Probability Notes and Concepts

• Prior distribution that incorporates your subjective beliefs about a parameter  
• Posterior is the result with data
• Likelihood - Based on the posterior the how ‘likely’ is the data is going to occur
• Bayesian Linear Regression - The response, y, is not estimated as a single value, but is assumed to be drawn from a probability distribution. Determine the posterior distribution for the model parameters
• Latent variables as opposed to observable variables), are variables that are not directly observed but are rather inferred (through a mathematical model) 
• A Bernoulli random variable has two possible outcomes: 0 or 1. A binomial distribution is the sum of independent and identically distributed Bernoulli random variables.
• Poisson distribution - events occurring in a fixed interval of time or space if these events occur with a known constant rate and independently of the time since the last event
• Negative binomial distribution describes the number of successes k until observing r failures

Happy Learning!!!