"확률 질량 함수"의 두 판 사이의 차이

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* ID :  [https://www.wikidata.org/wiki/Q869887 Q869887]

2020년 12월 26일 (토) 06:00 판

노트

  • The probability mass function (pmf) characterizes the distribution of a discrete random variable.[1]
  • Often, probability mass functions are plotted as column charts.[1]
  • The probability mass function above is defined in the “standardized” form.[2]
  • We can add the corresponding values from the pmf.[3]
  • Plug each value of \(x\) into the pmf.[3]
  • The Symbulate pmf method can be used to compute the pmf for named distributions.[3]
  • The constant \(e^{-2.3}\) doesn’t affect the shape of the probability mass function.[3]
  • Plot the probability mass functions for the Poisson distribution with mean 4.5 and 12 respectively.[4]
  • …distribution is defined by a probability mass function, denoted by f(x).[5]
  • The probability mass function of a discrete random variable is a list of probabilities associated with each of its possible values.[6]
  • The Probability Mass function is defined on all the values of R, where it takes all the argument of any real number.[7]
  • This is the reason why probability mass function is used in computer programming and statistical modelling.[7]
  • Probability mass function plays an important role in statistics.[7]
  • We can represent probability mass functions numerically with a table, graphically with a histogram, or analytically with a formula.[8]
  • In the next three sections, we will see examples of pmf's defined analytically with a formula.[8]
  • A probability mass function (PMF)— also called a frequency function— gives you probabilities for discrete random variables .[9]
  • The area under a graph of a probability mass function is 100% (i.e. the probability of all events, when added together, is 100%).[9]
  • However, some authors (not many) use the term “probability mass function” to mean either a discrete or continuous probability distribution.[9]
  • The aim of the probability mass function is to describe the probability of each possible value.[10]
  • We also have two probability mass functions associated with the random variables: P(x) and P(y).[10]
  • In the case of the probability mass function, we saw that the y-axis gives a probability.[10]
  • An example of a multivariate discrete distribution , and of its probability mass function, is provided by the multinomial distribution .[11]
  • The binomial probability mass function is a very common discrete probability mass function that has been studied since the 17th century.[12]
  • Figure 1 shows this probability mass function for the case p = 0 .[12]

소스

  1. ↑ 1.0 1.1 Probability mass function
  2. ↑ scipy.stats.binom — SciPy v1.5.4 Reference Guide
  3. ↑ 3.0 3.1 3.2 3.3 An Introduction to Probability and Simulation
  4. ↑ R lab 2 solution
  5. ↑ Probability mass function | statistics
  6. ↑ Common Discrete Probability Functions
  7. ↑ 7.0 7.1 7.2 Probability Mass Function
  8. ↑ 8.0 8.1 3.2: Probability Mass Functions (PMFs) and Cumulative Distribution Functions (CDFs) for Discrete Random Variables
  9. ↑ 9.0 9.1 9.2 Probability Mass Function (PMF) / Frequency Function: Easy Definition, Examples
  10. ↑ 10.0 10.1 10.2 Probability Mass and Density Functions
  11. ↑ Probability mass function
  12. ↑ 12.0 12.1 Probability Mass Function - an overview

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