Conditional statement and material implication
•Download as PPTX, PDF•
5 likes•3,729 views
Conditional statements relate two statements where the second statement is contingent on the first being true. They are represented by "if-then" phrases or a conditional arrow. The statement before the "if" is called the antecedent, and the statement after the "then" is called the consequent. Material implication describes the relationship between the antecedent and consequent, where a conditional statement is only false if the antecedent is true and consequent is false. There are different types of implications including logical, definitional, causal, and decisional.
Report
Share
Report
Share
Recommended
Probability Distributions
This presentation forms part of a free, online course on analytics
http://econ.anthonyjevans.com/courses/analytics/
Probability distribution
The document discusses various probability distributions including the binomial, Poisson, and normal distributions. It provides definitions and key properties of each distribution. It also discusses sampling with and without replacement as well as the Monte Carlo method for simulating physical systems using random sampling. The Monte Carlo method can be used to computationally estimate values like pi by simulating the throwing of darts at a circular target.
Bernoulli distribution
Bernoulli distribution under the complementary Statistics syllabus of University of Calicut in BSc core of Mathematics, Physics & Computer Science.
VECTOR ANALYSIS-1
Vectors are quantities that have both magnitude and direction. They can be represented by capital letters with an arrow or lowercase letters with a bar. A vector has components in different dimensions - for two dimensions it has x and y components, and for three dimensions it has x, y, and z components. Some key vector concepts are parallel vectors (same direction), equal vectors (same magnitude and direction), negative/opposite vectors, free vectors (originate from different points), and position vectors (originate from the same point). The dot product of two vectors is a scalar quantity that depends on the angle between the vectors, and can be used to determine properties like whether vectors are parallel or orthogonal.
Probability Concepts Applications
The document introduces basic probability concepts and provides examples to illustrate them. It discusses the key properties of probability, types of probability (objective and subjective), mutually exclusive and collectively exhaustive events, and probabilities of independent and dependent events. It also explains Bayes' theorem and how it can be used to update probabilities as new information becomes available.
Linear Combination, Span And Linearly Independent, Dependent Set
In this presentation, the topic of Linear Combination, Span and Linearly Independent and Linearly Dependent Sets have been discussed. The sums for each topic have been given to understand the concept clearly for viewers.
Conditional probability
This document discusses conditional probability and independence. It defines conditional probability as P(B|A), the probability of event B given that event A has occurred. To calculate this, one considers only the outcomes where A occurred and calculates the fraction where B also occurred. Two events A and B are independent if P(B|A) = P(B), meaning the probability of B is unaffected by the occurrence of A. The general multiplication rule for any events A and B is P(A and B) = P(A) × P(B|A) or P(A) × P(B|A). Disjoint events cannot be independent as the occurrence of one rules out the other. Conditional probabilities are best understood
Logic&proof
This document summarizes Chapter 1 of the textbook "Discrete Mathematics" by R. Johnsonbaugh. It covers the topics of logic, proofs, and propositional logic. Key points include:
- Logic is the study of correct reasoning and is used in mathematics and computer science.
- A proposition is a statement that can be determined as true or false. Connectives like AND, OR, and NOT can combine propositions.
- Truth tables define the truth values of compound propositions formed from connectives.
- Quantifiers like "for all" and "there exists" are used to make universal and existential statements.
- A proof is a logical argument establishing the truth of a theorem using definitions, ax
Recommended
Probability Distributions
This presentation forms part of a free, online course on analytics
http://econ.anthonyjevans.com/courses/analytics/
Probability distribution
The document discusses various probability distributions including the binomial, Poisson, and normal distributions. It provides definitions and key properties of each distribution. It also discusses sampling with and without replacement as well as the Monte Carlo method for simulating physical systems using random sampling. The Monte Carlo method can be used to computationally estimate values like pi by simulating the throwing of darts at a circular target.
Bernoulli distribution
Bernoulli distribution under the complementary Statistics syllabus of University of Calicut in BSc core of Mathematics, Physics & Computer Science.
VECTOR ANALYSIS-1
Vectors are quantities that have both magnitude and direction. They can be represented by capital letters with an arrow or lowercase letters with a bar. A vector has components in different dimensions - for two dimensions it has x and y components, and for three dimensions it has x, y, and z components. Some key vector concepts are parallel vectors (same direction), equal vectors (same magnitude and direction), negative/opposite vectors, free vectors (originate from different points), and position vectors (originate from the same point). The dot product of two vectors is a scalar quantity that depends on the angle between the vectors, and can be used to determine properties like whether vectors are parallel or orthogonal.
Probability Concepts Applications
The document introduces basic probability concepts and provides examples to illustrate them. It discusses the key properties of probability, types of probability (objective and subjective), mutually exclusive and collectively exhaustive events, and probabilities of independent and dependent events. It also explains Bayes' theorem and how it can be used to update probabilities as new information becomes available.
Linear Combination, Span And Linearly Independent, Dependent Set
In this presentation, the topic of Linear Combination, Span and Linearly Independent and Linearly Dependent Sets have been discussed. The sums for each topic have been given to understand the concept clearly for viewers.
Conditional probability
This document discusses conditional probability and independence. It defines conditional probability as P(B|A), the probability of event B given that event A has occurred. To calculate this, one considers only the outcomes where A occurred and calculates the fraction where B also occurred. Two events A and B are independent if P(B|A) = P(B), meaning the probability of B is unaffected by the occurrence of A. The general multiplication rule for any events A and B is P(A and B) = P(A) × P(B|A) or P(A) × P(B|A). Disjoint events cannot be independent as the occurrence of one rules out the other. Conditional probabilities are best understood
Logic&proof
This document summarizes Chapter 1 of the textbook "Discrete Mathematics" by R. Johnsonbaugh. It covers the topics of logic, proofs, and propositional logic. Key points include:
- Logic is the study of correct reasoning and is used in mathematics and computer science.
- A proposition is a statement that can be determined as true or false. Connectives like AND, OR, and NOT can combine propositions.
- Truth tables define the truth values of compound propositions formed from connectives.
- Quantifiers like "for all" and "there exists" are used to make universal and existential statements.
- A proof is a logical argument establishing the truth of a theorem using definitions, ax
Proofs by contraposition
The document discusses proofs by contraposition. It explains that a statement of the form "if p then q" can be proven by showing its contrapositive "if not q then not p" is true. It provides examples of proofs using this method, including proving if n^2 is even then n is even, if m + n is even then m and n have the same parity, and if 3n + 2 is odd then n is odd. Homework exercises are provided applying this proof technique.
Probability
The document discusses probability and set theory. It defines probability as a quantitative measure of uncertainty or a measure of degree of belief in a statement. It states that probability is measured on a scale from 0 to 1, where 0 is impossibility and 1 is certainty. It then discusses key concepts in set theory such as sets, subsets, Venn diagrams, and operations on sets like union, intersection, difference, and complement. Finally, it discusses definitions of probability including the classical, relative frequency, and axiomatic definitions.
Geometric Distribution
The document discusses the geometric distribution, a discrete probability distribution that models the number of Bernoulli trials needed to get one success. It defines the geometric distribution and gives its probability mass function. Some key properties and applications are discussed, including: the mean is 1/p, the variance is q/p^2, where q is 1-p. It is used in situations like modeling the probability of events occurring after repeated independent trials with a constant probability of success each trial. Examples given include analyzing success rates in sports and deciding when to stop research trials.
Error propagation
The document discusses error propagation in physics measurements and calculations. It provides formulas for calculating the uncertainty (error) when adding, subtracting, multiplying and dividing measurements. The key points are:
1) When adding or subtracting measurements, the total uncertainty is the square root of the sum of the individual measurement uncertainties squared.
2) When multiplying or dividing measurements, the total uncertainty is calculated by adding the individual relative uncertainties squared.
3) For measurements raised to a power, the relative uncertainty is equal to the relative uncertainty of the measurement itself multiplied by the power.
Introduction to Generalized Linear Models
The document discusses generalized linear models (GLMs) and provides examples of logistic regression and Poisson regression. Some key points covered include:
- GLMs allow for non-normal distributions of the response variable and non-constant variance, which makes them useful for binary, count, and other types of data.
- The document outlines the framework for GLMs, including the link function that transforms the mean to the scale of the linear predictor and the inverse link that transforms it back.
- Logistic regression is presented as a GLM example for binary data with a logit link function. Poisson regression is given for count data with a log link.
- Examples are provided to demonstrate how to fit and interpret a logistic
Thevenin and Norton's theorem
The document discusses Thevenin's theorem for circuit analysis. It provides examples of using the theorem to find the equivalent Thevenin circuit for more complex networks. The key steps are:
1) Find the open-circuit voltage VTH by removing loads and measuring voltage
2) Find the Thevenin resistance RTH by deactivating sources and measuring resistance
3) The Thevenin circuit is then VTH in series with RTH and can be used to analyze any load connected between the original terminals. Examples show applying this to circuits with resistors, voltage sources, current sources, and dependent sources.
Probability
Probability not Higher Level....This PPT is for beginners or for those who learnt or to study PROBABILITY........... :):):):):)
Markov Chains
This document provides a summary of Markov chains. It begins by defining stochastic processes and Markov chains. A Markov chain is a stochastic process where the probability of the next state depends only on the current state, not on the sequence of events that preceded it. The document discusses n-step transition probabilities, classification of states, and steady-state probabilities. It provides examples of Markov chains for cola purchases and camera store inventory to illustrate the concepts.
Geometric probability distribution
1. A geometric experiment involves independent trials with two possible outcomes (success/failure), where the probability of success (p) is constant across trials.
2. The geometric random variable (X) represents the number of trials until the first success. It has a geometric distribution where P(X=x) = p(1-p)^(x-1).
3. Examples are provided to illustrate calculating the probability of success on a given trial (x), as well as the mean and standard deviation, for geometric distributions with different probabilities of success (p).
Probability
Christiaan Huygens was a Dutch scientist known for his work in physics and mathematics. The document discusses probability theory, beginning with an introduction to probability as a measure between 0 and 1 of the likelihood of an event occurring. It discusses applications of probability theory in risk assessment and financial markets. Experiments are described as having possible outcomes that can be counted using rules for combinations and permutations to assign probabilities.
Covariance
A walk-through of the mathematics of covariance, the covariance matrix, and use cases when combined with k-means clustering. Focus on how to actually use the math, and shows how the equations turn into simple JavaScript code.
Statistics: Probability
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
Conditional Probability
Conditional probability is the probability of an event occurring given that another event has occurred. It is calculated as the probability of both events occurring divided by the probability of the first event. An example is given of calculating the probability of drawing two white balls in succession from an urn without replacement. The formula for conditional probability is derived as the probability of events A and B occurring divided by the probability of A. This is demonstrated using an example of finding the percentage of friends who like chocolate that also like strawberry.
Hypothesis presentation
This document defines and discusses hypotheses. It begins by defining a hypothesis as an assumption or educated guess that is intended to be tested. It then provides definitions of hypotheses from several scholars and discusses their key characteristics. Hypotheses are categorized as null or alternative, directional or non-directional, and deductive or inductive. The document outlines guidelines for forming a hypothesis and testing it, including relating it to the research problem, making it clear and testable, and falsifiable. In summary, this document provides an overview of what a hypothesis is and best practices for developing and testing one.
CMSC 56 | Lecture 1: Propositional Logic
Propositional Logic
CMSC 56 | Discrete Mathematical Structure for Computer Science
August 17, 2018
Instructor: Allyn Joy D. Calcaben
College of Arts & Sciences
University of the Philippines Visayas
Binomial Distribution Part 5
Binomial Distribution Part 5 deals with fitting & familiaring some concepts of B D under the complementary Statistics syllabus of University of Calicut in BSc core of Mathematics, Physics & Computer Science.
PROBABILITY AND IT'S TYPES WITH RULES
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.
Applications of differential equations by shahzad
This document summarizes applications of differential equations to real world systems including cooling/warming, population growth, radioactive decay, electrical circuits, survivability with AIDS, economics, drug distribution in the human body, and a pursuit problem. Examples are provided for each application to illustrate solutions to related differential equations. Key concepts covered include Newton's law of cooling, population models, carbon dating, series circuits, survival models, supply and demand models, compound interest, drug concentration in the body over time, and a mathematical model for a dog chasing a rabbit.
Negative Binomial Distribution
Negative Binomial Distribution introduction & over view under the complementary Statistics syllabus of University of Calicut in BSc core of Mathematics, Physics & Computer Science.
Moderation and Mediation | Dissertation Webinar
In this webinar, we discuss the basics of moderating effects and mediating effects. We explain the differences between moderation and mediation, when moderation and mediation are appropriate to conduct, and the statistical analyses used to test moderation and mediation.
Chapter1p1 2.pptx
This document summarizes Section 1.1 of a chapter on propositional logic. It introduces key concepts in propositional logic including propositions, connectives like negation, conjunction, disjunction, implication, biconditional, and truth tables. It provides examples and explanations of how to construct and interpret compound propositions using these logical connectives.
Drinkfromme.pptx
The document discusses propositional logic, including:
- The basic components of propositional logic like propositions, connectives, truth tables, and logical equivalences
- Applications such as translating English sentences to propositional logic, system specifications, logic puzzles
- Representing logical relationships using truth tables and showing logical equivalences
- Using propositional logic to represent an electrical system and diagnose faults
More Related Content
What's hot
Proofs by contraposition
The document discusses proofs by contraposition. It explains that a statement of the form "if p then q" can be proven by showing its contrapositive "if not q then not p" is true. It provides examples of proofs using this method, including proving if n^2 is even then n is even, if m + n is even then m and n have the same parity, and if 3n + 2 is odd then n is odd. Homework exercises are provided applying this proof technique.
Probability
The document discusses probability and set theory. It defines probability as a quantitative measure of uncertainty or a measure of degree of belief in a statement. It states that probability is measured on a scale from 0 to 1, where 0 is impossibility and 1 is certainty. It then discusses key concepts in set theory such as sets, subsets, Venn diagrams, and operations on sets like union, intersection, difference, and complement. Finally, it discusses definitions of probability including the classical, relative frequency, and axiomatic definitions.
Geometric Distribution
The document discusses the geometric distribution, a discrete probability distribution that models the number of Bernoulli trials needed to get one success. It defines the geometric distribution and gives its probability mass function. Some key properties and applications are discussed, including: the mean is 1/p, the variance is q/p^2, where q is 1-p. It is used in situations like modeling the probability of events occurring after repeated independent trials with a constant probability of success each trial. Examples given include analyzing success rates in sports and deciding when to stop research trials.
Error propagation
The document discusses error propagation in physics measurements and calculations. It provides formulas for calculating the uncertainty (error) when adding, subtracting, multiplying and dividing measurements. The key points are:
1) When adding or subtracting measurements, the total uncertainty is the square root of the sum of the individual measurement uncertainties squared.
2) When multiplying or dividing measurements, the total uncertainty is calculated by adding the individual relative uncertainties squared.
3) For measurements raised to a power, the relative uncertainty is equal to the relative uncertainty of the measurement itself multiplied by the power.
Introduction to Generalized Linear Models
The document discusses generalized linear models (GLMs) and provides examples of logistic regression and Poisson regression. Some key points covered include:
- GLMs allow for non-normal distributions of the response variable and non-constant variance, which makes them useful for binary, count, and other types of data.
- The document outlines the framework for GLMs, including the link function that transforms the mean to the scale of the linear predictor and the inverse link that transforms it back.
- Logistic regression is presented as a GLM example for binary data with a logit link function. Poisson regression is given for count data with a log link.
- Examples are provided to demonstrate how to fit and interpret a logistic
Thevenin and Norton's theorem
The document discusses Thevenin's theorem for circuit analysis. It provides examples of using the theorem to find the equivalent Thevenin circuit for more complex networks. The key steps are:
1) Find the open-circuit voltage VTH by removing loads and measuring voltage
2) Find the Thevenin resistance RTH by deactivating sources and measuring resistance
3) The Thevenin circuit is then VTH in series with RTH and can be used to analyze any load connected between the original terminals. Examples show applying this to circuits with resistors, voltage sources, current sources, and dependent sources.
Probability
Probability not Higher Level....This PPT is for beginners or for those who learnt or to study PROBABILITY........... :):):):):)
Markov Chains
This document provides a summary of Markov chains. It begins by defining stochastic processes and Markov chains. A Markov chain is a stochastic process where the probability of the next state depends only on the current state, not on the sequence of events that preceded it. The document discusses n-step transition probabilities, classification of states, and steady-state probabilities. It provides examples of Markov chains for cola purchases and camera store inventory to illustrate the concepts.
Geometric probability distribution
1. A geometric experiment involves independent trials with two possible outcomes (success/failure), where the probability of success (p) is constant across trials.
2. The geometric random variable (X) represents the number of trials until the first success. It has a geometric distribution where P(X=x) = p(1-p)^(x-1).
3. Examples are provided to illustrate calculating the probability of success on a given trial (x), as well as the mean and standard deviation, for geometric distributions with different probabilities of success (p).
Probability
Christiaan Huygens was a Dutch scientist known for his work in physics and mathematics. The document discusses probability theory, beginning with an introduction to probability as a measure between 0 and 1 of the likelihood of an event occurring. It discusses applications of probability theory in risk assessment and financial markets. Experiments are described as having possible outcomes that can be counted using rules for combinations and permutations to assign probabilities.
Covariance
A walk-through of the mathematics of covariance, the covariance matrix, and use cases when combined with k-means clustering. Focus on how to actually use the math, and shows how the equations turn into simple JavaScript code.
Statistics: Probability
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
Conditional Probability
Conditional probability is the probability of an event occurring given that another event has occurred. It is calculated as the probability of both events occurring divided by the probability of the first event. An example is given of calculating the probability of drawing two white balls in succession from an urn without replacement. The formula for conditional probability is derived as the probability of events A and B occurring divided by the probability of A. This is demonstrated using an example of finding the percentage of friends who like chocolate that also like strawberry.
Hypothesis presentation
This document defines and discusses hypotheses. It begins by defining a hypothesis as an assumption or educated guess that is intended to be tested. It then provides definitions of hypotheses from several scholars and discusses their key characteristics. Hypotheses are categorized as null or alternative, directional or non-directional, and deductive or inductive. The document outlines guidelines for forming a hypothesis and testing it, including relating it to the research problem, making it clear and testable, and falsifiable. In summary, this document provides an overview of what a hypothesis is and best practices for developing and testing one.
CMSC 56 | Lecture 1: Propositional Logic
Propositional Logic
CMSC 56 | Discrete Mathematical Structure for Computer Science
August 17, 2018
Instructor: Allyn Joy D. Calcaben
College of Arts & Sciences
University of the Philippines Visayas
Binomial Distribution Part 5
Binomial Distribution Part 5 deals with fitting & familiaring some concepts of B D under the complementary Statistics syllabus of University of Calicut in BSc core of Mathematics, Physics & Computer Science.
PROBABILITY AND IT'S TYPES WITH RULES
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.
Applications of differential equations by shahzad
This document summarizes applications of differential equations to real world systems including cooling/warming, population growth, radioactive decay, electrical circuits, survivability with AIDS, economics, drug distribution in the human body, and a pursuit problem. Examples are provided for each application to illustrate solutions to related differential equations. Key concepts covered include Newton's law of cooling, population models, carbon dating, series circuits, survival models, supply and demand models, compound interest, drug concentration in the body over time, and a mathematical model for a dog chasing a rabbit.
Negative Binomial Distribution
Negative Binomial Distribution introduction & over view under the complementary Statistics syllabus of University of Calicut in BSc core of Mathematics, Physics & Computer Science.
Moderation and Mediation | Dissertation Webinar
In this webinar, we discuss the basics of moderating effects and mediating effects. We explain the differences between moderation and mediation, when moderation and mediation are appropriate to conduct, and the statistical analyses used to test moderation and mediation.
What's hot (20)
Similar to Conditional statement and material implication
Chapter1p1 2.pptx
This document summarizes Section 1.1 of a chapter on propositional logic. It introduces key concepts in propositional logic including propositions, connectives like negation, conjunction, disjunction, implication, biconditional, and truth tables. It provides examples and explanations of how to construct and interpret compound propositions using these logical connectives.
Drinkfromme.pptx
The document discusses propositional logic, including:
- The basic components of propositional logic like propositions, connectives, truth tables, and logical equivalences
- Applications such as translating English sentences to propositional logic, system specifications, logic puzzles
- Representing logical relationships using truth tables and showing logical equivalences
- Using propositional logic to represent an electrical system and diagnose faults
Propositional Logic.pdf
This document provides an introduction to propositional logic and logical connectives. It defines propositions, truth values, and logical connectives such as negation, conjunction, disjunction, implication, biconditional, and how to represent them using truth tables. Examples are given for each logical connective. The document also discusses compound statements, equivalent statements, and precedence of logical operators.
Chapter1p1
This document provides an overview of propositional logic including:
- The basic components of propositional logic like propositions, connectives, truth tables
- Applications such as translating English sentences to logic, system specifications, puzzles
- Logical equivalences and showing equivalence through truth tables
- Sections cover propositions, connectives, truth tables, and applications including translation, specifications, puzzles
Chapter 01 - p1.pdf
This document summarizes Chapter 1, Part I of a textbook on propositional logic. It introduces key concepts in propositional logic, including propositions, logical connectives like negation, conjunction, disjunction, implication, biconditional, and truth tables. It provides examples of applying propositional logic to represent English sentences, system specifications, logic puzzles and logic circuits. It also briefly describes representing knowledge about an electrical system in propositional logic for fault diagnosis in artificial intelligence.
Chapter1p1.pdf
This section discusses applications of propositional logic, including translating English sentences to propositional logic, system specifications, and logic puzzles. It provides an example of translating the English sentence "You can access the Internet from campus only if you are a computer science major or you are not a freshman" to the propositional logic statement a→(c ∨ ¬f). It also gives an example of expressing the system specification "The automated reply cannot be sent when the file system is full" in propositional logic as p → ¬q.
Hypothetical%20proposition classpresentation[1]
The document discusses different types of hypothetical and logical propositions including:
- Conditional propositions expressed with "if-then" statements.
- Disjunctive propositions expressed with "either-or" statements.
- Conjunctive propositions that express two alternatives cannot be true simultaneously.
- Complex statements that contain logical components like hypothetical and negative propositions.
It also covers propositional logic concepts like negation, material implication, disjunction, conjunction, and material equivalence. Truth tables are provided to define the truth conditions for each logical operator.
lectures in prolog in order to advance in artificial intelligence
that is a prolog lecture. can students use it to lean prolog. i hope it will be awesome lecture
Conditionals
The document discusses conditional and biconditional statements in logic. It defines conditional statements using "if...then" and biconditional statements using "if and only if". It also discusses the converse, inverse, and contrapositive of conditional statements and how their truth values relate using truth tables. Specifically, the contrapositive of a conditional statement always has the same truth value as the original conditional statement.
Logic2.pptx
The document discusses key concepts in logic including:
- Propositions are declarative sentences that can be true or false.
- Logical connectives like negation, conjunction, disjunction, etc. and how they combine propositions.
- Truth tables are used to determine the truth values of compound propositions.
- Conditionals, biconditionals, and their converses/contrapositives.
- Tautologies, contradictions and contingent statements based on their truth tables.
Bab 1 proposisi
The document defines key concepts in propositional logic including propositions, truth tables, logical connectives like negation, conjunction, disjunction, implication, biconditional, and logical equivalences. It provides the definitions and truth tables for logical connectives and discusses examples like DeMorgan's laws and implications. Boolean variables and operations that correspond to logical connectives are also introduced.
Translating English to Propositional Logic
The document discusses translating statements from English to propositional logic, including:
- Conjunction and disjunction are commutative but order matters for statements with mixed operators
- How to translate conditional statements like "if P then Q" and biconditionals like "P if and only if Q"
- Necessary and sufficient conditions and how they relate to conditionals
- Examples of translating various English language statements into propositional logic statements
1. Logic.pptx
This document discusses elementary logic concepts such as statements, truth tables, and logical connectives. It begins by defining a statement as a declarative sentence that is either true or false. Examples of different types of sentences like questions and commands are provided. The rest of the document explores logical concepts like simple and compound statements, truth tables, logical connectives like conjunction and negation, conditional and biconditional statements, and identifying tautologies and contradictions. Practice problems are included throughout for the reader.
Presupposition and entailment
This document discusses entailment and presupposition in semantics. It defines entailment as a logical relationship between sentences where one follows from another. Presupposition refers to assumptions that are taken for granted prior to an utterance. Various presupposition triggers are discussed, including factive and aspectual verbs. Presuppositions must be true regardless of whether the statement is true. The document contrasts semantic and pragmatic approaches to analyzing presupposition and provides examples of entailment, presupposition, and presupposition failure.
Mathematical prepositions implications
This document discusses mathematical propositions and implications. An implication is a statement in the form "If P, then Q" where P is the hypothesis and Q is the conclusion. For an implication to be true, if P is false, the implication can be either true or false. The converse of an implication switches P and Q, but the converse is not necessarily equivalent to the original statement. The contrapositive negates both P and Q and switches them, making it logically equivalent to the original implication. The inverse negates only P or only Q. Examples of implications, converses, contrapositives and inverses are provided.
1 intro to logic
This document provides an introduction to symbolic logic, including simple and compound statements, logical connectives such as NOT, AND and OR, and truth tables. It defines statements, connectives, negation, conjunction, disjunction and uses truth tables to evaluate logical statements. Examples are provided to illustrate logical concepts like negation, conjunction, disjunction and logically equivalent statements using truth tables.
Entailment and presupposition semantics and pragmatics
This document discusses entailment and presupposition in semantics. It defines entailment as something that logically follows from an utterance. Presupposition is something assumed to be true before an utterance. There are two approaches to presupposition - semantic and pragmatic. Presuppositions are produced by triggers like factive verbs and aspectual verbs. Presuppositions relate to truth conditions and can fail if the presupposed information is not true. Pragmatic theories view presupposition as a pragmatic phenomenon influenced by syntax, intonation and background information.
Per3 logika&pembuktian
This document provides an overview of logic and proofs. It begins by defining logic as the study of correct reasoning, and discusses how logic is used in mathematics and computer science. Some key concepts introduced include:
- Propositions and truth values
- Logical connectives like AND, OR, NOT
- Truth tables for evaluating compound propositions
- Quantifiers like "for all" and "there exists"
- Propositional functions and their domains
- Types of proofs like direct proof, proof by contradiction, and mathematical induction
The document concludes by covering resolution proofs and the strong form of mathematical induction, which involves verifying a basis step and proving the induction step. Overall, it serves as an introduction to
Similar to Conditional statement and material implication (20)
lectures in prolog in order to advance in artificial intelligence
lectures in prolog in order to advance in artificial intelligence
Entailment and presupposition semantics and pragmatics
Entailment and presupposition semantics and pragmatics
Recently uploaded
Community pharmacy- Social and preventive pharmacy UNIT 5
Covered community pharmacy topic of the subject Social and preventive pharmacy for Diploma and Bachelor of pharmacy
How to Manage Your Lost Opportunities in Odoo 17 CRM
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
BÀI TẬP BỔ TRỢ TIẾNG ANH 8 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2023-2024 (CÓ FI...
BÀI TẬP BỔ TRỢ TIẾNG ANH 8 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2023-2024 (CÓ FI...Nguyen Thanh Tu Collection
https://app.box.com/s/y977uz6bpd3af4qsebv7r9b7s21935vdISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
-------------------------------------------------------------------------------
Find out more about ISO training and certification services
Training: ISO/IEC 27001 Information Security Management System - EN | PECB
ISO/IEC 42001 Artificial Intelligence Management System - EN | PECB
General Data Protection Regulation (GDPR) - Training Courses - EN | PECB
Webinars: https://pecb.com/webinars
Article: https://pecb.com/article
-------------------------------------------------------------------------------
For more information about PECB:
Website: https://pecb.com/
LinkedIn: https://www.linkedin.com/company/pecb/
Facebook: https://www.facebook.com/PECBInternational/
Slideshare: http://www.slideshare.net/PECBCERTIFICATION
The simplified electron and muon model, Oscillating Spacetime: The Foundation...
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.
PCOS corelations and management through Ayurveda.
This presentation includes basic of PCOS their pathology and treatment and also Ayurveda correlation of PCOS and Ayurvedic line of treatment mentioned in classics.
South African Journal of Science: Writing with integrity workshop (2024)
South African Journal of Science: Writing with integrity workshop (2024)Academy of Science of South Africa
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.Advanced Java[Extra Concepts, Not Difficult].docx
This is part 2 of my Java Learning Journey. This contains Hashing, ArrayList, LinkedList, Date and Time Classes, Calendar Class and more.
Azure Interview Questions and Answers PDF By ScholarHat
Azure Interview Questions and Answers PDF By ScholarHat
The Diamonds of 2023-2024 in the IGRA collection
A review of the growth of the Israel Genealogy Research Association Database Collection for the last 12 months. Our collection is now passed the 3 million mark and still growing. See which archives have contributed the most. See the different types of records we have, and which years have had records added. You can also see what we have for the future.
Walmart Business+ and Spark Good for Nonprofits.pdf
"Learn about all the ways Walmart supports nonprofit organizations.
You will hear from Liz Willett, the Head of Nonprofits, and hear about what Walmart is doing to help nonprofits, including Walmart Business and Spark Good. Walmart Business+ is a new offer for nonprofits that offers discounts and also streamlines nonprofits order and expense tracking, saving time and money.
The webinar may also give some examples on how nonprofits can best leverage Walmart Business+.
The event will cover the following::
Walmart Business + (https://business.walmart.com/plus) is a new shopping experience for nonprofits, schools, and local business customers that connects an exclusive online shopping experience to stores. Benefits include free delivery and shipping, a 'Spend Analytics” feature, special discounts, deals and tax-exempt shopping.
Special TechSoup offer for a free 180 days membership, and up to $150 in discounts on eligible orders.
Spark Good (walmart.com/sparkgood) is a charitable platform that enables nonprofits to receive donations directly from customers and associates.
Answers about how you can do more with Walmart!"
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...
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 KanvariaANATOMY AND BIOMECHANICS OF HIP JOINT.pdf
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
Recently uploaded (20)
Community pharmacy- Social and preventive pharmacy UNIT 5
Community pharmacy- Social and preventive pharmacy UNIT 5
How to Manage Your Lost Opportunities in Odoo 17 CRM
How to Manage Your Lost Opportunities in Odoo 17 CRM
BÀI TẬP BỔ TRỢ TIẾNG ANH 8 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2023-2024 (CÓ FI...
BÀI TẬP BỔ TRỢ TIẾNG ANH 8 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2023-2024 (CÓ FI...
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...
Liberal Approach to the Study of Indian Politics.pdf
Liberal Approach to the Study of Indian Politics.pdf
The simplified electron and muon model, Oscillating Spacetime: The Foundation...
The simplified electron and muon model, Oscillating Spacetime: The Foundation...
South African Journal of Science: Writing with integrity workshop (2024)
South African Journal of Science: Writing with integrity workshop (2024)
Azure Interview Questions and Answers PDF By ScholarHat
Azure Interview Questions and Answers PDF By ScholarHat
Walmart Business+ and Spark Good for Nonprofits.pdf
Walmart Business+ and Spark Good for Nonprofits.pdf
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...
Conditional statement and material implication
- 2. Conditional statements and material implication Presented by: Majid khan Roll no : 13 BBA morning
- 3. Conditional statement when two statements are combined by placing the word “if” before the first statement and “then” before the second statement is called conditional statement eg. If Mr. A work hard then he will pass.
- 4. Conditional statement We use the arrow → or horseshoe to represent the “if-then” phrase Also called a hypothetical statement
- 5. Components of conditional statement There are two components of conditional statement o The component statement that follows the “if” is called the antecedent o The component statement that follows the “then” is called the consequent o If (antecedent), then (consequent)
- 6. Conditional Statement A conditional statement does not assert that the antecedent is necessarily true. It only asserts that if the antecedent is true, then the consequent is true. And a conditional statement does not assert that the consequent is necessarily true. It asserts that the consequent is true only if the antecedent is true.
- 8. Material implication A conditional statement describes a relationship between the antecedent and the consequent. This relationship is called "material implication" Denoted by: “=>” or
- 9. For Example If you study hard, then you will pass. Antecedent Consequent => If p, then q.
- 10. Material implication Then the relationship of material implication between p and q is symbolized as p => q+ and is read “p implies q”.
- 11. Truth Table p Q P => q T T T T F F F T T F F T “p => q” is false if antecedent p is true and consequent q is false; otherwise, true.
- 12. Logical Implication The consequent follows logically from its antecedent If all humans are mortal and Socrates is a human, then Socrates is mortal.
- 13. Definitional Implication The consequent follows the antecedent by definition If Leslie is a bachelor, then Leslie is unmarried.
- 14. Causal Implication Theconnection between antecedent and consequent is discovered empirically If I put X in acid, then X will turn red.
- 15. Decisional Implication No logical connection nor one by definition between the consequent and antecedent. This is a decision of the speaker to behave in the specified way under the specified circumstances. Is we lose the game, then I’ll eat my hat.
- 16. Thank you