Easy text on conditional probability and Bayes theory

In summary, the conversation is about a machine learning course where the students have little understanding of conditional probability and Bayes rule. The teacher suggests a "really easy" text on the topic and offers a link to a guide as well as a video. The conversation ends with the teacher expressing hope that the resources will be helpful for the students.
  • #1
wmac
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Hi,

I am teaching a machine learning course and the students have very poor knowledge about conditional probability, Bayes rule etc. Most students have done their undergraduates years ago and I guess their educational background has not been that good. Last lecture was on Naive Bayes classification method and some students were complaining that they understand almost nothing!

Could you please suggest some "really easy" text on conditional probabilities, Bayes rule etc.

Thanks.
 
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  • #2
Greetings,
Not so sure if going easy is what helps most...
Anyways I hope http://web.mit.edu/jorloff/www/18.05/pdf/class3-prep.pdf.
Semi-dummies Guide no offense.
Literally for dummies.
Do you want a video?! My god...Don't be so Science loving and give nothing in return..I would appreciate liking what I gather up for you, thanks. (not begging just being scientifically greedy)
 
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  • #3
Thank you very much. It is very much appreciated. I hope it helps them.
 

1. What is conditional probability?

Conditional probability is the probability of an event occurring given that another event has already occurred. It is denoted as P(A|B), where A is the event we are interested in and B is the event that has already occurred.

2. How is conditional probability calculated?

Conditional probability is calculated by dividing the probability of the joint occurrence of both events (P(A∩B)) by the probability of the event that has already occurred (P(B)). This can also be written as P(A|B) = P(A∩B) / P(B).

3. What is Bayes theory?

Bayes theory, also known as Bayes' theorem or Bayes' rule, is a mathematical formula that describes the relationship between conditional probabilities. It allows us to update our beliefs about the likelihood of an event occurring based on new information or evidence.

4. How is Bayes theory used in real-life situations?

Bayes theory is often used in fields such as statistics, data analysis, and machine learning to make predictions or draw conclusions based on incomplete or uncertain information. It is also commonly applied in medical diagnosis, weather forecasting, and marketing research.

5. What is the difference between Bayes theory and frequentist statistics?

The main difference between Bayes theory and frequentist statistics is that Bayes theory incorporates prior knowledge or beliefs into the calculation of probabilities, while frequentist statistics relies solely on observed data. Additionally, Bayes theory uses probabilities to describe uncertainty, whereas frequentist statistics uses confidence intervals.

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