Andrew Gurung
  • Introduction
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    • Linear Algebra
      • Linear algebra explained in four pages
      • Vectors
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      • Linear Algebra: Deep Learning Book
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      • Maxima and Minima using Derivatives
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    • Statistics and Probability
      • Probability Rules and Axioms
      • Types of Events
      • Frequentist vs Bayesian View
      • Random Variables
      • MLE, MAP, and Naive Bayes
      • Probability Distributions
      • P-Value and hypothesis test
    • 7 Step DS Process
      • 1: Business Requirement
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      • 6: Model deployment
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  • Frequentist View
  • Bayesian View

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  1. Data Science
  2. Statistics and Probability

Frequentist vs Bayesian View

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Last updated 6 years ago

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Frequentist View

  • Defines probability of some event in terms of the relative frequency with which the event tends to occur

  • More widely used and usually involves simpler calculations

  • Frequentists think deductively: "If the true population looks like this, then my sample might look like this."

  • Terminology: p value, significant, null hypothesis, or confidence interval

  • Draw conclusions strictly from what’s in that set of given data

Bayesian View

  • Defines probability in more subjective terms — as a measure of the strength of your belief regarding the true situation

  • Requires powerful computers and sophisticated software

  • Bayesians think inductively: "My sample came out like this, so the true situation might be this."

  • Terminology: prior probability, noninformative priors, and credible intervals

  • Broader view of "usable information" which typically starts with some prior probabilities (based on previous experiments) and then blend in the results of the latest experiment to revise those probabilities

Note:

  • A population includes all of the elements from a set of data.

  • A sample consists one or more observations drawn from the population.

Link: - Dummies Series:

TWO VIEWS OF PROBABILITY