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Free Download Probability Distribution Models Published 10/2024 Created by Robert (Bob) Steele MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English | Duration: 12 Lectures ( 3h 8m ) | Size: 2.27 GB Mastering the Language of Data: From Distributions to Predictive Models What you'll learnIdentify various data distributions by examining the shape, center, and spread of datasets in real-world scenarios. Explain the significance of different data shapes, including symmetric, skewed, and bimodal distributions, in various contexts. Classify different types of distributions such as Uniform, Poisson, Exponential, and Binomial through theoretical understanding and practical examples. Analyze datasets to determine the appropriate mathematical models and describe their underlying patterns and behaviors. Compare the characteristics of different data distributions and their implications in quantitative analysis. Apply mathematical models to perform quantitative analysis, make predictions, and understand phenomena governing data in real-life situations. Evaluate the accuracy and relevance of different statistical models in the context of real-world applications, such as predicting sales outcomes or analyzing tr Create visual and verbal presentations of data analysis results, demonstrating a thorough understanding of data shapes and mathematical models. RequirementsBasic Mathematical Skills: Familiarity with fundamental mathematical concepts, including basic arithmetic, algebra, and probability. Introductory Statistics: A foundational understanding of basic statistical concepts, such as mean, median, mode, and standard deviation. Analytical Thinking: An ability to engage in logical reasoning and problem-solving to analyze data and interpret results. Computer Literacy: Basic proficiency in using a computer, including the ability to navigate and utilize software tools for data analysis. Interest in Data Analysis: A keen interest in understanding and working with data, as well as a desire to learn about statistical analysis and mathematical modeling. Access to a Computer: Parti[beeep]nts should have access to a computer for completing course exercises and assignments. DescriptionWelcome to a journey through the fascinating world of data shapes and mathematical models! In this course, we will embark on a deep dive into the three pivotal pillars of statistical data analysis: Shape, Center, and Spread, unraveling the mysteries behind diverse data distributions.Starting with the Shape of Data, we will explore how data can be represented through various distributions, emphasizing the significance of recognizing and understanding different data shapes in real-world scenarios. Take the corporate world, for example, where salaries often follow a skewed distribution, or the predictable intervals of atom decay, each presenting unique characteristic distributions. Through practical examples and interactive sessions, we will identify and analyze single-peaked histograms, symmetric, skewed, and bimodal distributions, gaining insights into the intrinsic patterns and behaviors of different datasets.Diving deeper, we will introduce and demystify a range of Mathematical Descriptions of Data Shapes. From the simplicity of Uniform Distributions, seen in rolling a fair die, to the complexity of Poisson Distributions, representing events in fixed intervals, we will traverse the landscape of Exponential and Binomial Distributions, uncovering the intricacies of these mathematical models. Each session will be filled with real-life examples, hands-on exercises, and discussions, ensuring that you not only grasp the theoretical aspects but also develop a practical understanding of these concepts.Our journey does not stop at mere identification and description; we delve into the Importance of Mathematical Models, unraveling how they empower us to perform quantitative analysis, make accurate predictions, and gain a profound understanding of the underlying phenomena governing the data. Whether it's predicting sales outcomes, analyzing traffic patterns, or exploring natural occurrences, you will learn to apply these models confidently and accurately.In conclusion, this course is designed to transform your perspective on data, equipping you with the knowledge and skills to analyze, describe, and predict with precision. Whether you are a student stepping into the world of statistics, a professional looking to sharpen your data analysis skills, or simply a data enthusiast eager to understand the language of numbers, this course is your gateway to mastering the art of deciphering data.Join us on this exhilarating adventure through the world of data shapes and mathematical models, and emerge with the tools and confidence to conquer the realm of statistical analysis! Who this course is forStudents: Those stepping into the world of statistics or pursuing degrees in fields such as mathematics, data science, economics, engineering, or social sciences. Professionals: Individuals working in fields where data analysis is essential, such as marketing, finance, business analytics, healthcare, and research, looking to sharpen their data analysis skills. Data Enthusiasts: Anyone with a keen interest in understanding and interpreting data, regardless of their professional background. Researchers: Academics and scientists who need to analyze data as part of their research work and wish to deepen their understanding of statistical methods. Educators: Teachers and instructors who teach statistics or data analysis and want to enhance their teaching methodologies with a deeper understanding of data shapes and mathematical models. Career Changers: Individuals looking to transition into data-related roles and seeking to build a solid foundation in statistical data analysis. Decision Makers: Managers and executives who want to leverage data-driven insights to inform business strategies and decisions. 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Free Download Foundations of Probability and Statistics Published 10/2024 Created by EDUCBA Bridging the Gap MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English | Duration: 15 Lectures ( 1h 40m ) | Size: 593 MB Unlock the power of data with our comprehensive course on probability and statistics for effective data analysis! What you'll learn Understanding of Random Variables: Students will grasp the concept of random variables and their significance in probability theory. Probability Distributions: They will explore various probability distributions, including discrete and continuous distributions. Basic Probability Concepts: Students will learn the fundamentals of probability, including mutually exclusive events, independent events. Statistical Measures: The course will cover essential statistical measures, including mean, variance, and standard deviation, enabling students to summarize. Correlation and Covariance: Students will understand the relationships between variables through correlation and covariance, equipping them with tools. Moments and Skewness: They will learn about central moments and the characteristics of distributions, such as positive skewness. Estimation Techniques: The course will introduce the concept of the best linear unbiased estimator (BLUE) and its application in statistical analysis. Requirements Basic Mathematical Skills: Students should have a fundamental understanding of algebra and basic mathematical operations, as these are essential for grasping statistical concepts. High School Level Mathematics: Familiarity with high school-level mathematics, including functions, equations, and basic graphing, is recommended. Analytical Thinking: Students should possess strong analytical and critical thinking skills to interpret data and statistical results effectively. No Prior Statistics Knowledge Required: While prior knowledge of statistics is not mandatory, a willingness to engage with new concepts and a desire to learn are essential. Description Welcome to "Foundations of Probability and Statistics," a course tailored for aspiring data analysts, researchers, and anyone interested in understanding the core concepts of probability and statistics. In this course, you will explore the fundamental principles that govern random phenomena and the statistical techniques used to analyze and interpret data. Whether you are starting your journey in data science or seeking to enhance your analytical skills, this course provides a solid foundation in probability and statistics, setting you on the path to making informed decisions based on data.Section 1: Introduction to ProbabilityIn the first section, we lay the groundwork for understanding probability. You will begin with an introduction to random variables, which are the building blocks of probability theory, allowing you to quantify uncertainty. Next, we will explore probability distributions, which describe how probabilities are assigned to different outcomes. To solidify your understanding, we'll examine an engaging example of rolling two dice to illustrate the concepts in action. You will also learn the fundamental principles of probability itself, including mutually exclusive events, where the occurrence of one event precludes the occurrence of another. The section wraps up with an introduction to contingency tables and independent events, providing you with a comprehensive overview of probability concepts and their applications.Section 2: Basic StatisticsTransitioning to statistics, this section covers essential statistical concepts that will enhance your data analysis skills. We begin with an overview of basic statistics, followed by an in-depth exploration of mean and variance-two critical measures that summarize data sets. You will learn how to calculate standard deviation, providing insights into the dispersion of data points around the mean. Furthermore, we will delve into correlation and covariance, allowing you to understand relationships between variables. The section continues with a discussion on central moments, offering insights into the shape of data distributions, and concludes with the concept of positive skewed distribution and the best linear unbiased estimator, empowering you with the tools to analyze and interpret data effectively.Conclusion:By the end of this course, you will have a solid understanding of the key concepts in probability and statistics. You will be equipped with the analytical skills necessary to tackle real-world problems and make data-driven decisions. Whether you are looking to advance your career in data analysis or simply enhance your quantitative skills, "Foundations of Probability and Statistics" provides the knowledge and tools you need to succeed in a data-centric world. Join us on this exciting journey into the world of data analysis! Who this course is for Students in Related Fields: Individuals pursuing degrees in fields such as mathematics, statistics, data science, engineering, economics, or social sciences who need a solid foundation in probability and statistics. Professionals Seeking Data Literacy: Professionals from various industries looking to enhance their data analysis skills and improve their decision-making abilities through a better understanding of statistical principles. Lifelong Learners: Anyone interested in learning about probability and statistics for personal growth, including those preparing for advanced studies or certifications that require statistical knowledge. Individuals New to Statistics: Beginners with little to no prior knowledge of statistics who want to build a strong base in the subject to support further study or practical application in their careers. Homepage https://www.udemy.com/course/foundations-of-probability-and-statistics/ Screenshot Rapidgator https://rg.to/file/30813cf95f921c06cfce5059f59edea5/hmjmn.Foundations.of.Probability.and.Statistics.rar.html Fikper Free Download https://fikper.com/mVJw6x5ezj/hmjmn.Foundations.of.Probability.and.Statistics.rar.html No Password - Links are Interchangeable

Free Download Math 0-1 Probability for Data Science & Machine Learning Published 9/2024 Created by Lazy Programmer Team,Lazy Programmer Inc. MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English | Duration: 94 Lectures ( 17h 30m ) | Size: 7.62 GB A Casual Guide for Artificial Intelligence, Deep Learning, and Python Programmers What you'll learn: Conditional probability, Independence, and Bayes' Rule Use of Venn diagrams and probability trees to visualize probability problems Discrete random variables and distributions: Bernoulli, categorical, binomial, geometric, Poisson Continuous random variables and distributions: uniform, exponential, normal (Gaussian), Laplace, Gamma, Beta Cumulative distribution functions (CDFs), probability mass functions (PMFs), probability density functions (PDFs) Joint, marginal, and conditional distributions Multivariate distributions, random vectors Functions of random variables, sums of random variables, convolution Expected values, expectation, mean, and variance Skewness, kurtosis, and moments Covariance and correlation, covariance matrix, correlation matrix Moment generating functions (MGF) and characteristic functions Key inequalities like Markov, Chebyshev, Cauchy-Schwartz, Jensen Convergence in probability, convergence in distribution, almost sure convergence Law of large numbers and the Central Limit Theorem (CLT) Applications of probability in machine learning, data science, and reinforcement learning Requirements: College / University-level Calculus (for most parts of the course) College / University-level Linear Algebra (for some parts of the course) Description: Common scenario: You try to get into machine learning and data science, but there's SO MUCH MATH.Either you never studied this math, or you studied it so long ago you've forgotten it all.What do you do?Well my friends, that is why I created this course.Probability is one of the most important math prerequisites for data science and machine learning. It's required to understand essentially everything we do, from the latest LLMs like ChatGPT, to diffusion models like Stable Diffusion and Midjourney, to statistics (what I like to call "probability part 2").Markov chains, an important concept in probability, form the basis of popular models like the Hidden Markov Model (with applications in speech recognition, DNA analysis, and stock trading) and the Markov Decision Process or MDP (the basis for Reinforcement Learning).Machine learning (statistical learning) itself has a probabilistic foundation. Specific models, like Linear Regression, K-Means Clustering, Prin[beeep]l Components Analysis, and Neural Networks, all make use of probability.In short, probability cannot be avoided!If you want to do machine learning beyond just copying library code from blogs and tutorials, you must know probability.This course will cover everything that you'd learn (and maybe a bit more) in an undergraduate-level probability class. This includes random variables and random vectors, discrete and continuous probability distributions, functions of random variables, multivariate distributions, expectation, generating functions, the law of large numbers, and the central limit theorem.Most important theorems will be derived from scratch. Don't worry, as long as you meet the prerequisites, they won't be difficult to understand. This will ensure you have the strongest foundation possible in this subject. No more memorizing "rules" only to apply them incorrectly / inappropriately in the future! This course will provide you with a deep understanding of probability so that you can apply it correctly and effectively in data science, machine learning, and beyond.Are you ready?Let's go!Suggested prerequisites:Differential calculus, integral calculus, and vector calculusLinear algebraGeneral comfort with university/collegelevel mathematics Who this course is for: Python developers and software developers curious about Data Science Professionals interested in Machine Learning and Data Science but haven't studied college-level math Students interested in ML and AI but find they can't keep up with the math Former STEM students who want to brush up on probability before learning about artificial intelligence Homepage https://www.udemy.com/course/probability-data-science-machine-learning/ Rapidgator https://rg.to/file/32b6b9086c50a019fba6df0e902c5fbc/nmmkg.Math.01.Probability.for.Data.Science..Machine.Learning.part5.rar.html https://rg.to/file/41154f19b9beb47677595b4c02d06ef0/nmmkg.Math.01.Probability.for.Data.Science..Machine.Learning.part4.rar.html https://rg.to/file/7b80052282597412fce1092da6ad6e5b/nmmkg.Math.01.Probability.for.Data.Science..Machine.Learning.part3.rar.html https://rg.to/file/8029dad93bccbf17819b32d783cedeb6/nmmkg.Math.01.Probability.for.Data.Science..Machine.Learning.part8.rar.html https://rg.to/file/afc6d03ec267b0c96440630d43977338/nmmkg.Math.01.Probability.for.Data.Science..Machine.Learning.part7.rar.html https://rg.to/file/b02a8e41004d037ba1af051f7e6ddeec/nmmkg.Math.01.Probability.for.Data.Science..Machine.Learning.part1.rar.html https://rg.to/file/df9764881658d8feef6afd16c2e33f18/nmmkg.Math.01.Probability.for.Data.Science..Machine.Learning.part2.rar.html https://rg.to/file/e33ef1f15da383173e5f374d6c05ddf4/nmmkg.Math.01.Probability.for.Data.Science..Machine.Learning.part6.rar.html Fikper Free Download https://fikper.com/8XlFT98L5F/nmmkg.Math.01.Probability.for.Data.Science..Machine.Learning.part2.rar.html https://fikper.com/AbcgkDYJ3P/nmmkg.Math.01.Probability.for.Data.Science..Machine.Learning.part4.rar.html https://fikper.com/FXMd2RPwEt/nmmkg.Math.01.Probability.for.Data.Science..Machine.Learning.part7.rar.html https://fikper.com/HAFsth9Luc/nmmkg.Math.01.Probability.for.Data.Science..Machine.Learning.part1.rar.html https://fikper.com/MW8cOocvWR/nmmkg.Math.01.Probability.for.Data.Science..Machine.Learning.part5.rar.html https://fikper.com/Pg9pHKbIHN/nmmkg.Math.01.Probability.for.Data.Science..Machine.Learning.part6.rar.html https://fikper.com/Y8W4uDUZb8/nmmkg.Math.01.Probability.for.Data.Science..Machine.Learning.part8.rar.html https://fikper.com/pS1ivjLw3K/nmmkg.Math.01.Probability.for.Data.Science..Machine.Learning.part3.rar.html No Password - Links are Interchangeable

Free Download Probability-The Engine of Inference Published 9/2024 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Language: English | Duration: 3h 37m | Size: 2.19 GB Mastering Uncertainty and Decision-Making through Probability and Statistics What you'll learn Define key concepts of probability, including randomness, sample space, and event probabilities. Explain the relationship between probability and statistics and how probability is used to make inferences about populations. Apply probability principles to solve real-world problems, such as calculating expected outcomes and making decisions under uncertainty. Analyze situations involving risk and uncertainty, and determine the likelihood of various outcomes. Evaluate different probabilistic models and assess the validity of inductive reasoning in various contexts. Create structured decision-making processes that incorporate probability to manage uncertainty in practical scenarios. Requirements Basic Math Skills: Students should have a foundational understanding of algebra, including working with fractions, percentages, and solving simple equations. Logical Thinking and Problem-Solving Skills: Students should be comfortable analyzing problems and thinking critically to apply mathematical concepts to real-world scenarios. Interest in Data and Decision-Making: A curiosity about how probability and statistics apply to everyday situations and decision-making processes will enhance the learning experience. Description Dive into the world of probability and statistics in this dynamic course that explores the mathematical foundations behind decision-making under uncertainty. Beginning with the basics, we define probability as a measure of likelihood and its relationship to statistics. The course delves into key concepts like inductive reasoning, where generalizations are made from specific data points, and introduces probability as the driving force behind statistical inferences.Through engaging with real-world examples and thought experiments, students will uncover how our intuition can mislead us in situations involving randomness and learn the significance of experiments and trials in estimating probabilities. The course also introduces Pascal's Wager, using it as a springboard to explore expected value, and guides students through concepts such as complementary, independent, and mutually exclusive events.A major focus is placed on understanding sample spaces and event probabilities to predict outcomes, and the role of the expected value in evaluating risks. Finally, the course explores the central limit theorem, showing why larger sample sizes yield more reliable results in both theory and practice. By the end of the course, students will be equipped with the tools and confidence to apply probabilistic thinking and statistical analysis to complex, real-life problems, offering a fresh perspective on how numbers drive the future. This course is ideal for those seeking to understand how probability informs decisions in everything from games of chance to critical life choices. Who this course is for Students and Lifelong Learners: Anyone curious about understanding how probability and statistics shape decision-making and predictions in everyday life and various fields. Professionals in Data-Driven Fields: Individuals working in industries such as finance, business, economics, marketing, or healthcare who want to improve their ability to assess risk, forecast outcomes, and make data-driven decisions. Aspiring Data Scientists and Analysts: Those looking to build a strong foundation in probability as a key component of statistical analysis, machine learning, and data science. Educators and Teachers: Teachers and educators who want to deepen their understanding of probability to improve their teaching methods and explain complex concepts to students. General Enthusiasts: People interested in mathematics, logic, and problem-solving who want to explore the fascinating world of probability and how it informs everything from games of chance to critical decisions. Homepage https://www.udemy.com/course/probability-the-engine-of-inference/ Rapidgator https://rg.to/file/cf648e341fdf6bd0b78dcd74f16d4439/ssfbw.ProbabilityThe.Engine.of.Inference.part1.rar.html https://rg.to/file/7da083bce6fac4e191048e4336e54c47/ssfbw.ProbabilityThe.Engine.of.Inference.part2.rar.html https://rg.to/file/d9b3cf4f847df610aa8d2e24e1bb1cdb/ssfbw.ProbabilityThe.Engine.of.Inference.part3.rar.html Fikper Free Download https://fikper.com/emfvdecLBn/ssfbw.ProbabilityThe.Engine.of.Inference.part1.rar.html https://fikper.com/XgwtQ3kmDs/ssfbw.ProbabilityThe.Engine.of.Inference.part2.rar.html https://fikper.com/EvAECCCsQt/ssfbw.ProbabilityThe.Engine.of.Inference.part3.rar.html No Password - Links are Interchangeable

pdf | 22.36 MB | English| Isbn:9781467244824 | Author: CTI Reviews, Douglas Montgomery | Year: 2016 Description: Category:Education, Education - General & Miscellaneous, Education - Miscellaneous Topics https://rapidgator.net/file/ae7496a14bb67db4968d7d36a1cdb856/ https://nitroflare.com/view/81B6E8FEDCE2E10/

pdf | 20.17 MB | English| Isbn:9783110562606 | Author: Arak M. Mathai, Hans J. Haubold | Year: 2017 Description: Category:Science & Technology, Mathematics, Probability Theory, Statistics https://rapidgator.net/file/4da21a2e4c31bea6a44f8a9188785586/ https://nitroflare.com/view/FCAD57A858679FE/

Math Tutor DVD - Mastering Statistics: Vol 2 - Probability Distributions | 549.3 MB Genre: E-Learning Statistics is one of the most important areas of Math to understand. It has applications in science, engineering, business, economics, political science, and more. In this 4 Hour Course, Jason Gibson teaches the fundamental concepts needed to truly Master Statistics with step-by-step video tutorials. The lessons begin with fundamental definitions of statistics such as probability, distribution, z-score, normal table, and more. Next, we introduce the concept of a discrete probability distribution and contrast this with a continuous distribution. Finally, we will spend considerable time on the Normal Probability distribution, which is the most important distribution in all of statistics. We will learn how to solve practice statistical problems using the normal distribution and the z-score table. Statistics is a difficult subject for most students, but anyone can Master Statistics with our step-by-step teaching style! Course Contents: Sect 1: Intro to this Course Sect 2: Random Variables and Discrete Probability Distributions Sect 3: The Normal Probability Distribution Sect 4: Properties of the Normal Distribution Sect 5: The Area Under the Normal Distribution Sect 6: The Standard Normal Distribution Sect 7: Practice with the Standard Normal Distribution Sect 8: Using a Z-Chart Table, Part 1 Sect 9: Using a Z-Chart Table, Part 2 Sect 10: Using a Z-Chart Table, Part 3 Sect 11: Using a Z-Chart Table, Part 4 Sect 12: Finding Probability using a Normal Distribution, Part 1 Sect 13: Finding Probability using a Normal Distribution, Part 2 Sect 14: Finding Probability using a Normal Distribution, Part 3 Sect 15: Finding Probability using a Normal Distribution, Part 4 Sect 16: Finding Probability using a Normal Distribution, Part 5 Sect 17: Finding Z-Values with a Normal Distribution, Part 1 Sect 18: Finding Z-Values with a Normal Distribution, Part 2 Sect 19: Finding Z-Values with a Normal Distribution, Part 3 Sect 20: Finding Z-Values with a Normal Distribution, Part 4 http://www.nitroflare.com/view/7AEA90D9415C12C/Mastering.Statistics.Vol.2.Probability.Distributions.part1.rar http://www.nitroflare.com/view/F7EBDE63390EEF1/Mastering.Statistics.Vol.2.Probability.Distributions.part2.rar http://www.nitroflare.com/view/17976190650BD8D/Mastering.Statistics.Vol.2.Probability.Distributions.part3.rar http://www.nitroflare.com/view/C1B732CB482B606/Mastering.Statistics.Vol.2.Probability.Distributions.part4.rar http://www.nitroflare.com/view/6962C85048DDF35/Mastering.Statistics.Vol.2.Probability.Distributions.part5.rar http://www.nitroflare.com/view/9AD9FF69D6138FA/Mastering.Statistics.Vol.2.Probability.Distributions.part6.rar http://rapidgator.net/file/fa280a79f154a7740ef4c613d83f5001/Mastering.Statistics.Vol.2.Probability.Distributions.part1.rar.html http://rapidgator.net/file/6d507799505d3a275e1b2f2be3aff46c/Mastering.Statistics.Vol.2.Probability.Distributions.part2.rar.html http://rapidgator.net/file/aef12f7d18ae75c13fe16130b1e75d93/Mastering.Statistics.Vol.2.Probability.Distributions.part3.rar.html http://rapidgator.net/file/22e13bca140191c59d847fcd187a1242/Mastering.Statistics.Vol.2.Probability.Distributions.part4.rar.html http://rapidgator.net/file/502fad10fce8aeec8511f8b1ee6725ea/Mastering.Statistics.Vol.2.Probability.Distributions.part5.rar.html http://rapidgator.net/file/9473f7741270fbf01ab9cd2d206c9f73/Mastering.Statistics.Vol.2.Probability.Distributions.part6.rar.html https://www.uploadable.ch/file/YGpKQDJDGSAB/Mastering.Statistics.Vol.2.Probability.Distributions.part1.rar https://www.uploadable.ch/file/YSagxdksFrNH/Mastering.Statistics.Vol.2.Probability.Distributions.part2.rar https://www.uploadable.ch/file/6T8ExtJsEvVR/Mastering.Statistics.Vol.2.Probability.Distributions.part3.rar https://www.uploadable.ch/file/fNFm6xNBT3XS/Mastering.Statistics.Vol.2.Probability.Distributions.part4.rar https://www.uploadable.ch/file/N4BQkY3sPZEm/Mastering.Statistics.Vol.2.Probability.Distributions.part5.rar https://www.uploadable.ch/file/ngNfMPueZhKX/Mastering.Statistics.Vol.2.Probability.Distributions.part6.rar

What Are the Chances? Probability Made Clear (DVDRip) 14??DVDRip | AVI/XviD, ~1000 kb/s | 720x480 | Duration: 06:11:12 | English: MP3, 192 kb/s (2 ch) | + PDF Guide | 3.12 GB Genre: Probability Life is full of probabilities. Every time you choose something to eat, you deal with probable effects on your health. Every time you drive your car, probability gives a small but measurable chance that you will have an accident. Every time you buy a stock, play poker, or make plans based on a weather forecast, you are consigning your fate to probability. What Are the Chances? Probability Made Clear helps you understand the random factors that lurk behind almost everything-from the chance combinations of genes that produced you to the high odds that the waiting time at a bus stop will be longer than the average time between buses if they operate on a random schedule. In 12 stimulating half-hour lectures, you will explore the fundamental concepts and fascinating applications of probability. High Probability You Will Enjoy This Course Professor Michael Starbird knows the secret of making numbers come alive to non-mathematicians: he picks intriguing, useful, and entertaining examples. Here are some that you will explore in your investigation of probability as a reasoning tool: When did the most recent common ancestor of all humans live? Applying probabilistic methods to the observed mutation rate of human genetic material, scientists have traced our lineage to a female ancestor who lived about 150,000 years ago. How much should you pay for a stock option? Options trading used to be tantamount to gambling until about 1970, when two economists, Fischer Black and Myron Scholes, found a method to quantify those risks and to create a rational model for options pricing. What do you do on third down with long yardage? In football, a pass is the obvious play on third down with many yards to go. Of course, the other team knows that. Probability and game theory help decide when to run with the ball to keep your opponent guessing. What You Will Learn The course literally begins with a roll of the dice, as Professor Starbird demonstrates that games of chance perfectly illustrate the basic prin[beeep]les of probability, including the importance of counting all possible outcomes of any random event. In Lecture 2, you probe the nature of randomness, which is famously symbolized by monkeys randomly hitting typewriter keys and creating Hamlet. In Lecture 3, you explore the concept of expected value, which is the average net loss or gain from performing an experiment or playing a game many times. Then in Lecture 4, you investigate the simple but mathematically fertile idea of the random walk, which may seem like a mindless way of going nowhere but which has important applications in many fields. After this introduction to the key concepts of probability, you delve into the wealth of applications. Lectures 5 and 6 show that randomness and probability are central components of modern scientific descriptions of the world in physics and biology. Lecture 7 looks into the world of finance, particularly probabilistic models of stock and option behavior. Lecture 8 examines unusual applications, including game theory, which is the study of strategic decision-making in games, wars, business, and other areas. Then in Lecture 9 you consider two famous probability puzzles guaranteed to cause a stir: the birthday problem and the Let's Make a DealÂ? Monty Hall question. Finally, Lectures 10-12 cover increasingly sophisticated and surprising results of probabilistic reasoning associated with Bayes theorem. The course concludes with probability paradoxes. Take the Weather Forecasting Challenge One of the most familiar experiences of probability that we have on a daily basis is the weather report, with predictions like, "There is a 30 percent chance of rain tomorrow." But what does that mean? What do you think? Choose one: (a) Rain will occur 30 percent of the day. (b) At a specific point in the forecast area, for example, your house, there is a 30 percent chance of rain occurring. © There is a 30 percent chance that rain will occur somewhere in the forecast area during the day. (d) 30 percent of the forecast area will receive rain, and 70 percent will not. (e) None of the above. In Lecture 5, Dr. Starbird puts this particular forecast under the microscope to demonstrate that probabilistic statements have very precise meanings that can easily be misinterpreted-or misstated. He explains why the answer is (e) and not one of the other choices. He also explains why the official definition from the National Weather Service is subtly but decidedly wrong. He even wagers that within five years the phrasing of the official definition will change because somebody at the National Weather Service will hear this lecture! Games People Play The formal study of probability was born at the dice table. Gambling continues to provide instructive examples of the prin[beeep]les of chance and probability, including: Gambler's ruin: A random walk is a sequence of steps in which the direction of each step is taken at random. In gambling, the phenomenon assures that a bettor who repeatedly plays the same game with even odds will eventually-and invariably-go broke. St. Petersburg paradox: A famous problem in probability involves a hypothetical game supposedly played at a casino in St. Petersburg. Though simple and apparently moderately profitable for the gambler, the expected value of the game is infinite! Yet no reasonable person would pay very much to play it. Why not? Gambler's addiction: Randomness plays a valuable role in reinforcing animal behavior. Changing the reinforcement in an unpredictable, random way leads to behaviors that are retained for a long time, even in the absence of rewards. Applied to humans, this observation may help explain the compulsiveness of some gamblers. Probability to the Rescue One approach to probability, developed by mathematician and Presbyterian minister Thomas Bayes in the 18th century, interprets probability in terms of degrees of belief. As new information becomes available, the calculation of probability changes to take account of the new data. The Bayesian view reflects the reality that we adjust our confidence in our knowledge as we gain evidence. The world of fluctuating probabilities, under continual adjustment as new evidence comes to light, captures the way the world works in realms like medicine, where a physician makes a preliminary diagnosis based on symptoms and probabilities, then orders tests, and then refines the diagnosis based on the test results and a new set of probabilities. If you think about it, it's also the way you work when you're on a jury. At the outset, you have a vague impression of the likelihood of guilt or innocence of the defendant. As evidence mounts, you adjust the relative probabilities you assign to each of these verdicts. You may not do a formal calculation, but your informal procedure is nonetheless Bayesian. Randomness is all around us. "Many or most parts of our lives involve situations where we don't know what's going to happen,"; says Professor Starbird. Probability comes to the rescue to describe what we should expect from randomness. It is a powerful tool for dispelling illusions and uncertainty to help us understand the true odds when we roll the dice in the game of life. Lectures: 1 Our Random World-Probability Defined 2 The Nature of Randomness 3 Expected Value-You Can Bet on It 4 Random Thoughts on Random Walks 5 Probability Phenomena of Physics 6 Probability Is in Our Genes 7 Options and Our Financial Future 8 Probability Where We Don't Expect It 9 Probability Surprises 10 Conundrums of Conditional Probability 11 Believe It or Not-Bayesian Probability 12 Probability Everywhere Download From NitroFlare http://www.nitroflare.com/view/93ED0482004421C/Wh.Are.the.Cha.ctn.part01.rar http://www.nitroflare.com/view/7A75FC929C74B77/Wh.Are.the.Cha.ctn.part02.rar http://www.nitroflare.com/view/01A4594AB2D9A0C/Wh.Are.the.Cha.ctn.part03.rar http://www.nitroflare.com/view/FC3E73F645F6486/Wh.Are.the.Cha.ctn.part04.rar http://www.nitroflare.com/view/7EFB34D035272DC/Wh.Are.the.Cha.ctn.part05.rar http://www.nitroflare.com/view/539AB0D9DAAE8BF/Wh.Are.the.Cha.ctn.part06.rar http://www.nitroflare.com/view/766215EB93A2B00/Wh.Are.the.Cha.ctn.part07.rar http://www.nitroflare.com/view/A91E28D09DB0F29/Wh.Are.the.Cha.ctn.part08.rar Download From Rapidgator http://rapidgator.net/file/db3481a6387c66283629822fe222e7ec/Wh.Are.the.Cha.ctn.part01.rar.html http://rapidgator.net/file/f8a17e7223978099c7fbe2f8ccf96b6f/Wh.Are.the.Cha.ctn.part02.rar.html http://rapidgator.net/file/fb27bdc45986ce2b6f453b02538677ec/Wh.Are.the.Cha.ctn.part03.rar.html http://rapidgator.net/file/df5b87258a6ce2a98dba6218e9485191/Wh.Are.the.Cha.ctn.part04.rar.html http://rapidgator.net/file/892de31eb53eb1f607f0a8e5e7e5b133/Wh.Are.the.Cha.ctn.part05.rar.html http://rapidgator.net/file/99702c31e4ec42f634c632cc0dc49c47/Wh.Are.the.Cha.ctn.part06.rar.html http://rapidgator.net/file/3c11fba4b7f2126480285bb693c412e8/Wh.Are.the.Cha.ctn.part07.rar.html http://rapidgator.net/file/1b5aaa669e892408caa627d813238f01/Wh.Are.the.Cha.ctn.part08.rar.html