Maths
- Bayesian Rule: p(z|x)= p(x|z) p(z) /p(x)
- Prior Distribution: "often simply called the prior, of an uncertain quantity is the probability distribution that would express one's beliefs about this quantity before some evidence is taken into account." (e.g. p(z))
- Posterior Distribution: is a probability distribution that represents your updated beliefs about the parameter after having seen the data. (e.g. p(z|x))
- Posterior probability = prior probability + new evidence (called likelihood)
- Probability Density Function (PDF): the set of possible values taken by the random variable
- Gaussian (Normal) Distribution: A symmetrical data distribution, where most of the results lie near the mean.
- Bayesian Analysis:
- Prior distribution: p(z)
- Gather data
- "Update your prior distribution with the data using Bayes' theorem to obtain a posterior distribution."
- "Analyze the posterior distribution and summarize it (mean, median, etc.)"
- It is expected that you have knowledge of neural network concept (gradient descent, cost function, activation functions, regression, classification)
- Typically used for regression or classification
- Basically: fit(X,Y) and predict(X)