Sampling
Introduction:
Sampling is a method that allows us to get information about the population based on the statistics from a subset of the population (sample), without having to investigate every individual. Data sampling refers to statistical methods for selecting observations from the domain with the objective of estimating a population parameter.
The below diagram perfectly illustrates what sampling is.
This is an important part of Artificial Intelligence course and this lab report is done by myself under the supervision
of Nuruzzaman Faruqui, lecturer of City University, Bangladesh. From this course we get to explore the real
applicable approaches through AI and also acquires better
knowledge of the functionality of AI and how AI is making our daily life
easier. That's why this is the best Artificial Intelligence course in Bangladesh.
Problem Statement:
Sampling is one technique of approximate inference. In sampling, each variable is sampled for a value according to its probability distribution.
Here we discuss an example with sampling the Rain variable, the value none will be generated with probability of 0.7, the value light will be generated with probability of 0.2, and the value heavy will be generated with probability of 0.1. If you want to know about sampling, then definitely you want to know more about uncertainty. From this, we will find out an approximate probability for P(Train = on time)We can also solve the involve conditional probability, such as P(Rain = light | Train = on time).
Code Commentary:
We implement the problem in Python Language.
Result:
Conclusion:
In this lab work, we learned, what is sampling, how can get the inference approximate. Sampling is an active process of gathering observations intent on estimating from any group of variable. In this report, we learned about the concept of sampling, steps involved in sampling. Sampling has wide applications in the statistical world as well as the real world.
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