Products related to Inference:
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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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Statistical Inference
This book 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 are natural extensions and consequences of previous concepts.Intended for first-year graduate students, this book can be used for students majoring in statistics who have a solid mathematics background.It can also be used in a way that stresses the more practical uses of statistical theory, being more concerned with understanding basic statistical concepts and deriving reasonable statistical procedures for a variety of situations, and less concerned with formal optimality investigations.
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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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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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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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Why have the bonds in my portfolio, which are securities, lost the most value, even though they are EU government bonds considered safe investment havens?
The value of bonds in your portfolio may have decreased due to changes in interest rates. When interest rates rise, the value of existing bonds decreases because they are paying lower interest rates than newly issued bonds. This is known as interest rate risk. Even though EU government bonds are considered safe investments, they are still subject to fluctuations in interest rates, which can impact their value. Additionally, other factors such as economic conditions, inflation expectations, and market sentiment can also affect the value of bonds in your portfolio.
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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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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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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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Statistical Inference and Probability
An experienced author in the field of data analytics and statistics, John Macinnes has produced a straight-forward text that breaks down the complex topic of inferential statistics with accessible language and detailed examples.It covers a range of topics, including: · Probability and Sampling distributions · Inference and regression · Power, effect size and inverse probability Part of The SAGE Quantitative Research Kit, this book will give you the know-how and confidence needed to succeed on your quantitative research journey.
Price: 31.99 £ | Shipping*: 0.00 £
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How does investing in bonds differ from investing in a bank account?
Investing in bonds involves purchasing debt securities issued by governments or corporations, which pay a fixed interest rate over a specified period of time. In contrast, investing in a bank account typically involves depositing money into a savings or checking account, where it earns a variable interest rate set by the bank. Bonds generally offer higher potential returns than bank accounts, but they also carry a higher level of risk. Additionally, bonds have a maturity date, while bank accounts provide more immediate access to funds.
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Why have the bonds in my portfolio, which are securities, lost the most value, even though they are EU government bonds considered as safe investment havens?
The value of EU government bonds in your portfolio may have decreased due to a variety of factors such as changes in interest rates, inflation expectations, or market sentiment. Even though EU government bonds are generally considered safe investment havens, they are still subject to market fluctuations and can lose value in certain economic conditions. Additionally, global events, economic uncertainty, or changes in government policies can also impact the value of these securities. It's important to monitor the market and economic conditions to understand the reasons behind the decrease in value of your bond holdings.
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Is it worth investing in Ukraine's war bonds?
Investing in Ukraine's war bonds can be a way to show support for the country during its conflict with Russia, but it also comes with risks. The situation in Ukraine is volatile and the outcome of the conflict is uncertain, which could affect the value of the bonds. Additionally, there may be concerns about the stability of the Ukrainian economy and the government's ability to repay the bonds. Therefore, investing in Ukraine's war bonds should be carefully considered and individuals should weigh the potential risks and rewards before making a decision.
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Can you finance a dual study program with savings?
Yes, it is possible to finance a dual study program with savings. If you have saved up enough money to cover the costs of tuition, living expenses, and other related expenses, you can use your savings to fund your dual study program. However, it is important to carefully consider the amount of savings you have and whether it will be enough to cover all the expenses associated with the program before making a decision. Additionally, you may also want to explore other financing options such as scholarships, student loans, or part-time work to supplement your savings if needed.
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