Products related to Inference:
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Statistical Inference
This classic textbook builds theoretical statistics from the first principles of probability theory.Starting from the basics of probability, the authors develop the theory of statistical inference using techniques, definitions, and concepts that are statistical and natural extensions, and consequences, of previous concepts.It covers all topics from a standard inference course including: distributions, random variables, data reduction, point estimation, hypothesis testing, and interval estimation. Features The classic graduate-level textbook on statistical inferenceDevelops elements of statistical theory from first principles of probabilityWritten in a lucid style accessible to anyone with some background in calculusCovers all key topics of a standard course in inferenceHundreds of examples throughout to aid understandingEach chapter includes an extensive set of graduated exercisesStatistical Inference, Second Edition is primarily aimed at graduate students of statistics, but can be used by advanced undergraduate students majoring in statistics who have a solid mathematics background.It also stresses the more practical uses of statistical theory, being more concerned with understanding basic statistical concepts and deriving reasonable statistical procedures, while less focused on formal optimality considerations. This is a reprint of the second edition originally published by Cengage Learning, Inc. in 2001.
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Causal Inference
A nontechnical guide to the basic ideas of modern causal inference, with illustrations from health, the economy, and public policy. Which of two antiviral drugs does the most to save people infected with Ebola virus?Does a daily glass of wine prolong or shorten life? Does winning the lottery make you more or less likely to go bankrupt?How do you identify genes that cause disease? Do unions raise wages? Do some antibiotics have lethal side effects? Does the Earned Income Tax Credit help people enter the workforce?Causal Inference provides a brief and nontechnical introduction to randomized experiments, propensity scores, natural experiments, instrumental variables, sensitivity analysis, and quasi-experimental devices.Ideas are illustrated with examples from medicine, epidemiology, economics and business, the social sciences, and public policy.
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Inference and Learning from Data: Volume 2 : Inference
This extraordinary three-volume work, written in an engaging and rigorous style by a world authority in the field, provides an accessible, comprehensive introduction to the full spectrum of mathematical and statistical techniques underpinning contemporary methods in data-driven learning and inference.This second volume, Inference, builds on the foundational topics established in volume I to introduce students to techniques for inferring unknown variables and quantities, including Bayesian inference, Monte Carlo Markov Chain methods, maximum-likelihood estimation, hidden Markov models, Bayesian networks, and reinforcement learning.A consistent structure and pedagogy is employed throughout this volume to reinforce student understanding, with over 350 end-of-chapter problems (including solutions for instructors), 180 solved examples, almost 200 figures, datasets and downloadable Matlab code.Supported by sister volumes Foundations and Learning, and unique in its scale and depth, this textbook sequence is ideal for early-career researchers and graduate students across many courses in signal processing, machine learning, statistical analysis, data science and inference.
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Nonparametric Statistical Inference
Praise for previous editions:"… a classic with a long history." – Statistical Papers"The fact that the first edition of this book was published in 1971 … [is] testimony to the book’s success over a long period." – ISI Short Book Reviews"… one of the best books available for a theory course on nonparametric statistics. … very well written and organized … recommended for teachers and graduate students." – Biometrics"… There is no competitor for this book and its comprehensive development and application of nonparametric methods.Users of one of the earlier editions should certainly consider upgrading to this new edition." – Technometrics"… Useful to students and research workers … a good textbook for a beginning graduate-level course in nonparametric statistics." – Journal of the American Statistical AssociationSince its first publication in 1971, Nonparametric Statistical Inference has been widely regarded as the source for learning about nonparametrics.The Sixth Edition carries on this tradition and incorporates computer solutions based on R.Features Covers the most commonly used nonparametric procedures States the assumptions, develops the theory behind the procedures, and illustrates the techniques using realistic examples from the social, behavioral, and life sciences Presents tests of hypotheses, confidence-interval estimation, sample size determination, power, and comparisons of competing procedures Includes an Appendix of user-friendly tables needed for solutions to all data-oriented examples Gives examples of computer applications based on R, MINITAB, STATXACT, and SAS Lists over 100 new referencesNonparametric Statistical Inference, Sixth Edition, has been thoroughly revised and rewritten to make it more readable and reader-friendly.All of the R solutions are new and make this book much more useful for applications in modern times.It has been updated throughout and contains 100 new citations, including some of the most recent, to make it more current and useful for researchers.
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What is inference in linear regression?
Inference in linear regression refers to the process of drawing conclusions about the relationships between variables based on the estimated coefficients of the regression model. It involves testing hypotheses about the significance of these coefficients and making predictions about the dependent variable. Inference helps us understand the strength and direction of the relationships between the independent and dependent variables, as well as the overall fit of the model to the data. It is an important aspect of linear regression analysis that allows us to make informed decisions and interpretations based on the statistical results.
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What exactly is a mathematical inference in mathematics and computer science?
A mathematical inference in mathematics and computer science is the process of drawing conclusions or making predictions based on existing information or data. In mathematics, this often involves using logical reasoning and mathematical principles to make deductions or prove the validity of a statement. In computer science, mathematical inference can be used in areas such as artificial intelligence and machine learning to make predictions or decisions based on patterns and data. Overall, mathematical inference is a fundamental concept in both fields that allows for the application of logic and reasoning to solve problems and make decisions.
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How are logical inference, the Gentzen calculus, and De Morgan's laws correctly derived?
Logical inference is the process of deriving new information from existing knowledge using valid reasoning. The Gentzen calculus is a formal system for representing and manipulating logical inference in a rigorous way. De Morgan's laws, which describe the relationships between logical conjunction and disjunction, can be correctly derived using the rules of the Gentzen calculus, which ensures that the inference process is sound and valid. By following the rules of the Gentzen calculus, one can systematically derive De Morgan's laws and other logical principles in a mathematically rigorous manner.
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What is a Roman coin?
A Roman coin is a form of currency that was used in the Roman Empire. These coins were typically made of precious metals such as gold, silver, and bronze, and featured images of Roman emperors, gods, and symbols of power. Roman coins were used for trade and commerce throughout the empire and played a significant role in the economy. Today, Roman coins are highly sought after by collectors and historians for their historical and artistic value.
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Causal Inference in Python : Applying Causal Inference in the Tech Industry
How many buyers will an additional dollar of online marketing bring in?Which customers will only buy when given a discount coupon?How do you establish an optimal pricing strategy? The best way to determine how the levers at our disposal affect the business metrics we want to drive is through causal inference. In this book, author Matheus Facure, senior data scientist at Nubank, explains the largely untapped potential of causal inference for estimating impacts and effects.Managers, data scientists, and business analysts will learn classical causal inference methods like randomized control trials (A/B tests), linear regression, propensity score, synthetic controls, and difference-in-differences.Each method is accompanied by an application in the industry to serve as a grounding example. With this book, you will:Learn how to use basic concepts of causal inferenceFrame a business problem as a causal inference problemUnderstand how bias gets in the way of causal inferenceLearn how causal effects can differ from person to personUse repeated observations of the same customers across time to adjust for biasesUnderstand how causal effects differ across geographic locationsExamine noncompliance bias and effect dilution
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Essentials of Statistical Inference
Aimed at advanced undergraduate and graduate students in mathematics and related disciplines, this book presents the concepts and results underlying the Bayesian, frequentist and Fisherian approaches, with particular emphasis on the contrasts between them.Computational ideas are explained, as well as basic mathematical theory.Written in a lucid and informal style, this concise text provides both basic material on the main approaches to inference, as well as more advanced material on developments in statistical theory, including: material on Bayesian computation, such as MCMC, higher-order likelihood theory, predictive inference, bootstrap methods and conditional inference.It contains numerous extended examples of the application of formal inference techniques to real data, as well as historical commentary on the development of the subject.Throughout, the text concentrates on concepts, rather than mathematical detail, while maintaining appropriate levels of formality.Each chapter ends with a set of accessible problems.
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Probability and Statistical Inference
Priced very competitively compared with other textbooks at this level!This gracefully organized textbook reveals the rigorous theory of probability and statistical inference in the style of a tutorial, using worked examples, exercises, numerous figures and tables, and computer simulations to develop and illustrate concepts.Beginning with an introduction to the basic ideas and techniques in probability theory and progressing to more rigorous topics, Probability and Statistical Inferencestudies the Helmert transformation for normal distributions and the waiting time between failures for exponential distributions develops notions of convergence in probability and distribution spotlights the central limit theorem (CLT) for the sample variance introduces sampling distributions and the Cornish-Fisher expansions concentrates on the fundamentals of sufficiency, information, completeness, and ancillarity explains Basu's Theorem as well as location, scale, and location-scale families of distributions covers moment estimators, maximum likelihood estimators (MLE), Rao-Blackwellization, and the Cramér-Rao inequality discusses uniformly minimum variance unbiased estimators (UMVUE) and Lehmann-Scheffé Theorems focuses on the Neyman-Pearson theory of most powerful (MP) and uniformly most powerful (UMP) tests of hypotheses, as well as confidence intervals includes the likelihood ratio (LR) tests for the mean, variance, and correlation coefficient summarizes Bayesian methods describes the monotone likelihood ratio (MLR) property handles variance stabilizing transformations provides a historical context for statistics and statistical discoveries showcases great statisticians through biographical notes Employing over 1400 equations to reinforce its subject matter, Probability and Statistical Inference is a groundbreaking text for first-year graduate and upper-level undergraduate courses in probability and statistical inference who have completed a calculus prerequisite, as well as a supplemental text for classes in Advanced Statistical Inference or Decision Theory.
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Paradoxes in Scientific Inference
Paradoxes are poems of science and philosophy that collectively allow us to address broad multidisciplinary issues within a microcosm.A true paradox is a source of creativity and a concise expression that delivers a profound idea and provokes a wild and endless imagination.The study of paradoxes leads to ultimate clarity and, at the same time, indisputably challenges your mind. Paradoxes in Scientific Inference analyzes paradoxes from many different perspectives: statistics, mathematics, philosophy, science, artificial intelligence, and more.The book elaborates on findings and reaches new and exciting conclusions.It challenges your knowledge, intuition, and conventional wisdom, compelling you to adjust your way of thinking.Ultimately, you will learn effective scientific inference through studying the paradoxes.
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Which Roman coin is this?
The Roman coin in the image appears to be a denarius, which was a silver coin used in ancient Rome. The denarius was first minted in the 3rd century BC and remained in circulation for several centuries. It typically featured the portrait of a Roman emperor on one side and various symbols and inscriptions on the other. The specific emperor and design on the coin would need to be examined to determine its exact identity.
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How can one pay with a collectible coin?
One can pay with a collectible coin by finding a willing buyer who is interested in the specific coin and is willing to exchange goods or services for it. The value of the coin will need to be agreed upon by both parties, and the transaction can be completed by physically exchanging the coin for the agreed-upon value. Alternatively, the coin can be sold to a dealer or collector for its market value, and the proceeds can then be used to make a purchase. It's important to note that some collectible coins may have a higher value as a collector's item than their face value, so it's important to research and understand the value of the coin before attempting to use it as payment.
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What is an ancient Roman coin?
An ancient Roman coin is a form of currency that was minted and used in the Roman Empire during ancient times. These coins were typically made of precious metals such as gold, silver, and bronze, and featured various designs and inscriptions that reflected the political and cultural values of the Roman society. Roman coins were used for everyday transactions, as well as for propaganda purposes by emperors to showcase their power and authority. Today, ancient Roman coins are highly sought after by collectors and historians for their historical significance and artistic value.
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How much is this Roman coin worth?
The value of a Roman coin can vary greatly depending on factors such as its rarity, condition, and historical significance. Some Roman coins can be worth just a few dollars, while others can fetch thousands or even millions of dollars at auction. To determine the specific value of a Roman coin, it is best to consult with a numismatic expert or appraiser who can assess its unique characteristics and provide an accurate valuation.
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