We will cover a few key concepts before moving on to the body of the chapter:
- In the case of discrete distribution, a probability mass function is used to find out the probability, p(X= x), where X is a discrete random variable and x is a real value number.
- In the case of continuous distribution, probability density function is used to find out the probability p(X <= x). In this scenario, a probability curve is plotted and the area under the curve (integration) helps us with the probability.
- Conditional probability is to understand this, a cricket match can be the perfect example. Suppose there is a game scheduled between India and Australia and we are trying to pass on our belief of India triumphing. Do you think that the probability will be impacted by the team selected by India? Will the probability of India winning the match be impacted if Virat Kohli and Rohit Sharma are part of the team? So, p(India winning|Rohit and Virat are playing) denotes the probability of India...