Probability and Statistics in Computing

Question 1

are fundamental in understanding uncertainty,
randomness, and data analysis in computing.

Answer 1

Probability and statistics

Question 2

Probability theory is the study of random events
and the likelihood of various outcomes.

Answer 2

Probability

Question 3

Statistics focuses on data collection, analysis,
interpretation, and presentation.

Answer 3

Statistics

Question 4

In computing, we often encounter uncertainty, especially in areas like network
behavior, machine learning models, cryptography, and algorithmic performance.

Answer 4

Modeling Uncertainty in Systems

Question 5

These are variables that take on different values based on random events.

Answer 5

Random Variables

Question 6

Distributions like normal, binomial, and Poisson describe the likelihood of different outcomes of random variables.

Answer 6

Probability Distributions

Question 7

A statistical method that updates
the probability of a hypothesis as more evidence or
information becomes available.

Answer 7

Bayesian Statistics

Question 8

Some algorithms use randomness to make decisions, which can lead to more
efficient solutions in some cases.

Answer 8

Randomized Algorithms

Question 9

Probability and statistics are at the core of machine learning. Algorithms like Naive
Bayes, Hidden Markov Models, and Bayesian Networks rely heavily on probabilistic
reasoning to make predictions and decisions.

Answer 9

Machine Learning and Data Analysis

Question 10

are a class of algorithms that use repeated random sampling
to estimate the probability of different outcomes.

Answer 10

Monte Carlo Methods