Parameter Count | Question 6 | Chapter 1 | Bayesian Reasoning & Machine Learning

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Easy to follow worked solution to question 6, chapter 1 from David Barber's textbook 'Bayesian Reasoning and Machine Learning'.

  1. TwinEd Productions
  2. machine learning
  3. probability
  4. factorisation
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Very nice video.. simple short and can have clear understanding

Mithu
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if we have mixed variables as such, can you please explain on how can we calculate the same. Example :- Consider the problem of learning a function 𝑋→𝑌
, where 𝑌
is Boolean. 𝑋
is an input vector (𝑋1, 𝑋2)
, where 𝑋1
is categorical and takes 3 values, and 𝑋2
is a continuous variable (normally distributed). What would be the minimum number of parameters required to define a Naive Bayes model for this function?

saikrishnaprasadmunnaluri
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This was helpful! Is there a formula which we can use to calculate the number of parameters without having to write out all the probabilities.

ankurlohiya