This document discusses Boolean algebra properties and concepts. It defines Boolean variables and constants, and lists common Boolean algebra properties like idempotence, complement, duality, commutativity, associativity, distributivity, De Morgan's laws, and absorption. It also discusses Boolean expressions, truth tables, canonical forms, minterms, maxterms, and how to derive the complement and canonical forms of a Boolean function.
The document summarizes the Rubin causal model and key assumptions and methods for causal inference using observational data, including linear regression models.
It introduces the Rubin model for causal effects, noting the need for a good counterfactual to estimate causal parameters. It then covers simple linear regression models (SLRM) and assumptions needed for causal interpretation, including the zero conditional mean assumption.
Finally, it discusses multivariate linear regression models (MLRM), outlining additional assumptions required like no multicollinearity between covariates and the independence of errors from covariates. It also introduces ordinary least squares estimation and the Frisch-Waugh theorem for interpreting slope estimates from MLRM.
Intermediate Microeconomic Theory Midterm 2 "Cheat Sheet"Laurel Ayuyao
For Intermediate Microeconomic Theory (ECON 30010) at University of Notre Dame. Topics include demand, elasticity, income and substitution effects, compensating and equivalent variation, intertemporal choice, uncertainty, and preferences over risk.
1. The optimal input blend is where the marginal productivity per dollar spent is equal for all inputs.
2. A production function specifies the maximum output possible from different combinations of inputs.
3. A cost minimizing firm will produce at the input levels where the marginal costs of each input are equal to its price.
14 inverse trig functions and linear trig equations-xmath260
The document discusses inverse functions. It begins by defining a function and its inverse. The inverse reverses the relationship by taking an output and finding the corresponding input(s). However, not all functions have inverses as this reversing process may not be single-valued. Examples are provided to illustrate one-to-one functions that have inverses and those that are not one-to-one and do not have inverses. The domain of a function impacts whether an inverse exists or not.
This document summarizes a presentation on SAT/SMT solving in Haskell. It discusses two main Haskell libraries for SMT solving - sbv, which provides a high-level DSL for specifying SMT problems and interfaces with multiple solvers, and toysolver, which contains the toysat SAT solver and toysmt SMT solver implemented natively in Haskell. It then demonstrates solving the "send more money" puzzle using sbv and running a simple problem on toysmt.
This document provides an introduction to creative coding and Processing basics, including variables, conditionals, and interaction design. It begins with an overview of Processing and provides examples of using variables to continuously update values and using multiple variables. Conditional statements like if/else are demonstrated to control program flow based on conditions. Boolean variables and logical operators are also introduced. The document concludes with exercises for students to create interactive drawings and buttons using concepts covered.
7. inverse trig functions and linear trig equations-xharbormath240
- The document discusses inverse functions, which reverse the input-output relationship of a function.
- For a function f(x) to have an inverse f-1(y), it must be one-to-one, meaning different inputs give different outputs.
- Examples show how to find the inverse of functions like f(x)=2x by taking the output and reversing the operation, and that some functions like f(x)=x2 do not have inverses because they are not one-to-one.
TensorFlow is a wonderful tool for rapidly implementing neural networks. In this presentation, we will learn the basics of TensorFlow and show how neural networks can be built with just a few lines of code. We will highlight some of the confusing bits of TensorFlow as a way of developing the intuition necessary to avoid common pitfalls when developing your own models. Additionally, we will discuss how to roll our own Recurrent Neural Networks. While many tutorials focus on using built in modules, this presentation will focus on writing neural networks from scratch enabling us to build flexible models when Tensorflow’s high level components can’t quite fit our needs.
About Nathan Lintz:
Nathan Lintz is a research scientist at indico Data Solutions where he is responsible for developing machine learning systems in the domains of language detection, text summarization, and emotion recognition. Outside of work, Nathan is currently writting a book on TensorFlow as an extension to his tutorial repository https://github.com/nlintz/TensorFlow-Tutorials
Link to video https://www.youtube.com/watch?v=op1QJbC2g0E&feature=youtu.be
The document summarizes the Rubin causal model and key assumptions and methods for causal inference using observational data, including linear regression models.
It introduces the Rubin model for causal effects, noting the need for a good counterfactual to estimate causal parameters. It then covers simple linear regression models (SLRM) and assumptions needed for causal interpretation, including the zero conditional mean assumption.
Finally, it discusses multivariate linear regression models (MLRM), outlining additional assumptions required like no multicollinearity between covariates and the independence of errors from covariates. It also introduces ordinary least squares estimation and the Frisch-Waugh theorem for interpreting slope estimates from MLRM.
Intermediate Microeconomic Theory Midterm 2 "Cheat Sheet"Laurel Ayuyao
For Intermediate Microeconomic Theory (ECON 30010) at University of Notre Dame. Topics include demand, elasticity, income and substitution effects, compensating and equivalent variation, intertemporal choice, uncertainty, and preferences over risk.
1. The optimal input blend is where the marginal productivity per dollar spent is equal for all inputs.
2. A production function specifies the maximum output possible from different combinations of inputs.
3. A cost minimizing firm will produce at the input levels where the marginal costs of each input are equal to its price.
14 inverse trig functions and linear trig equations-xmath260
The document discusses inverse functions. It begins by defining a function and its inverse. The inverse reverses the relationship by taking an output and finding the corresponding input(s). However, not all functions have inverses as this reversing process may not be single-valued. Examples are provided to illustrate one-to-one functions that have inverses and those that are not one-to-one and do not have inverses. The domain of a function impacts whether an inverse exists or not.
This document summarizes a presentation on SAT/SMT solving in Haskell. It discusses two main Haskell libraries for SMT solving - sbv, which provides a high-level DSL for specifying SMT problems and interfaces with multiple solvers, and toysolver, which contains the toysat SAT solver and toysmt SMT solver implemented natively in Haskell. It then demonstrates solving the "send more money" puzzle using sbv and running a simple problem on toysmt.
This document provides an introduction to creative coding and Processing basics, including variables, conditionals, and interaction design. It begins with an overview of Processing and provides examples of using variables to continuously update values and using multiple variables. Conditional statements like if/else are demonstrated to control program flow based on conditions. Boolean variables and logical operators are also introduced. The document concludes with exercises for students to create interactive drawings and buttons using concepts covered.
7. inverse trig functions and linear trig equations-xharbormath240
- The document discusses inverse functions, which reverse the input-output relationship of a function.
- For a function f(x) to have an inverse f-1(y), it must be one-to-one, meaning different inputs give different outputs.
- Examples show how to find the inverse of functions like f(x)=2x by taking the output and reversing the operation, and that some functions like f(x)=x2 do not have inverses because they are not one-to-one.
TensorFlow is a wonderful tool for rapidly implementing neural networks. In this presentation, we will learn the basics of TensorFlow and show how neural networks can be built with just a few lines of code. We will highlight some of the confusing bits of TensorFlow as a way of developing the intuition necessary to avoid common pitfalls when developing your own models. Additionally, we will discuss how to roll our own Recurrent Neural Networks. While many tutorials focus on using built in modules, this presentation will focus on writing neural networks from scratch enabling us to build flexible models when Tensorflow’s high level components can’t quite fit our needs.
About Nathan Lintz:
Nathan Lintz is a research scientist at indico Data Solutions where he is responsible for developing machine learning systems in the domains of language detection, text summarization, and emotion recognition. Outside of work, Nathan is currently writting a book on TensorFlow as an extension to his tutorial repository https://github.com/nlintz/TensorFlow-Tutorials
Link to video https://www.youtube.com/watch?v=op1QJbC2g0E&feature=youtu.be
Video: https://youtu.be/dYhrCUFN0eM
Article: https://medium.com/p/the-gentlest-introduction-to-tensorflow-248dc871a224
Code: https://github.com/nethsix/gentle_tensorflow/blob/master/code/linear_regression_one_feature.py
This alternative introduction to Google's official Tensorflow (TF) tutorial strips away the unnecessary concepts that overly complicates getting started. The goal is to use TF to perform Linear Regression (LR) that has only a single-feature. We show how to model the LR using a TF graph, how to define the cost function to measure how well the an LR model fits the dataset, and finally train the LR model to find the best fit model.
This document provides a list of commonly used test functions for validating new optimization algorithms. It describes 24 test functions, including functions originally developed by De Jong, Griewank, Rastrigin, and Rosenbrock. The test functions have various properties like being unimodal, multimodal, convex, or stochastic. They serve as benchmarks for comparing how well new algorithms can find the optimal value for problems with different characteristics.
Gentlest Introduction to Tensorflow - Part 2Khor SoonHin
Video: https://youtu.be/Trc52FvMLEg
Article: https://medium.com/@khor/gentlest-introduction-to-tensorflow-part-2-ed2a0a7a624f
Code: https://github.com/nethsix/gentle_tensorflow
Continuing from Part 1 where we used Tensorflow to perform linear regression for a model with single feature, here we:
* Use Tensorboard to visualize linear regression variables and the Tensorflow network graph
* Perform stochastic/mini-batch/batch gradient descent
This document provides an introduction to copulas and modeling correlated risks. It discusses copulas in dimensions 2 and higher, including definitions, properties, and examples of classical copulas like the independent, comonotonic, and countercomonotonic copulas. It also discusses estimating copulas from data using ranks rather than raw values due to unknown marginal distributions, and using copulas to model dependence between multiple risks.
Lesson 26: The Fundamental Theorem of Calculus (Section 10 version)Matthew Leingang
The First Fundamental Theorem of Calculus looks at the area function and its derivative. It so happens that the derivative of the area function is the original integrand.
Gentlest Introduction to Tensorflow - Part 3Khor SoonHin
Articles:
* https://medium.com/all-of-us-are-belong-to-machines/gentlest-intro-to-tensorflow-part-3-matrices-multi-feature-linear-regression-30a81ebaaa6c
* https://medium.com/all-of-us-are-belong-to-machines/gentlest-intro-to-tensorflow-4-logistic-regression-2afd0cabc54
Video: https://youtu.be/F8g_6TXKlxw
Code: https://github.com/nethsix/gentle_tensorflow
In this part, we:
* Use Tensorflow for linear regression models with multiple features
* Use Tensorflow for logistic regression models with multiple features. Specifically:
* Predict multi-class/discrete outcome
* Explain why we use cross-entropy as cost function
* Explain why we use softmax
* Tensorflow Cheatsheet #1
* Single feature linear regression
* Multi-feature linear regression
* Multi-feature logistic regression
This document discusses modeling and estimating extreme risks and quantiles in non-life insurance. It introduces the generalized extreme value distribution and three limiting distributions used to model extreme values. It also discusses estimators like the Hill estimator that are used to estimate the shape parameter of distributions modeling extreme risks. Methods for estimating value-at-risk and tail-value-at-risk based on the generalized Pareto distribution above a threshold are also presented.
This document discusses quantile and expectile regressions. It begins by explaining the differences between the econometrics and machine learning approaches. It then introduces quantile and expectile regressions as generalizations of ordinary least squares regression that minimize different loss functions. Finally, it discusses properties of quantile and expectile regressions such as their elicitable measures and how they can be estimated.
This document discusses regression, least squares, quantile regression, and analyzing inequality and welfare using empirical income data from the UK from 1988, 1992, and 1996. It provides examples of using R to perform regressions, calculate inequality measures like the Gini coefficient, and plot Lorenz curves. Key concepts covered include the regression effect first identified by Galton, least squares estimation, quantile regression, and exploring trends in UK income inequality over time.
Unit IV UNCERTAINITY AND STATISTICAL REASONING in AI K.Sundar,AP/CSE,VECsundarKanagaraj1
This document discusses uncertainty and statistical reasoning in artificial intelligence. It covers probability theory, Bayesian networks, and certainty factors. Key topics include probability distributions, Bayes' rule, building Bayesian networks, different types of probabilistic inferences using Bayesian networks, and defining and combining certainty factors. Case studies are provided to illustrate each algorithm.
This document summarizes the use of log-Poisson regression models for claims reserving and calculating reserves. It shows how to fit a log-Poisson regression model to incremental claims payments data and use it to estimate total reserves. It also provides methods for calculating the prediction error and quantifying the uncertainty of reserve estimates, including using the bootstrap procedure to generate multiple simulated reserve estimates.
This document summarizes a presentation on multi-attribute utility and copulas. It discusses independence and additivity assumptions in multi-attribute utility theory. It also describes how to construct multi-attribute utility functions using marginal utility functions and copulas. Finally, it introduces the concept of one-switch utility independence and discusses how utility trees and bidirectional diagrams can represent relationships between attributes.
Pybelsberg is a project allowing constraint-based programming in Python using the Z3 theorem prover [1].
It is available on Github [2] and is licensed under the BSD 3-Clause License.
By Robert Lehmann, Christoph Matthies, Conrad Calmez, Thomas Hille.
See also Babelsberg/R [4] and Babelsberg/JS [5].
[1] https://github.com/Z3Prover/z3
[2] https://github.com/babelsberg/pybelsberg
[3] http://opensource.org/licenses/BSD-3-Clause
[4] https://github.com/timfel/babelsberg-r
[5] https://github.com/timfel/babelsberg-js
This document summarizes notes from an advanced econometrics graduate course. It discusses topics like model and variable selection, numerical optimization techniques like gradient descent, convex optimization problems, and the Karush-Kuhn-Tucker conditions for solving convex problems. It also covers reducing dimensionality with techniques like principal component analysis and partial least squares, penalizing complex models, and information criteria like AIC that balance model fit and complexity.
1. The document discusses A/B testing approaches for game design, noting key areas that can be tested like onboarding experiences, monetization strategies, and retention mechanics.
2. It introduces Bayesian approaches to A/B testing, noting that observing results allows updating beliefs about hypotheses rather than relying on passing a threshold for significance.
3. Key challenges with frequentist approaches are discussed like multiple comparisons inflating false positive rates, and "peeking" at intermediate results invalidating conclusions. Bayesian methods account for uncertainty and can incorporate prior information and new evidence iteratively.
This document discusses various modeling techniques for non-life insurance ratemaking including individual and collective models, Tweedie regression, and the LASSO method. It explores using a Tweedie distribution for compound Poisson models and the relationship between individual and collective models. The document also examines issues with high-dimensional data in insurance, bias-variance tradeoffs, and regularization methods like ridge regression and the LASSO for variable selection.
Lesson 16: Derivatives of Logarithmic and Exponential FunctionsMatthew Leingang
We show the the derivative of the exponential function is itself! And the derivative of the natural logarithm function is the reciprocal function. We also show how logarithms can make complicated differentiation problems easier.
Explanation on Tensorflow example -Deep mnist for expert홍배 김
you can find the exact and detailed network architecture of 'Deep mnist for expert' example of tensorflow's tutorial. I also added descriptions on the program for your better understanding.
The document contains code snippets demonstrating the use of TensorFlow for building and training neural networks. It shows how to:
1. Define operations like convolutions, max pooling, fully connected layers using common TensorFlow functions like tf.nn.conv2d and tf.nn.max_pool.
2. Create and initialize variables using tf.Variable and initialize them using tf.global_variables_initializer.
3. Construct a multi-layer perceptron model for MNIST classification with convolutional and fully connected layers.
4. Train the model using tf.train.AdamOptimizer by running optimization steps and calculating loss over batches of data.
5. Evaluate the trained model on test data to calculate accuracy.
The document discusses properties and theorems of Boolean algebra. It defines key concepts like Boolean variables, constants, minterms, maxterms, and duality. Properties covered include idempotence, complement, absorption, consensus, commutative, associative, distributive, De Morgan's laws. Truth tables and canonical forms are also discussed. Duality and complementation are shown to derive equivalent expressions.
The document provides an overview of digital logic circuits. It begins with an introduction to logic gates and Boolean algebra. Common logic gates like AND, OR, NAND, NOR, XOR and XNOR are described along with their truth tables. Boolean algebra is introduced as an algebra used for analysis and synthesis of digital logic circuits. Standard forms like sum of products and product of sums are discussed. Karnaugh maps are then described as a method for simplifying Boolean functions to minimize logic circuits. The document concludes with examples of map simplification using adjacent cells and combinations of multiple cells.
Video: https://youtu.be/dYhrCUFN0eM
Article: https://medium.com/p/the-gentlest-introduction-to-tensorflow-248dc871a224
Code: https://github.com/nethsix/gentle_tensorflow/blob/master/code/linear_regression_one_feature.py
This alternative introduction to Google's official Tensorflow (TF) tutorial strips away the unnecessary concepts that overly complicates getting started. The goal is to use TF to perform Linear Regression (LR) that has only a single-feature. We show how to model the LR using a TF graph, how to define the cost function to measure how well the an LR model fits the dataset, and finally train the LR model to find the best fit model.
This document provides a list of commonly used test functions for validating new optimization algorithms. It describes 24 test functions, including functions originally developed by De Jong, Griewank, Rastrigin, and Rosenbrock. The test functions have various properties like being unimodal, multimodal, convex, or stochastic. They serve as benchmarks for comparing how well new algorithms can find the optimal value for problems with different characteristics.
Gentlest Introduction to Tensorflow - Part 2Khor SoonHin
Video: https://youtu.be/Trc52FvMLEg
Article: https://medium.com/@khor/gentlest-introduction-to-tensorflow-part-2-ed2a0a7a624f
Code: https://github.com/nethsix/gentle_tensorflow
Continuing from Part 1 where we used Tensorflow to perform linear regression for a model with single feature, here we:
* Use Tensorboard to visualize linear regression variables and the Tensorflow network graph
* Perform stochastic/mini-batch/batch gradient descent
This document provides an introduction to copulas and modeling correlated risks. It discusses copulas in dimensions 2 and higher, including definitions, properties, and examples of classical copulas like the independent, comonotonic, and countercomonotonic copulas. It also discusses estimating copulas from data using ranks rather than raw values due to unknown marginal distributions, and using copulas to model dependence between multiple risks.
Lesson 26: The Fundamental Theorem of Calculus (Section 10 version)Matthew Leingang
The First Fundamental Theorem of Calculus looks at the area function and its derivative. It so happens that the derivative of the area function is the original integrand.
Gentlest Introduction to Tensorflow - Part 3Khor SoonHin
Articles:
* https://medium.com/all-of-us-are-belong-to-machines/gentlest-intro-to-tensorflow-part-3-matrices-multi-feature-linear-regression-30a81ebaaa6c
* https://medium.com/all-of-us-are-belong-to-machines/gentlest-intro-to-tensorflow-4-logistic-regression-2afd0cabc54
Video: https://youtu.be/F8g_6TXKlxw
Code: https://github.com/nethsix/gentle_tensorflow
In this part, we:
* Use Tensorflow for linear regression models with multiple features
* Use Tensorflow for logistic regression models with multiple features. Specifically:
* Predict multi-class/discrete outcome
* Explain why we use cross-entropy as cost function
* Explain why we use softmax
* Tensorflow Cheatsheet #1
* Single feature linear regression
* Multi-feature linear regression
* Multi-feature logistic regression
This document discusses modeling and estimating extreme risks and quantiles in non-life insurance. It introduces the generalized extreme value distribution and three limiting distributions used to model extreme values. It also discusses estimators like the Hill estimator that are used to estimate the shape parameter of distributions modeling extreme risks. Methods for estimating value-at-risk and tail-value-at-risk based on the generalized Pareto distribution above a threshold are also presented.
This document discusses quantile and expectile regressions. It begins by explaining the differences between the econometrics and machine learning approaches. It then introduces quantile and expectile regressions as generalizations of ordinary least squares regression that minimize different loss functions. Finally, it discusses properties of quantile and expectile regressions such as their elicitable measures and how they can be estimated.
This document discusses regression, least squares, quantile regression, and analyzing inequality and welfare using empirical income data from the UK from 1988, 1992, and 1996. It provides examples of using R to perform regressions, calculate inequality measures like the Gini coefficient, and plot Lorenz curves. Key concepts covered include the regression effect first identified by Galton, least squares estimation, quantile regression, and exploring trends in UK income inequality over time.
Unit IV UNCERTAINITY AND STATISTICAL REASONING in AI K.Sundar,AP/CSE,VECsundarKanagaraj1
This document discusses uncertainty and statistical reasoning in artificial intelligence. It covers probability theory, Bayesian networks, and certainty factors. Key topics include probability distributions, Bayes' rule, building Bayesian networks, different types of probabilistic inferences using Bayesian networks, and defining and combining certainty factors. Case studies are provided to illustrate each algorithm.
This document summarizes the use of log-Poisson regression models for claims reserving and calculating reserves. It shows how to fit a log-Poisson regression model to incremental claims payments data and use it to estimate total reserves. It also provides methods for calculating the prediction error and quantifying the uncertainty of reserve estimates, including using the bootstrap procedure to generate multiple simulated reserve estimates.
This document summarizes a presentation on multi-attribute utility and copulas. It discusses independence and additivity assumptions in multi-attribute utility theory. It also describes how to construct multi-attribute utility functions using marginal utility functions and copulas. Finally, it introduces the concept of one-switch utility independence and discusses how utility trees and bidirectional diagrams can represent relationships between attributes.
Pybelsberg is a project allowing constraint-based programming in Python using the Z3 theorem prover [1].
It is available on Github [2] and is licensed under the BSD 3-Clause License.
By Robert Lehmann, Christoph Matthies, Conrad Calmez, Thomas Hille.
See also Babelsberg/R [4] and Babelsberg/JS [5].
[1] https://github.com/Z3Prover/z3
[2] https://github.com/babelsberg/pybelsberg
[3] http://opensource.org/licenses/BSD-3-Clause
[4] https://github.com/timfel/babelsberg-r
[5] https://github.com/timfel/babelsberg-js
This document summarizes notes from an advanced econometrics graduate course. It discusses topics like model and variable selection, numerical optimization techniques like gradient descent, convex optimization problems, and the Karush-Kuhn-Tucker conditions for solving convex problems. It also covers reducing dimensionality with techniques like principal component analysis and partial least squares, penalizing complex models, and information criteria like AIC that balance model fit and complexity.
1. The document discusses A/B testing approaches for game design, noting key areas that can be tested like onboarding experiences, monetization strategies, and retention mechanics.
2. It introduces Bayesian approaches to A/B testing, noting that observing results allows updating beliefs about hypotheses rather than relying on passing a threshold for significance.
3. Key challenges with frequentist approaches are discussed like multiple comparisons inflating false positive rates, and "peeking" at intermediate results invalidating conclusions. Bayesian methods account for uncertainty and can incorporate prior information and new evidence iteratively.
This document discusses various modeling techniques for non-life insurance ratemaking including individual and collective models, Tweedie regression, and the LASSO method. It explores using a Tweedie distribution for compound Poisson models and the relationship between individual and collective models. The document also examines issues with high-dimensional data in insurance, bias-variance tradeoffs, and regularization methods like ridge regression and the LASSO for variable selection.
Lesson 16: Derivatives of Logarithmic and Exponential FunctionsMatthew Leingang
We show the the derivative of the exponential function is itself! And the derivative of the natural logarithm function is the reciprocal function. We also show how logarithms can make complicated differentiation problems easier.
Explanation on Tensorflow example -Deep mnist for expert홍배 김
you can find the exact and detailed network architecture of 'Deep mnist for expert' example of tensorflow's tutorial. I also added descriptions on the program for your better understanding.
The document contains code snippets demonstrating the use of TensorFlow for building and training neural networks. It shows how to:
1. Define operations like convolutions, max pooling, fully connected layers using common TensorFlow functions like tf.nn.conv2d and tf.nn.max_pool.
2. Create and initialize variables using tf.Variable and initialize them using tf.global_variables_initializer.
3. Construct a multi-layer perceptron model for MNIST classification with convolutional and fully connected layers.
4. Train the model using tf.train.AdamOptimizer by running optimization steps and calculating loss over batches of data.
5. Evaluate the trained model on test data to calculate accuracy.
The document discusses properties and theorems of Boolean algebra. It defines key concepts like Boolean variables, constants, minterms, maxterms, and duality. Properties covered include idempotence, complement, absorption, consensus, commutative, associative, distributive, De Morgan's laws. Truth tables and canonical forms are also discussed. Duality and complementation are shown to derive equivalent expressions.
The document provides an overview of digital logic circuits. It begins with an introduction to logic gates and Boolean algebra. Common logic gates like AND, OR, NAND, NOR, XOR and XNOR are described along with their truth tables. Boolean algebra is introduced as an algebra used for analysis and synthesis of digital logic circuits. Standard forms like sum of products and product of sums are discussed. Karnaugh maps are then described as a method for simplifying Boolean functions to minimize logic circuits. The document concludes with examples of map simplification using adjacent cells and combinations of multiple cells.
This document discusses Boolean algebra and logic simplifications. It covers binary logic and gates, Boolean algebra properties and manipulation techniques, standard and canonical forms such as sum of products (SOP) and product of sums (POS), and Karnaugh maps. Minterms and maxterms represent the canonical forms, denoting each combination in the truth table. Boolean algebra allows simplifying logic functions through algebraic manipulation and putting them in standard forms.
This is the entrance exam paper for ISI MSQE Entrance Exam for the year 2010. Much more information on the ISI MSQE Entrance Exam and ISI MSQE Entrance preparation help available on http://crackdse.com
Karnaugh maps are a graphical method used to minimize logic functions. They arrange the minterms of a function in a grid based on the number of variables. Groupings of adjacent 1s in the map correspond to simplified logic terms. The largest possible groupings are used to find a minimum logic expression for the function. Don't cares can also be grouped and treated as 0s or 1s to further simplify expressions.
This document provides an introduction to Boolean algebra and its applications in digital logic. It discusses how Boolean algebra was developed by George Boole in the 1800s as an algebra of logic to represent logical statements as either true or false. The document then explains how Boolean algebra is used to perform logical operations in digital computers by representing true as 1 and false as 0. It introduces the basic logical operators of AND, OR, and NOT and provides their truth tables. The rest of the document discusses topics such as logic gates, truth tables, minterms, maxterms, and how to realize Boolean functions using sum of products and product of sums forms.
ECE 2103_L6 Boolean Algebra Canonical Forms [Autosaved].pptxMdJubayerFaisalEmon
This document discusses digital system design and Boolean algebra concepts. It covers canonical and standard forms, minterms and maxterms, conversions between forms, sum of minterms, product of maxterms, and other logic operations. Examples are provided to demonstrate minimizing Boolean functions using K-maps and converting between standard forms. DeMorgan's laws and other Boolean algebra properties are also explained. Tutorial problems are given at the end to practice simplifying Boolean expressions and converting between standard forms.
18 pc09 1.2_ digital logic gates _ boolean algebra_basic theoremsarunachalamr16
Digital logic gates are basic building blocks of digital circuits that make logical decisions based on input combinations. There are three basic logic gates: OR, AND, and NOT. Other common gates such as NAND, NOR, XOR, and XNOR are derived from these. Boolean algebra uses variables that can be 1 or 0, and logical operators like AND, OR, and NOT to represent logic functions. Logic functions can be expressed in canonical forms such as sum of minterms or product of maxterms. Standard forms like SOP and POS are also used. Conversions between these forms allow simplifying logic functions.
This document discusses canonical and standard forms in digital logic circuits. It defines minterms and maxterms, and describes how to convert between sum of products and product of sums forms. Procedures for converting Boolean functions to sum of minterms and product of maxterms are provided with examples. Other logic operations such as XOR and XNOR are also defined.
The document discusses functions and evaluating functions. It provides examples of determining if a given equation is a function using the vertical line test and evaluating functions by substituting values into the function equation. It also includes examples of evaluating composite functions using flow diagrams to illustrate the steps of evaluating each individual function.
This document discusses Boolean algebra and logic gates. It begins with an introduction to binary logic and Boolean variables that can take on values of 0 or 1. It describes logical operators like AND, OR, and NOT. Boolean algebra provides a mathematical system for specifying and transforming logic functions. The document provides examples of Boolean functions and logic gates. It discusses topics like Boolean variables and values, Boolean functions, logical operators, Boolean arithmetic, theorems, and algebraic proofs. George Boole is credited with developing Boolean algebra. Truth tables and Karnaugh maps are shown as ways to analyze Boolean functions.
digital logic design Chapter 2 boolean_algebra_&_logic_gatesImran Waris
The document discusses Boolean algebra and logic gates. It defines binary operators like AND, OR, and NOT. It covers Boolean algebra postulates and theorems including duality, DeMorgan's theorem, and absorption. Standard forms like sum of products and product of sums are presented. Common logic gates such as AND, OR, NAND, NOR, XOR, and XNOR are defined. Homework problems from a textbook are listed involving simplifying Boolean expressions, drawing logic diagrams, and converting expressions between canonical forms.
The document discusses Boolean expressions and their use in computer programming. It defines Boolean expressions as expressions that evaluate to true or false. Boolean expressions are composed of logical operators like AND, OR, and NOT. The document then discusses different logical operators and their truth tables. It also covers Boolean algebra identities and theorems. Finally, it introduces concepts like minterms, maxterms, sum of products, and product of sums and how Karnaugh maps can be used to simplify Boolean expressions.
This document discusses Boolean logic and its application to logic circuits. It begins by explaining George Boole's development of Boolean logic using variables to represent classes. It then defines the axioms of Boolean algebra, including operators for AND, OR, and NOT. The document demonstrates how to simplify Boolean expressions using these axioms. It also explains how Boolean logic relates to logic circuits, with references to Claude Shannon's work. Finally, it provides an example of how Boolean logic can be applied to analyze the control circuitry for horizontal ball movement in the early video game PONG.
Digital Logic.pptxghuuhhhhhhuu7ffghhhhhgAnujyotiDe
Jdjddjdjjfjrjrhhfbrhrjhjjfjrjfrifjhfjdirurfu8fi3hdudoickegdhejdofjrvdjozdkjieofiudjrhciyyfifjjvi9r7ugidof8cdukrkfj2keoekeRecombination in bacterial growth refers to the process by which bacteria exchange genetic material with each other, leading to the formation of new genetic combinations. This process can occur through several mechanisms, including transformation, conjugation, and transduction.
1. Transformation: In transformation, bacteria take up free DNA from their environment and incorporate it into their genome. This DNA may come from lysed bacterial cells or be released into the environment by other means. Once inside the cell, the foreign DNA can recombine with the bacterial chromosome, leading to genetic diversity.
2. Conjugation: Conjugation involves the direct transfer of genetic material from one bacterium to another through physical contact. This transfer typically occurs through a conjugative pilus, a structure that forms a bridge between the donor and recipient cells. During conjugation, a copy of the donor's DNA, often in the form of a plasmid, is transferred to the recipient cell, where it can integrate into the chromosome or exist as a separate genetic element.
3. Transduction: Transduction occurs when bacterial DNA is transferred from one bacterium to another by a bacteriophage (a virus that infects bacteria). During the lytic cycle of viral replication, some bacteriophages may accidentally package bacterial DNA instead of their own genome. When these phages infect other bacteria, they inject this bacterial DNA into the new host, where it can recombine with the recipient chromosome.
Recombination plays a crucial role in bacterial evolution and adaptation by facilitating the exchange of beneficial genetic traits, such as antibiotic resistance or metabolic capabilities. It contributes to the genetic diversity of bacterial populations and can influence their ability to survive and thrive in various environments.In enzymes, a domain refers to a distinct structural and functional unit that contributes to the overall activity of the enzyme. Domains are often modular components within the enzyme's overall structure, each with its own specific role or function. These functions may include substrate binding, catalysis, regulation, or interaction with other molecules. Domains can have specific shapes, surface properties, and active sites tailored to perform particular biochemical tasks within the enzyme's catalytic cycle. Additionally, enzymes can contain multiple domains, which may interact with each other to modulate enzyme activity or to confer versatility in substrate recognition and binding. The organization of domains within an enzyme contributes to its overall efficiency and specificity in catalyzing biochemical reactions.Superscalar architecture is a CPU design paradigm that enables the simultaneous execution of multiple instructions within a single clock cycle. This is achieved by having multiple execution units, allowing t
This is the entrance exam paper for ISI MSQE Entrance Exam for the year 2008. Much more information on the ISI MSQE Entrance Exam and ISI MSQE Entrance preparation help available on http://crackdse.com
This document discusses affine functions. It defines affine functions as functions of the form f(x) = ax + b, where a and b are real numbers. It provides examples of linear functions where b = 0, constant functions where a = 0, and the identity function where a = 1 and b = 0. It discusses the angular coefficient a and the linear coefficient b. It explains that the graph of an affine function is a straight line that can be increasing or decreasing. It also discusses finding the zero or root of an affine function and studying the sign of an affine function.
This document provides a probability cheatsheet compiled by William Chen and Joe Blitzstein with contributions from others. It is licensed under CC BY-NC-SA 4.0 and contains information on topics like counting rules, probability definitions, random variables, expectations, independence, and more. The cheatsheet is designed to summarize essential concepts in probability.
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
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2. Boolean Algebra Properties
7/20/2020@kuntaladas
Let X: boolean variable, 0,1: constants
1. X + 0 = X -- Zero Axiom
2. X • 1 = X -- Unit Axiom
3. X + 1 = 1 -- Unit Property
4. X • 0 = 0 -- Zero Property
PJF - 2
3. Boolean Algebra Properties (cont.)
7/20/2020@kuntaladas
Let X: boolean variable, 0,1: constants
5. X + X = X -- Idepotence
6. X • X = X -- Idepotence
7. X + X’ = 1 -- Complement
8. X • X’ = 0 -- Complement
9. (X’)’ = X -- Involution
PJF - 3
4. Boolean Algebra Properties
7/20/2020@kuntaladas
Let X: boolean variable, 0,1: constants
1. X + 0 = X -- Zero Axiom
2. X • 1 = X -- Unit Axiom
3. X + 1 = 1 -- Unit Property
4. X • 0 = 0 -- Zero Property
PJF - 4
5. Duality
7/20/2020@kuntaladas
The dual of an expression is obtained by
exchanging (• and +), and (1 and 0) in it,
provided that the precedence of operations is not
changed.
Cannot exchange x with x’
Example:
Find H(x,y,z), the dual of F(x,y,z) = x’yz’ + x’y’z
H = (x’+y+z’) (x’+y’+ z)
PJF - 5
6. Duality (cont’d)
7/20/2020@kuntaladas
With respect to duality, Identities 1 – 8
have the following relationship:
1. X + 0 = X 2. X • 1 = X (dual of 1)
3. X + 1 = 1 4. X • 0 = 0 (dual of 3)
5. X + X = X 6. X • X = X (dual of 5)
7. X + X’ = 1 8. X • X’ = 0 (dual of 8)
PJF - 6
7. More Boolean Algebra
Properties
7/20/2020@kuntaladas
Let X,Y, and Z: boolean variables
10. X + Y = Y + X 11. X • Y = Y • X -- Commutative
12. X + (Y+Z) = (X+Y) + Z 13. X•(Y•Z) = (X•Y)•Z -- Associative
14. X•(Y+Z) = X•Y + X•Z 15. X+(Y•Z) = (X+Y) • (X+Z)
-- Distributive
16. (X + Y)’ = X’ • Y’ 17. (X • Y)’ = X’ + Y’ -- DeMorgan’s
In general,
( X1 + X2 + … + Xn )’ = X1’•X2’ • … •Xn’, and
( X1•X2•… •Xn )’ = X1’ + X2’ + … + Xn’
PJF - 7
9. Power of Duality
7/20/2020@kuntaladas
1. x + x•y = x is true, so (x + x•y)’=x’
2. (x + x•y)’=x’•(x’+y’)
3. x’•(x’+y’) =x’
4. Let X=x’, Y=y’
5. X•(X+Y) =X, which is the dual of x + x•y = x.
6. The above process can be applied to any
formula. So if a formula is valid, then its dual
must also be valid.
7. Proving one formula also proves its dual.
PJF - 9
11. Truth Tables (revisited)
7/20/2020@kuntaladas
Enumerates all possible
combinations of variable values
and the corresponding function
value
Truth tables for some arbitrary
functions
F1(x,y,z), F2(x,y,z), and F3(x,y,z)
are shown to the right.
x y z F1 F2 F3
0 0 0 0 1 1
0 0 1 0 0 1
0 1 0 0 0 1
0 1 1 0 1 1
1 0 0 0 1 0
1 0 1 0 1 0
1 1 0 0 0 0
1 1 1 1 0 1
PJF - 11
12. Truth Tables (cont.)
7/20/2020@kuntaladas
Truth table: a unique representation of a
Boolean function
If two functions have identical truth tables, the
functions are equivalent (and vice-versa).
Truth tables can be used to prove equality
theorems.
However, the size of a truth table grows
exponentially with the number of variables
involved, hence unwieldy. This motivates the
use of Boolean Algebra.
PJF - 12
13. Boolean expressions-NOT
unique
7/20/2020@kuntaladas
Unlike truth tables, expressions
representing a Boolean function are
NOT unique.
Example:
F(x,y,z) = x’•y’•z’ + x’•y•z’ + x•y•z’
G(x,y,z) = x’•y’•z’ + y•z’
The corresponding truth tables for F()
and G() are to the right. They are
identical.
Thus, F() = G()
x y z F G
0 0 0 1 1
0 0 1 0 0
0 1 0 1 1
0 1 1 0 0
1 0 0 0 0
1 0 1 0 0
1 1 0 1 1
1 1 1 0 0
PJF - 13
14. Algebraic Manipulation
7/20/2020@kuntaladas
Boolean algebra is a useful tool for simplifying
digital circuits.
Why do it? Simpler can mean cheaper,
smaller, faster.
Example: Simplify F = x’yz + x’yz’ + xz.
F = x’yz + x’yz’ + xz
= x’y(z+z’) + xz
= x’y•1 + xz
= x’y + xz
PJF - 14
16. Complement of a Function
7/20/2020@kuntaladas
The complement of a function is derived by
interchanging (• and +), and (1 and 0), and
complementing each variable.
Otherwise, interchange 1s to 0s in the truth
table column showing F.
The complement of a function IS NOT THE
SAME as the dual of a function.
PJF - 16
17. Complementation: Example
7/20/2020@kuntaladas
Find G(x,y,z), the complement of
F(x,y,z) = xy’z’ + x’yz
G = F’ = (xy’z’ + x’yz)’
= (xy’z’)’ • (x’yz)’ DeMorgan
= (x’+y+z) • (x+y’+z’) DeMorgan again
Note: The complement of a function can also be
derived by finding the function’s dual, and then
complementing all of the literals
PJF - 17
18. Canonical and Standard Forms
7/20/2020@kuntaladas
We need to consider formal techniques for the
simplification of Boolean functions.
Identical functions will have exactly the same
canonical form.
Minterms and Maxterms
Sum-of-Minterms and Product-of- Maxterms
Product and Sum terms
Sum-of-Products (SOP) and Product-of-Sums (POS)
PJF - 18
19. Complement of a Function
7/20/2020@kuntaladas
The complement of a function is derived by
interchanging (• and +), and (1 and 0), and
complementing each variable.
Otherwise, interchange 1s to 0s in the truth
table column showing F.
The complement of a function IS NOT THE
SAME as the dual of a function.
PJF - 19
20. Complementation: Example
7/20/2020@kuntaladas
Find G(x,y,z), the complement of
F(x,y,z) = xy’z’ + x’yz
G = F’ = (xy’z’ + x’yz)’
= (xy’z’)’ • (x’yz)’ DeMorgan
= (x’+y+z) • (x+y’+z’) DeMorgan again
Note: The complement of a function can also be
derived by finding the function’s dual, and then
complementing all of the literals
PJF - 20
21. Canonical and Standard Forms
7/20/2020@kuntaladas
We need to consider formal techniques for the
simplification of Boolean functions.
Identical functions will have exactly the same
canonical form.
Minterms and Maxterms
Sum-of-Minterms and Product-of- Maxterms
Product and Sum terms
Sum-of-Products (SOP) and Product-of-Sums (POS)
PJF - 21
22. Definitions
7/20/2020@kuntaladas
Literal: A variable or its complement
Product term: literals connected by •
Sum term: literals connected by +
Minterm: a product term in which all the
variables appear exactly once, either
complemented or uncomplemented
Maxterm: a sum term in which all the variables
appear exactly once, either complemented or
uncomplemented
PJF - 22
23. 7/20/2020@kuntaladas
Sum of products(SOP): The logical sum of two
or more logical product terms is called a SOP
expression.
Y=AB+BC+DA
Product of Sums(POS): A product of sums
expression is a logical product of two or more
logical terms.
Y=(A+B)(C+D)
PJF - 23
24. Minterm
7/20/2020@kuntaladas
Represents exactly one combination in the truth
table.
Denoted by mj, where j is the decimal equivalent
of the minterm’s corresponding binary
combination (bj).
A variable in mj is complemented if its value in bj
is 0, otherwise is uncomplemented.
Example: Assume 3 variables (A,B,C), and j=3.
Then, bj = 011 and its corresponding minterm is
denoted by mj = A’BC
PJF - 24
25. Maxterm
7/20/2020@kuntaladas
Represents exactly one combination in the truth
table.
Denoted by Mj, where j is the decimal equivalent
of the maxterm’s corresponding binary
combination (bj).
A variable in Mj is complemented if its value in bj
is 1, otherwise is uncomplemented.
Example: Assume 3 variables (A,B,C), and j=3.
Then, bj = 011 and its corresponding maxterm is
denoted by Mj = A+B’+C’
PJF - 25
26. Truth Table notation for Minterms
and Maxterms
7/20/2020@kuntaladas
Minterms and
Maxterms are easy
to denote using a
truth table.
Example:
Assume 3 variables
x,y,z
(order is fixed)
x y z Minterm Maxterm
0 0 0 x’y’z’ = m0 x+y+z = M0
0 0 1 x’y’z = m1 x+y+z’ = M1
0 1 0 x’yz’ = m2 x+y’+z = M2
0 1 1 x’yz = m3 x+y’+z’= M3
1 0 0 xy’z’ = m4 x’+y+z = M4
1 0 1 xy’z = m5 x’+y+z’ = M5
1 1 0 xyz’ = m6 x’+y’+z = M6
1 1 1 xyz = m7 x’+y’+z’ = M7
PJF - 26
27. Canonical Forms (Unique)
7/20/2020@kuntaladas
Any Boolean function F( ) can be expressed as
a unique sum of minterms and a unique
product of maxterms (under a fixed variable
ordering).
In other words, every function F() has two
canonical forms:
Canonical Sum-Of-Products (sum of minterms)
Canonical Product-Of-Sums (product of
maxterms)
PJF - 27
28. Canonical Forms (Unique)
7/20/2020@kuntaladas
A minterm gives the value 1
for exactly 1 combination of
variables.
The sum of all minterms of ‘f’
for which ‘f’ assumes ‘1’ is
called canonical sum of
product.
A maxterm gives the value 0
for exactly 1 combination of
variables.
The product of all maxterms of
‘f’ for which ‘f’ assumes ‘0’ is
called canonical products of
sums.
a b c f1
0 0 0 0
0 0 1 1
0 1 0 1
0 1 1 0
1 0 0 1
1 0 1 0
1 1 0 1
1 1 1 0
PJF - 28
29. Example
7/20/2020@kuntaladas
Truth table for f1(a,b,c) at right
The canonical sum-of-products form for
f1 is
f1(a,b,c) = m1 + m2 + m4 + m6
= a’b’c + a’bc’ + ab’c’ + abc’
The canonical product-of-sums form for
f1 is
f1(a,b,c) = M0 • M3 • M5 • M7
= (a+b+c)•(a+b’+c’)•
(a’+b+c’)•(a’+b’+c’).
Observe that: mj = Mj’
For minterm ‘0’ means complemented
form and for maxterm ‘0’ means true
a b c f1
0 0 0 0
0 0 1 1
0 1 0 1
0 1 1 0
1 0 0 1
1 0 1 0
1 1 0 1
1 1 1 0
PJF - 29
30. Shorthand: ∑ and ∏
7/20/2020@kuntaladas
• f1(a,b,c) = ∑ m(1,2,4,6), where ∑ indicates that
this is a sum-of-products form, and m(1,2,4,6)
indicates that the minterms to be included are m1,
m2, m4, and m6.
• f1(a,b,c) = ∏ M(0,3,5,7), where ∏ indicates that
this is a product-of-sums form, and M(0,3,5,7)
indicates that the maxterms to be included are
M0, M3, M5, and M7.
• Since mj = Mj’ for any j,
∑ m(1,2,4,6) = ∏ M(0,3,5,7) = f1(a,b,c)
PJF - 30
31. Conversion Between Canonical Forms
7/20/2020@kuntaladas
Replace ∑ with ∏ (or vice versa) and replace those j’s
that appeared in the original form with those that do not.
Example:
f1(a,b,c) = a’b’c + a’bc’ + ab’c’ + abc’
= m1 + m2 + m4 + m6
= ∑(1,2,4,6)
= ∏(0,3,5,7)
= (a+b+c)•(a+b’+c’)•(a’+b+c’)•(a’+b’+c’)
PJF - 31
32. Standard Forms (NOT Unique)
7/20/2020@kuntaladas
• Standard forms are “like” canonical forms,
except that not all variables need appear in the
individual product (SOP) or sum (POS) terms.
• Example:
f1(a,b,c) = a’b’c + bc’ + ac’
is a standard sum-of-products form
• f1(a,b,c) = (a+b+c)•(b’+c’)•(a’+c’)
is a standard product-of-sums form.
PJF - 32
33. Conversion of SOP from standard
to canonical form
7/20/2020@kuntaladas
Expand non-canonical terms by inserting
equivalent of 1 in each missing variable x:
(x + x’) = 1
Remove duplicate minterms
f1(a,b,c) = a’b’c + bc’ + ac’
= a’b’c + (a+a’)bc’ + a(b+b’)c’
= a’b’c + abc’ + a’bc’ + abc’ + ab’c’
= a’b’c + abc’ + a’bc + ab’c’
PJF - 33
34. Conversion of POS from standard
to canonical form
7/20/2020@kuntaladas
Expand noncanonical terms by adding 0 in terms
of missing variables (e.g., xx’ = 0) and using the
distributive law
Remove duplicate maxterms
f1(a,b,c) = (a+b+c)•(b’+c’)•(a’+c’)
= (a+b+c)•(aa’+b’+c’)•(a’+bb’+c’)
= (a+b+c)•(a+b’+c’)•(a’+b’+c’)•
(a’+b+c’)•(a’+b’+c’)
= (a+b+c)•(a+b’+c’)•(a’+b’+c’)•(a’+b+c’)
PJF - 34