StatisticalLearningLib.bib

@book{dalpiazChapterBiasVariance,
  title = {Chapter 8 {{Bias}}\textendash{{Variance Tradeoff}} | {{R}} for {{Statistical Learning}}},
  author = {Dalpiaz, David},
  abstract = {Chapter 8 Bias\textendash Variance Tradeoff | R for Statistical Learning},
  file = {C\:\\Users\\Maurizio\\Zotero\\storage\\W6QKA7FT\\Dalpiaz_Chapter 8 Bias–Variance Tradeoff R for Statistical Learning.pdf;C\:\\Users\\Maurizio\\Zotero\\storage\\BJUILVDQ\\biasvariance-tradeoff.html}
}

@misc{DecisionTheoryStatistics,
  title = {Decision Theory | Statistics | {{Britannica}}},
  abstract = {decision theory, in statistics, a set of quantitative methods for reaching optimal decisions. A solvable decision problem must be capable of being tightly formulated in terms of initial conditions and choices or courses of action, with their consequences. In general, such consequences are not known with certainty but are expressed as a set of probabilistic outcomes. Each outcome is assigned a ``utility'' value based on the preferences of the decision maker. An optimal decision, following the logic of the theory, is one that maximizes the expected utility. Thus, the ideal of decision theory is to make choices rational by reducing},
  howpublished = {https://www.britannica.com/science/decision-theory-statistics},
  langid = {english},
  file = {C\:\\Users\\Maurizio\\Zotero\\storage\\SGJC9NIS\\decision-theory-statistics.html}
}

@misc{LawIteratedExpectation,
  title = {Law of {{Iterated Expectation}} | {{Brilliant Math}} \& {{Science Wiki}}},
  abstract = {The Law of Iterated Expectation states that the expected value of a random variable is equal to the sum of the expected values of that random variable conditioned on a second random variable. Intuitively speaking, the law states that the expected outcome of an event can be calculated using casework on the possible outcomes of an event it depends on; for instance, if the probability of rain tomorrow depends on the probability of rain today, \ldots},
  howpublished = {https://brilliant.org/wiki/law-of-iterated-expectation/},
  langid = {american},
  file = {C\:\\Users\\Maurizio\\Zotero\\storage\\7KXGM7K4\\law-of-iterated-expectation.html}
}

@article{sohilIntroductionStatisticalLearning2022,
  title = {An Introduction to Statistical Learning with Applications in {{R}}: By {{Gareth James}}, {{Daniela Witten}}, {{Trevor Hastie}}, and {{Robert Tibshirani}}, {{New York}}, {{Springer Science}} and {{Business Media}}, 2013, \$41.98, {{eISBN}}: 978-1-4614-7137-7},
  shorttitle = {An Introduction to Statistical Learning with Applications in {{R}}},
  author = {Sohil, Fariha and Sohali, Muhammad Umair and Shabbir, Javid},
  year = {2022},
  month = jan,
  journal = {Statistical Theory and Related Fields},
  volume = {6},
  number = {1},
  pages = {87--87},
  issn = {2475-4269, 2475-4277},
  doi = {10.1080/24754269.2021.1980261},
  langid = {english},
  file = {C\:\\Users\\Maurizio\\Zotero\\storage\\FDP9FWFS\\Sohil et al. - 2022 - An introduction to statistical learning with appli.pdf}
}