Data Science different topic's explanation -- Part-6 -- Marginal Probability and Conditional Probability

[anish-arya]

This is data science different topic explanation conversation series.
If you are not following below mentioned are the links for each part.

Part-1:

Data Science different explanation -- An introduction

Data Science different explanation – An introduction [image] There are a lot of engineers who have never been involved in the field of statistics or Data Science. But in order to build data pipelines or rewrite produced code by Data Scientists to an adequate, easily maintained code many nuances and misunderstandings arise on the engineering side. For those Data/ML engineers and novice Data Scientists, I’ve made this series of posts. I will try to explain some basic approaches in plain eng…

Part-2:

Data Science different topic explanation -- Part-2 -- Events. Probabilities of events

If you missed the previous post, here’s the below mentioned link. Data Science different topic explanation – Part-2 – Events. Probabilities of events One of the basic concepts in statistics in an event. Events are simply results of experiments. Events can be certain impossible, or random. A certain event is an event that as a result of an experiment (the execution of certain actions with a certain set of conditions) will occur in 100% cases. For instance, tossed coin will certainly fall (on…

Part-3:

Data Science different topic explanation -- Part-3 -- Event Types

Continue above post. Example: What are the chances of getting heads 3 times in a row? Let’s define possible outcomes of 3 coin tosses (H-Heads, T-tails): HHH, HHT, HTH, THH, TTH HTH, HTT, TTT Number of ways it can happen: 1 Total number of outcomes: 8 So, the answer is 1/8. However, we know that the results of a coin toss are independent and we can multiply them to get the total probability: P(3x heads) = P(heads)P(heads)P(heads) = 1/2 * 1/2 * 1/2 = 1/8 On the other hand, knowing that the…

Part-4:

Data Science different topic explanation -- Part-4 -- Disjoint and overlapping events

This is data science different topic explanation conversation series. If you are not following below mentioned are the links for each part. Part-1: Part-2: Part-3: Data Science different topic explanation – Part-4 – Disjoint and overlapping events Disjoint events cannot happen at the same time. A synonym for this term is “mutually exclusive”. For instance, the outcome of a single coin toss cannot be head and a tail, it can be either head or tails. The not disjoint event can happend a…

Part-5:

Data Science different topic's explanation -- Part-5 -- Types of Probabilities

This is data science different topic explanation conversation series. If you are not following below mentioned are the links for each part. Part-1: Part-2: Part-3: Part-4: Data Science different topic’s explanation – Part-5 – Types of Probabilities Joint Probability Joint probability is a type of probability where more than one event can occur simultaneously. The joint probability is the probability that event A will occur at the same time as event B. For instance, from a deck o…

Data Science different topic’s explanation – Part-6 – Marginal Probability and Conditional Probability

Marginal Probability

Marginal Probability – a probability of any single event occurring unconditioned on any other events.

Whenever someone asks you whether the weather is going to be rainy or sunny today (without any conditional or prior information), you are computing a marginal probability.

Conditional Probability

Conditional probability – is a probability of an event given that (by assumption, presumption, assertion, or evidence another event has occurred.

When i ask you what is the probability that today will be rainy or sunny given that i notices the temperature is going to be above 80 F, you are computing a conditional probability.

So, we want to understand the probability of event B given A. It is defined as the probability of the joint of events A and B divided by the probability of B. Thus,

[anish-arya]

Continuing the above post, what ever i missed out.

Example: A math teacher gave her class two tests. 25% of the class passed both tests and 42% of the class passed the first test. What is the probability of those who passed the first test also passed the second test?

Next Post: Probability Distributions