Probability is the chance of occurring an event. It can be expressed as a fraction or percentage. It is defined as the number of favorable cases divided by the total number of cases. At least one question related to this topic is asked in gate examination every year. Hence learn this course with full attention.
Baye’s Theorem (Conditional Probability)
Probability of an event when it is known that another event has already occurred is known as conditional probability. We can find the conditional probability by using Baye’s Theorem. Watch the video lecture given below to learn about the conditional probability with an example.
Bernoulli’s Trials / Repeated Trials / Binomial Distribution
We can find the probability of r success in n trials by using Bernoulli’s Trials formula. Here, n is the number of trials (for example tossing a coin n times). This is called repeated trials because we repeat the event n number of times. Watch the video lecture to learn the Bernoulli’s trials.
Poisson distribution is useful when the number of trials tends to infinity. Here we can also find the probability of r success in n trials using Poisson distribution when n is tending to infinity. We have explained this concept with an example through the video lecture given below. Watch it and learn about Poisson distribution.
The normal distribution is the continuous and symmetric distribution. We can find the probability by finding the area under the normal curve. The total area under the normal curve is equal to 1 square unit because the total probability is 1. In normal distribution, mean, median, and mode coincide because it is symmetrical distribution. Watch the video lecture given below to grab the concepts of normal distribution.
That is all for this post. We have included at least one example in each video tutorial of advanced engineering maths to make the topic more clear. If you still have any doubts or queries, comment below or contact us. We are always ready to solve your queries. If you like our work, leave your positive feedback to motivate us. Thanks!.