA analysis requires stationarity.
