STATISTICS AND PROBABILITY DISTRIBUTIONS

IIT Bombay organized a session on “Statistics and Probability Distributions” on 24th February, 26th February and 02nd March 2016 attended by students from Computer Science Engineering Department, Dronacharya College of Engineering, Gurgaon.

The sessions were delivered by Prof. Sanjeev Sabnis from IIT Bombay discussing in details about Probability Distribution.

In the First session Dr. Sabnis shared his views on Sample Spaces and Event, Axioms of Probability and Permutations and Combinations. Permutations means each of several possible ways in which a set or number of things can be ordered or arranged , on the other hand s combinations is a way of selecting items from a collection such that (unlike permutations) the order of selection does not matter. He explained about law of Total Probability, Bayes Theorem, Dependence & Independence of events, Properties of CDF and PDF. PDF is probability density function (PDF - is the derivative of the cumulative density function (CDF). PDF is a function of a random variable, x, and its magnitude will be some indication of the relative likelihood of measuring a particular value. The practical examples given by Dr. Sabnis helped students to understand the subject matter and cleared the doubt through Question - Answer session.

In the Second session, Dr. Sabnis explained all the key aspects of Probability Distributions for continuous variable. Probability distribution is a table or an equation that links each outcome of a statistical experiment with its probability of occurrence. He also discussed in details about Cumulative distribution Function, Variance, Continuous Uniform Distribution, Normal Distribution and Non Standard Normal Distribution. Cumulative distribution Function is a function whose value is the probability that a corresponding continuous random variable has a value less than or equal to the argument of the function.

In the Third session the professor deeply focused on Binomial Distribution, the Gamma Function, the Weibull Distribution, the Chi Squared Distribution, Students t distribution and F distribution. The binomial distribution function specifies the number of times (x) that an event occurs in an independent trials where p is the probability of the event occurring in a single trial. It is an exact probability distribution for any number of discrete trials.

The whole session provided students good understanding of the subject matter as the students came to know about various concepts of Probability Theory and Distributions.

Some Glimpses :