The definition of probability of an event is explored here as a part of the axiomatic approach to probability. We also take a look at probability of equally likely events occurring.
Lam's Theorem states that the number of steps (n) required to compute the greatest common divisor (GCD) of two numbers (x and y) using Euclid's algorithm is bounded above by the logarithm of x/φ, where φ is the golden ratio. The maximum value of n occurs when x and y are consecutive Fibonacci numbers. The theorem draws on over 5000 years of mathematical history, from the ancient Egyptians' use of the golden ratio to Euclid's algorithm from 300 BC to Gabriel Lamé's theorem in 1845.
What does it mean for an event to have occurred? This slide builds on the previous slide deck on event and explains the above question with an example.
The document discusses key concepts in probability such as random experiments, outcomes, sample spaces, events, and the axiomatic approach to probability. It provides examples of random experiments like tossing a coin or rolling a die. An outcome is a possible result of an experiment, and a sample space is the set of all possible outcomes. Events can be simple, compound, impossible, or sure depending on the number of outcomes they include. The document also discusses mutually exclusive and exhaustive events and how probability can be defined through axioms about events and their probabilities.
The document discusses key concepts in probability such as random experiments, outcomes, sample spaces, events, types of events including impossible, sure, simple, and compound events. It also covers algebra of events including unions, intersections, complements and mutually exclusive events. The document defines mutually exclusive and exhaustive events. Finally, it introduces the axiomatic approach to defining probability as a function that satisfies three axioms.
This document discusses types of probability and provides definitions and examples of key probability concepts. It begins with an introduction to probability theory and its applications. The document then defines terms like random experiments, sample spaces, events, favorable events, mutually exclusive events, and independent events. It describes three approaches to measuring probability: classical, frequency, and axiomatic. It concludes with theorems of probability and references.
This document provides an introduction to probability theory, including key concepts such as:
- The foundational definitions of probability put forth by Pascal and Fermat.
- Key terms like sample space, trial, random experiment, and classical definition of probability.
- Important probability rules including addition rule, mutually exclusive events, complements of events, conditional probability, and multiplication theorem.
Lam's Theorem states that the number of steps (n) required to compute the greatest common divisor (GCD) of two numbers (x and y) using Euclid's algorithm is bounded above by the logarithm of x/φ, where φ is the golden ratio. The maximum value of n occurs when x and y are consecutive Fibonacci numbers. The theorem draws on over 5000 years of mathematical history, from the ancient Egyptians' use of the golden ratio to Euclid's algorithm from 300 BC to Gabriel Lamé's theorem in 1845.
What does it mean for an event to have occurred? This slide builds on the previous slide deck on event and explains the above question with an example.
The document discusses key concepts in probability such as random experiments, outcomes, sample spaces, events, and the axiomatic approach to probability. It provides examples of random experiments like tossing a coin or rolling a die. An outcome is a possible result of an experiment, and a sample space is the set of all possible outcomes. Events can be simple, compound, impossible, or sure depending on the number of outcomes they include. The document also discusses mutually exclusive and exhaustive events and how probability can be defined through axioms about events and their probabilities.
The document discusses key concepts in probability such as random experiments, outcomes, sample spaces, events, types of events including impossible, sure, simple, and compound events. It also covers algebra of events including unions, intersections, complements and mutually exclusive events. The document defines mutually exclusive and exhaustive events. Finally, it introduces the axiomatic approach to defining probability as a function that satisfies three axioms.
This document discusses types of probability and provides definitions and examples of key probability concepts. It begins with an introduction to probability theory and its applications. The document then defines terms like random experiments, sample spaces, events, favorable events, mutually exclusive events, and independent events. It describes three approaches to measuring probability: classical, frequency, and axiomatic. It concludes with theorems of probability and references.
This document provides an introduction to probability theory, including key concepts such as:
- The foundational definitions of probability put forth by Pascal and Fermat.
- Key terms like sample space, trial, random experiment, and classical definition of probability.
- Important probability rules including addition rule, mutually exclusive events, complements of events, conditional probability, and multiplication theorem.
The document provides guidance on teaching a lesson on probability. It outlines key concepts to define, such as experimental and theoretical probabilities. It also describes solving problems involving combined probabilities of mutually exclusive and independent events. Example problems are provided for different probability concepts. The teacher is instructed to check students' understanding through oral questions and exercises.
AI 8 | Probability Basics, Bayes' Rule, Probability DistributionMohammad Imam Hossain
1. Uncertainty and Decision Theory
2. Basic Prob. Theory
3. Prior and posterior probabilities
4. Bayes' Rule
5. Random variable
6. Different types of probability distribution
This document introduces key concepts in probability, including:
- A sample space is the set of all possible outcomes of an experiment. It can be discrete (a finite or countable set of outcomes) or continuous (containing an interval of real numbers).
- An event is a subset of the sample space consisting of possible outcomes. The complement of an event contains outcomes not in the event.
- Probability is defined as the number of favorable cases divided by the total number of possible cases. It quantifies the likelihood an event will occur.
- Experiments can be done with or without replacement of items, and cases can be equally likely, mutually exclusive, or exhaustive.
This document introduces key concepts in probability:
- Probability is the likelihood of an event occurring, which can be measured numerically or described qualitatively.
- Events can be classified as exhaustive, favorable, mutually exclusive, equally likely, complementary, and independent.
- There are three approaches to defining probability: classical, frequency, and axiomatic. The classical approach defines probability as the number of favorable outcomes over the total number of possible outcomes. The frequency approach defines probability as the limit of the ratio of favorable outcomes to the total number of trials. The axiomatic approach defines probability based on axioms or statements assumed to be true.
- Key properties of probability include that the probability of an event is between 0
This document introduces key concepts in probability:
1. Probability is the likelihood of an event occurring, which can be expressed as a number or words like "impossible" or "likely".
2. Events can be classified as exhaustive, favorable, mutually exclusive, equally likely, complementary, and independent.
3. There are three approaches to defining probability: classical, frequency, and axiomatic. The classical approach defines probability as the number of favorable outcomes over the total number of possible outcomes. The frequency approach defines it as the limit of favorable outcomes over total trials. The axiomatic approach uses axioms like probabilities being between 0 and 1.
4. Several properties of probability are described, like the sum
06 Probability Simple and Compound EventsDhruvSethi28
Simple and compound events are defined and explored with the help of an example of two coin tossing examples. These two types of events are fundamental to the understanding of probability theory
Probability is a branch of mathematics that studies patterns of chance. It is used to quantify the likelihood of events occurring in experiments or other situations involving uncertainty. The probability of an event is expressed as a number between 0 and 1, with 0 indicating impossibility and 1 indicating certainty. Key concepts in probability include theoretical and experimental probability, sample spaces, events, mutually exclusive and exhaustive events, and rules like addition rules for calculating combined probabilities. Probability is applied in many fields including statistics, gambling, science, and machine learning.
This document discusses key concepts and terms related to probability. It begins by defining probability and explaining how it is used in various fields like science, commerce, and weather forecasting. Some key terms are then defined, such as outcome, event, experiment, trial, elementary event, and sample space. The document outlines two types of probability - experimental probability, which is based on empirical results from repeated experiments, and theoretical probability, which assumes all outcomes are equally likely. It provides examples of common experiments like coin tosses, die rolls, and card draws and lists their possible outcomes. Finally, it discusses the range of probability from 0 to 1 and types of events like sure, impossible, and complementary events.
This document provides an introduction to probability. It defines probability as a measure of how likely an event is to occur. Probability is expressed as a ratio of favorable outcomes to total possible outcomes. The key terms used in probability are defined, including event, outcome, sample space, and elementary events. The theoretical approach to probability is discussed, where probability is predicted without performing the experiment. Random experiments are described as those that may not produce the same outcome each time. Laws of probability are presented, such as a probability being between 0 and 1. Applications of probability in everyday life are mentioned, such as reliability testing of products. Two example probability problems are worked out.
The document provides an introduction to probability. It defines probability as a measure of how likely an event is. Probability is expressed as the ratio of favorable outcomes to total possible outcomes. The concept of probability originated in the 16th century and has been developed by many mathematicians. Today, probability theory has applications in fields like science, economics, and engineering. The document also defines key probability terms like events, outcomes, sample space, and theoretical probability. It provides examples of calculating probability for experiments like tossing coins, rolling dice, and drawing cards.
Probability theory or probability calculus is the branch of mathematics concerned with probability. Although there are several different probability interpretations, probability theory treats the concept in a rigorous mathematical manner by expressing it through a set of axioms. Typically these axioms formalise probability in terms of a probability space, which assigns a measure taking values between 0 and 1, termed the probability measure, to a set of outcomes called the sample space. Any specified subset of the sample space is called an event.
This presentation is about the topic PROBABILITY. Details of this topic, starting from basic level and slowly moving towards advanced level , has been discussed in this presentation.
This document introduces the principles of mathematical induction. It explains that induction can be used to prove statements for all natural numbers if (1) the statement is true for n=1, and (2) if the statement is true for an integer k, then it is also true for k+1. The document provides an example to prove the formula for the sum of squares from 1 to n using induction. It shows that the formula is true for the base case of n=1, and assumes the formula is true for an integer k to prove it is also true for k+1.
08 probability mutually exclusive eventsDhruvSethi28
Here we explore mutually exclusive events starting with its definition and exploring the concept with an example. The example used is the rolling of a die
Here various operations which are available in set theory are performed on events. Here we can combine different events with a union, perform an intersection between different events, explore their complement etc
Here we explore definitions of the impossible event, the sure event, the simple event and the compound event. To understand these events deeper please look at subsequent slides
An event E is a subset of the sample space S. In these slides, I define an event and give examples of different types of events along with their corresponding subsets of S
How to Add Chatter in the odoo 17 ERP ModuleCeline George
In Odoo, the chatter is like a chat tool that helps you work together on records. You can leave notes and track things, making it easier to talk with your team and partners. Inside chatter, all communication history, activity, and changes will be displayed.
This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.
The document provides guidance on teaching a lesson on probability. It outlines key concepts to define, such as experimental and theoretical probabilities. It also describes solving problems involving combined probabilities of mutually exclusive and independent events. Example problems are provided for different probability concepts. The teacher is instructed to check students' understanding through oral questions and exercises.
AI 8 | Probability Basics, Bayes' Rule, Probability DistributionMohammad Imam Hossain
1. Uncertainty and Decision Theory
2. Basic Prob. Theory
3. Prior and posterior probabilities
4. Bayes' Rule
5. Random variable
6. Different types of probability distribution
This document introduces key concepts in probability, including:
- A sample space is the set of all possible outcomes of an experiment. It can be discrete (a finite or countable set of outcomes) or continuous (containing an interval of real numbers).
- An event is a subset of the sample space consisting of possible outcomes. The complement of an event contains outcomes not in the event.
- Probability is defined as the number of favorable cases divided by the total number of possible cases. It quantifies the likelihood an event will occur.
- Experiments can be done with or without replacement of items, and cases can be equally likely, mutually exclusive, or exhaustive.
This document introduces key concepts in probability:
- Probability is the likelihood of an event occurring, which can be measured numerically or described qualitatively.
- Events can be classified as exhaustive, favorable, mutually exclusive, equally likely, complementary, and independent.
- There are three approaches to defining probability: classical, frequency, and axiomatic. The classical approach defines probability as the number of favorable outcomes over the total number of possible outcomes. The frequency approach defines probability as the limit of the ratio of favorable outcomes to the total number of trials. The axiomatic approach defines probability based on axioms or statements assumed to be true.
- Key properties of probability include that the probability of an event is between 0
This document introduces key concepts in probability:
1. Probability is the likelihood of an event occurring, which can be expressed as a number or words like "impossible" or "likely".
2. Events can be classified as exhaustive, favorable, mutually exclusive, equally likely, complementary, and independent.
3. There are three approaches to defining probability: classical, frequency, and axiomatic. The classical approach defines probability as the number of favorable outcomes over the total number of possible outcomes. The frequency approach defines it as the limit of favorable outcomes over total trials. The axiomatic approach uses axioms like probabilities being between 0 and 1.
4. Several properties of probability are described, like the sum
06 Probability Simple and Compound EventsDhruvSethi28
Simple and compound events are defined and explored with the help of an example of two coin tossing examples. These two types of events are fundamental to the understanding of probability theory
Probability is a branch of mathematics that studies patterns of chance. It is used to quantify the likelihood of events occurring in experiments or other situations involving uncertainty. The probability of an event is expressed as a number between 0 and 1, with 0 indicating impossibility and 1 indicating certainty. Key concepts in probability include theoretical and experimental probability, sample spaces, events, mutually exclusive and exhaustive events, and rules like addition rules for calculating combined probabilities. Probability is applied in many fields including statistics, gambling, science, and machine learning.
This document discusses key concepts and terms related to probability. It begins by defining probability and explaining how it is used in various fields like science, commerce, and weather forecasting. Some key terms are then defined, such as outcome, event, experiment, trial, elementary event, and sample space. The document outlines two types of probability - experimental probability, which is based on empirical results from repeated experiments, and theoretical probability, which assumes all outcomes are equally likely. It provides examples of common experiments like coin tosses, die rolls, and card draws and lists their possible outcomes. Finally, it discusses the range of probability from 0 to 1 and types of events like sure, impossible, and complementary events.
This document provides an introduction to probability. It defines probability as a measure of how likely an event is to occur. Probability is expressed as a ratio of favorable outcomes to total possible outcomes. The key terms used in probability are defined, including event, outcome, sample space, and elementary events. The theoretical approach to probability is discussed, where probability is predicted without performing the experiment. Random experiments are described as those that may not produce the same outcome each time. Laws of probability are presented, such as a probability being between 0 and 1. Applications of probability in everyday life are mentioned, such as reliability testing of products. Two example probability problems are worked out.
The document provides an introduction to probability. It defines probability as a measure of how likely an event is. Probability is expressed as the ratio of favorable outcomes to total possible outcomes. The concept of probability originated in the 16th century and has been developed by many mathematicians. Today, probability theory has applications in fields like science, economics, and engineering. The document also defines key probability terms like events, outcomes, sample space, and theoretical probability. It provides examples of calculating probability for experiments like tossing coins, rolling dice, and drawing cards.
Probability theory or probability calculus is the branch of mathematics concerned with probability. Although there are several different probability interpretations, probability theory treats the concept in a rigorous mathematical manner by expressing it through a set of axioms. Typically these axioms formalise probability in terms of a probability space, which assigns a measure taking values between 0 and 1, termed the probability measure, to a set of outcomes called the sample space. Any specified subset of the sample space is called an event.
This presentation is about the topic PROBABILITY. Details of this topic, starting from basic level and slowly moving towards advanced level , has been discussed in this presentation.
This document introduces the principles of mathematical induction. It explains that induction can be used to prove statements for all natural numbers if (1) the statement is true for n=1, and (2) if the statement is true for an integer k, then it is also true for k+1. The document provides an example to prove the formula for the sum of squares from 1 to n using induction. It shows that the formula is true for the base case of n=1, and assumes the formula is true for an integer k to prove it is also true for k+1.
08 probability mutually exclusive eventsDhruvSethi28
Here we explore mutually exclusive events starting with its definition and exploring the concept with an example. The example used is the rolling of a die
Here various operations which are available in set theory are performed on events. Here we can combine different events with a union, perform an intersection between different events, explore their complement etc
Here we explore definitions of the impossible event, the sure event, the simple event and the compound event. To understand these events deeper please look at subsequent slides
An event E is a subset of the sample space S. In these slides, I define an event and give examples of different types of events along with their corresponding subsets of S
How to Add Chatter in the odoo 17 ERP ModuleCeline George
In Odoo, the chatter is like a chat tool that helps you work together on records. You can leave notes and track things, making it easier to talk with your team and partners. Inside chatter, all communication history, activity, and changes will be displayed.
This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.
Main Java[All of the Base Concepts}.docxadhitya5119
This is part 1 of my Java Learning Journey. This Contains Custom methods, classes, constructors, packages, multithreading , try- catch block, finally block and more.
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
Exploiting Artificial Intelligence for Empowering Researchers and Faculty,
International FDP on Fundamentals of Research in Social Sciences
at Integral University, Lucknow, 06.06.2024
By Dr. Vinod Kumar Kanvaria
The simplified electron and muon model, Oscillating Spacetime: The Foundation...RitikBhardwaj56
Discover the Simplified Electron and Muon Model: A New Wave-Based Approach to Understanding Particles delves into a groundbreaking theory that presents electrons and muons as rotating soliton waves within oscillating spacetime. Geared towards students, researchers, and science buffs, this book breaks down complex ideas into simple explanations. It covers topics such as electron waves, temporal dynamics, and the implications of this model on particle physics. With clear illustrations and easy-to-follow explanations, readers will gain a new outlook on the universe's fundamental nature.
Executive Directors Chat Leveraging AI for Diversity, Equity, and InclusionTechSoup
Let’s explore the intersection of technology and equity in the final session of our DEI series. Discover how AI tools, like ChatGPT, can be used to support and enhance your nonprofit's DEI initiatives. Participants will gain insights into practical AI applications and get tips for leveraging technology to advance their DEI goals.
This presentation includes basic of PCOS their pathology and treatment and also Ayurveda correlation of PCOS and Ayurvedic line of treatment mentioned in classics.
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Odoo 17 CRM allows us to track why we lose sales opportunities with "Lost Reasons." This helps analyze our sales process and identify areas for improvement. Here's how to configure lost reasons in Odoo 17 CRM
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UPRAHUL
This Dissertation explores the particular circumstances of Mirzapur, a region located in the
core of India. Mirzapur, with its varied terrains and abundant biodiversity, offers an optimal
environment for investigating the changes in vegetation cover dynamics. Our study utilizes
advanced technologies such as GIS (Geographic Information Systems) and Remote sensing to
analyze the transformations that have taken place over the course of a decade.
The complex relationship between human activities and the environment has been the focus
of extensive research and worry. As the global community grapples with swift urbanization,
population expansion, and economic progress, the effects on natural ecosystems are becoming
more evident. A crucial element of this impact is the alteration of vegetation cover, which plays a
significant role in maintaining the ecological equilibrium of our planet.Land serves as the foundation for all human activities and provides the necessary materials for
these activities. As the most crucial natural resource, its utilization by humans results in different
'Land uses,' which are determined by both human activities and the physical characteristics of the
land.
The utilization of land is impacted by human needs and environmental factors. In countries
like India, rapid population growth and the emphasis on extensive resource exploitation can lead
to significant land degradation, adversely affecting the region's land cover.
Therefore, human intervention has significantly influenced land use patterns over many
centuries, evolving its structure over time and space. In the present era, these changes have
accelerated due to factors such as agriculture and urbanization. Information regarding land use and
cover is essential for various planning and management tasks related to the Earth's surface,
providing crucial environmental data for scientific, resource management, policy purposes, and
diverse human activities.
Accurate understanding of land use and cover is imperative for the development planning
of any area. Consequently, a wide range of professionals, including earth system scientists, land
and water managers, and urban planners, are interested in obtaining data on land use and cover
changes, conversion trends, and other related patterns. The spatial dimensions of land use and
cover support policymakers and scientists in making well-informed decisions, as alterations in
these patterns indicate shifts in economic and social conditions. Monitoring such changes with the
help of Advanced technologies like Remote Sensing and Geographic Information Systems is
crucial for coordinated efforts across different administrative levels. Advanced technologies like
Remote Sensing and Geographic Information Systems
9
Changes in vegetation cover refer to variations in the distribution, composition, and overall
structure of plant communities across different temporal and spatial scales. These changes can
occur natural.
2. Probability of an Event
Let a sample space of an experiment be
S = {w1, w2, w3, …, wn}
Let all the outcomes be equally likely to occur i.e. the chance of
occurrence of each simple event must be the same.
Σ P(wi) = p for all wi ∈ S where 0 ≤ P(wi) ≤ 1
since Σ P(wi) = p + p +…(n times) … + p = 1
np = 1 i.e. p = 1/n
3. Probability of an Event
Also, let S be a sample space and E be an event, such that
n(S) = n and n(E)=m. If each outcome is equally likely, then
it follows that
P(E) = m/n = (Number of outcomes favourable to E)/(Total possible
outcomes)
4. Probability of an Event
• You can contact me for private or group tuitions from the details below
• By Dhruv Sethi: +91 9310805977/dhrsethi1@gmail.com
• Whatsapp: +91 8291687783