Discrete probability - PowerPoint PPT Presentation

Today's lecture in CSE 312 Spring 24 covers the basics of probability, including sample spaces, events, and probability calculations. Understand the foundational processes behind quantifying uncertainty, such as flipping coins, rolling dice, and shuffling cards. Dive into concepts like sample spaces

2 views • 26 slides

Conditional probability relates the likelihood of an event to the occurrence of another event. Theorems such as the Multiplication Theorem and Bayes Theorem provide a framework to calculate probabilities based on prior information. Conditional probability is used to analyze scenarios like the relati

1 views • 5 slides

Explore the concepts of independent and dependent events in probability, learn how to determine the probability of independent events using examples, and find out the difference between the two types of events through clear explanations and illustrations. Enhance your understanding of conditional pr

0 views • 39 slides

Probability rules and models explain how to calculate the likelihood of different outcomes in a chance process by utilizing sample spaces, probability models, events, and basic rules of probability. Learn about the importance of sample space, probability models, calculating probabilities, mutually e

0 views • 17 slides

Explore concepts of probability in various events like rolling a die, compound events, simple events, and spinner probability. Learn how to calculate probabilities of different outcomes and understand the difference between single and compound events. Discover key principles in probability theory an

0 views • 13 slides

Explore the concept of probability by learning about simple events, outcomes, and calculating probabilities using favorable outcomes. Discover how to express probability as fractions, decimals, or percentages through real-world examples like coin flips and dice rolls. Enhance your understanding of c

1 views • 13 slides

Conditional probability explores the likelihood of event A given event B, while Bayes Theorem provides a method to update the probability estimate of an event based on new information. Statistical concepts such as the multiplication rule, statistical independence, and the law of total probability ar

0 views • 15 slides

Conditional probability and independence are essential concepts in statistics. This lesson covers how to find and interpret conditional probabilities using two-way tables, calculate probabilities using the conditional probability formula, and determine the independence of events. Through examples li

0 views • 15 slides

Joint probability distributions are crucial in analyzing the simultaneous behavior of random variables. They can be described using mass functions for discrete variables and density functions for continuous variables. This concept is fundamental in probability and statistics, aiding in calculating p

1 views • 30 slides

Explore the concepts of randomness, probability, and simulation in this informative lesson. Learn how to interpret probability as a long-run relative frequency, dispel common myths about randomness, and use simulation to model chance behavior. Delve into the idea that chance behavior is unpredictabl

1 views • 13 slides

Probability is a number between zero and one that indicates the likelihood of an event occurring due to chance factors alone. This content covers the concept of probability, the calculation of probabilities using z-scores, and practical examples related to probability in statistics. You will learn a

0 views • 12 slides

Discover the essentials of probability with lessons on the probability scale, including expressing probabilities in words and numbers using a fair dice example. Explore how to calculate both the probability of rolling a 6 and not rolling a 6. Sharpen your skills with quizzes on Hegarty Maths.

0 views • 19 slides

Discrete Optimization is a field of applied mathematics that uses techniques from combinatorics, graph theory, linear programming, and algorithms to solve optimization problems over discrete structures. This involves creating mathematical models, defining objective functions, decision variables, and

0 views • 12 slides

In epidemiological analyses, inverse probability weights play a crucial role in addressing issues such as sampling, confounding, missingness, and censoring. By reshaping the data through up-weighting or down-weighting observations based on probabilities, biases can be mitigated effectively. Differen

0 views • 25 slides

Probability theory is a branch of mathematics that deals with the likelihood of different outcomes in random phenomena. It involves concepts such as sample space, probability distributions, and random variables to determine the chance of events occurring. The theory utilizes theoretical and experime

0 views • 11 slides

Understand the concepts of random variables, probability distributions, and cumulative distribution functions in the context of machine learning. Explore examples of discrete and continuous random variables, probability mass functions, and practice problems to enhance your understanding.

0 views • 27 slides

Probability is the measure of the likelihood of an event happening, with experimental and theoretical probability being key concepts. Experimental probability involves determining probabilities through experience or experiments, while theoretical probability can be calculated without prior experienc

2 views • 23 slides

Explore probability concepts in functional maths, such as understanding probability scales, comparing likelihood of events, calculating probabilities of simple and combined events, and expressing probabilities as fractions, decimals, and percentages. Practice drawing probability lines, simplifying f

0 views • 8 slides

This article delves into the use of probability in psychological research, covering key concepts such as random variables, probability functions, and distribution functions. It explains fundamental ideas like random experiments, sample spaces, types of sample spaces, events, and the formal approach

1 views • 14 slides

Explore the definition of random variables, probability distributions, and three types of discrete distributions - Binomial, Hypergeometric, and Poisson. Learn about the mean, variance, and standard deviation of probability distributions, as well as the difference between discrete and continuous dis

3 views • 32 slides

Probability is the likelihood of an event occurring, with theoretical probability based on all possible outcomes and experimental probability based on results. Events can be independent or dependent, impacting subsequent outcomes. Explore vocabulary, scenarios like rock-paper-scissors, and coin flip

0 views • 15 slides

Sampling in social research involves selecting a portion of a population to draw conclusions about the entire group. It helps save time, money, and allows for accurate measurements. The key principles of sampling include systematic selection, clear definition of sample units, independence of units,

0 views • 19 slides

Exploring the fundamental principles of probability through Kolmogorov Axioms, this content delves into the rules that govern probabilities of events, such as non-negativity, total probability, and the addition rule. Handy consequences like the probability of complements, unions, and intersections a

1 views • 8 slides

The composition of the 108th Congress includes 51 Republicans, 48 Democrats, and 1 Independent. A committee on aid to higher education is formed with 3 Senators chosen at random to head the committee. The probability of selecting all Republicans, all Democrats, and a mix of one Democrat, one Republi

0 views • 34 slides

This online course on Probability and Statistics for Data Science covers essential topics such as Probability theory, Statistical inference, Regression analysis, and more. The course emphasizes the application of statistical techniques in data analysis and provides a solid foundation in Probability

0 views • 37 slides

Explore the integration of TI-83/84 and TI-Nspire in supporting teaching and learning in Units 3.5 of the AP Statistics course, covering collecting data, probability, random variables, probability distributions, and sampling distributions. Dive into a real-world example involving the fit of lids on

0 views • 13 slides

Understanding the basic rules and principles of probability in business analytics, including conditional probability and Bayes Rule. Learn how to solve problems involving uncertainty by decomposition or simulation. Explore how beliefs can be updated using Bayes Rule with practical scenarios like ide

0 views • 13 slides

Exploring the significance of probability in game development, the content discusses basic probability concepts, examples of probabilities needed for game development, and specific scenarios like the likelihood of successful attacks, rare loot drops, enemy spawns, and time taken for actions. The art

0 views • 72 slides

Combinatorics, a key facet of discrete mathematics, explores the arrangement of objects and finds applications in various fields like discrete probability and algorithm analysis. The Rule of Sum, a fundamental principle, dictates how tasks can be accomplished when they cannot be done simultaneously.

0 views • 70 slides

Discussion on the concept of complex probability in solving real-world problems, particularly focusing on the transition probability matrix of discrete Markov chains. The paper introduces a measure more general than conventional probability, leading to the idea of complex probability. Various exampl

1 views • 10 slides

Explore the fundamental concepts of probability and statistics in computational network biology with a focus on sample spaces, random variables, probability distributions, and notation. Gain insights into the intuitive definition of probability, sample spaces for various experiments, different types

0 views • 39 slides

Explore the concept of random variables, differentiate between discrete and continuous variables, understand probability distributions, and calculate probabilities for events using properties of random variables. Dive into examples and probability histograms to grasp key principles.

0 views • 39 slides

Explore the fundamental concepts of probability including basic probability, conditional probability, Bayes Theorem, independence, sample space, events, counting, and the definition of probability. Learn about the significance of sample space, event subsets, and how probability laws encode knowledge

0 views • 31 slides

Probability density functions (PDFs) are introduced for continuous random variables to represent the likelihood of events in a continuous space. Unlike discrete probability mass functions, PDFs operate with integration instead of summation, ensuring total probability is 1. Consistency and differenti

1 views • 32 slides

Explore the concepts of independence in random variables, shifting variances, and facts about variance in the context of discrete random variables. Learn about key relationships such as Var(X + Y) = Var(X) + Var(Y) and discover common patterns in the Discrete Random Variable Zoo. Embrace the goal of

0 views • 27 slides

Understanding the basics of probability involves defining sample and event spaces, interpreting probability models, and applying these concepts to solve problems. By the end of the lecture, you will be able to identify sample and event spaces in probability questions and create meaningful probabilit

0 views • 18 slides

Discrete random variables play a crucial role in probability theory and statistics. This content explores three key types: Bernoulli random variable, binomial random variable, and error-correcting codes. From understanding the basics of Bernoulli trials to exploring the application of error correcti

0 views • 27 slides

A trial is a single event with possible outcomes, while a sequence of trials forms an experiment. For example, flipping a coin results in outcomes like heads, tails, or on edge, creating a sample space. This concept delves into the essence of discrete probability and how it applies to various experi

0 views • 63 slides

This content covers a lecture on a randomized algorithm for finding an approximate median element using elementary probability theory. It discusses the importance of insight and basic probability in designing and analyzing such algorithms. The lecture presents a simple probability exercise involving

0 views • 25 slides

Probability is a fundamental concept in statistics that measures the likelihood of specific events occurring. This summary delves into classical probability, relative frequency concepts, the Law of Large Numbers, and Bayesian or subjective probability. It covers key properties of probability, approa

0 views • 30 slides