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Pythagoras0 (토론 | 기여)님의 2021년 2월 17일 (수) 01:17 판
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  1. Statistical models are sometimes termed “black-box” models.[1]
  2. Statistical models involve the estimation of parameters, usually from some form of regression.[1]
  3. Statistical models contain variables that can be used to explain relationships between other variables.[2]
  4. We use statistical models to find insights given a particular set of data.[2]
  5. Later, in the hierarchical models chapter, we will describe one of the most influential statistical methods in the analysis of genomics data.[3]
  6. Statistical models We showed some specific statistical models for experiments with categorical outcomes (binomial and multinomial).[4]
  7. A statistical model is a mathematical model that embodies a set of statistical assumptions concerning the generation of sample data (and similar data from a larger population).[5]
  8. A statistical model is usually specified as a mathematical relationship between one or more random variables and other non-random variables.[5]
  9. All statistical hypothesis tests and all statistical estimators are derived via statistical models.[5]
  10. The first statistical assumption constitutes a statistical model: because with the assumption alone, we can calculate the probability of any event.[5]
  11. When data analysts apply various statistical models to the data they are investigating, they are able to understand and interpret the information more strategically.[6]
  12. While data scientists are most often tasked with building models and writing algorithms, analysts also interact with statistical models in their work on occasion.[6]
  13. There are many different types of statistical models, and an effective data analyst needs to have a comprehensive understanding of them all.[6]
  14. Before any statistical model can be created, an analyst needs to collect or fetch the data housed on a database, clouds, social media, or within a plain excel file.[6]
  15. In this post we cover some of the common Statistical models in Predictive Analytics.[7]
  16. By the end of the course the student learns the basic notions to define statistical models.[8]
  17. We allow for any number and types of structures, and any statistical model.[9]
  18. Statistical models are, however, bound to their calibration range and can only predict results within the data space they are calibrated from.[10]
  19. Since they are based on correlation and not causality, statistical models are actually a black box and do not provide any mechanistic process understanding.[10]
  20. A statistical models is generally a mathematical representation of observed data.[11]
  21. When data analysts apply various statistical models to the data they are working on, they are able to understand and interpret the information more strategically.[11]
  22. Although it is usually not made explicit, every sensible statistical model admits such an extension.[12]
  23. We introduce the notion of a-diffeological statistical model, which allows us to apply the theory of diffeological spaces to (possibly singular) statistical models.[13]
  24. In particular, we introduce a class of almost 2-integrable-diffeological statistical models that encompasses all known statistical models for which the Fisher metric is defined.[13]
  25. This class contains a statistical model which does not appear in the Ay–Jost–Lê–Schwachhöfer theory of parametrized measure models.[13]
  26. Then, we show that, for any positive integer, the class of almost 2-integrable-diffeological statistical models is preserved under probabilistic mappings.[13]
  27. The Wolfram Language's symbolic architecture makes possible a uniquely convenient approach to working with statistical models.[14]
  28. This weak assumption is useful for devising realistic models but it renders statistical inference very difficult.[15]
  29. A critical observation repeatedly made by reviewers of statistical models is the inclusion of unwanted correlations in data.[16]
  30. It is the concept of developing a statistical model of the population.[17]
  31. The statistical model We want the statistical model to describe variation in human height (ie.[17]

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Spacy 패턴 목록

  • [{'LOWER': 'statistical'}, {'LEMMA': 'model'}]
  • [{'LOWER': 'statistical'}, {'LEMMA': 'model'}]
  • [{'LOWER': 'models'}, {'OP': '*'}, {'LEMMA': 'Statistical'}]