Yeni
#smrgKİTABEVİ Probability and Probability Distributions With R Applications - 2025
Editör:
Kondisyon:
Yeni
Sunuş / Önsöz / Sonsöz / Giriş:
Basıldığı Matbaa:
Dizi Adı:
ISBN-10:
6253765125
Kargoya Teslim Süresi:
3&7
Hazırlayan:
Cilt:
Amerikan Cilt
Boyut:
21x29
Sayfa Sayısı:
578
Basım Yeri:
Ankara
Baskı:
1
Basım Tarihi:
2025
Kapak Türü:
Karton Kapak
Kağıt Türü:
1. Hamur
Dili:
Türkçe
Kategori:
indirimli
600,00
Havale/EFT ile:
582,00
Siparişiniz 3&7 iş günü arasında kargoda
1199242678
629879
https://www.simurgkitabevi.com/probability-and-probability-distributions-with-r-applications-2025-1
Probability and Probability Distributions With R Applications - 2025 #smrgKİTABEVİ
600.00
This textbook is designed to serve as both a primary and supplementary resource for all academic departments offering a course in probability. Each chapter is systematically structured to begin with a theoretical exposition, followed by detailed, fully worked examples. To support deeper understanding and practical application of probability concepts, each section includes corresponding R programming implementations.
The content encompasses the foundational concepts of set theory, construction of sample spaces, conditional probability, independence, and Bayes' theorem. It further explores the concept of random variables, properties of discrete and continuous random variables, probability mass functions, probability density functions, and cumulative distribution functions. Scenarios involving univariate, bivariate, and multivariate random variables are thoroughly analyzed. Additionally, the text covers independence of random variables, conditional probability functions, quantiles, and key statistical measures associated with probability distributions including expected value, variance, moments, moment-generating functions, covariance, correlation, characteristic functions, and factorial moment-generating functions. Essential inequalities such as Markov, Chebyshev, and Cauchy–Schwarz, along with the Central Limit Theorem, are presented with comprehensive exercises and R-based solutions. The book also provides an in-depth examination of commonly used discrete and continuous probability distributions, including: Bernoulli, Binomial, Multinomial, Geometric, Negative Binomial, Hypergeometric, Generalized Hypergeometric, Poisson, Discrete Uniform, Continuous Uniform, Normal, Standard Normal, Bivariate Normal, Log-Normal, Exponential, Gamma, Beta, and Cauchy distributions. For each distribution, the probability and distribution functions, distributional shapes, expected values, moments, and moment-generating functions are derived and illustrated with examples and R programming applications.
The content encompasses the foundational concepts of set theory, construction of sample spaces, conditional probability, independence, and Bayes' theorem. It further explores the concept of random variables, properties of discrete and continuous random variables, probability mass functions, probability density functions, and cumulative distribution functions. Scenarios involving univariate, bivariate, and multivariate random variables are thoroughly analyzed. Additionally, the text covers independence of random variables, conditional probability functions, quantiles, and key statistical measures associated with probability distributions including expected value, variance, moments, moment-generating functions, covariance, correlation, characteristic functions, and factorial moment-generating functions. Essential inequalities such as Markov, Chebyshev, and Cauchy–Schwarz, along with the Central Limit Theorem, are presented with comprehensive exercises and R-based solutions. The book also provides an in-depth examination of commonly used discrete and continuous probability distributions, including: Bernoulli, Binomial, Multinomial, Geometric, Negative Binomial, Hypergeometric, Generalized Hypergeometric, Poisson, Discrete Uniform, Continuous Uniform, Normal, Standard Normal, Bivariate Normal, Log-Normal, Exponential, Gamma, Beta, and Cauchy distributions. For each distribution, the probability and distribution functions, distributional shapes, expected values, moments, and moment-generating functions are derived and illustrated with examples and R programming applications.
This textbook is designed to serve as both a primary and supplementary resource for all academic departments offering a course in probability. Each chapter is systematically structured to begin with a theoretical exposition, followed by detailed, fully worked examples. To support deeper understanding and practical application of probability concepts, each section includes corresponding R programming implementations.
The content encompasses the foundational concepts of set theory, construction of sample spaces, conditional probability, independence, and Bayes' theorem. It further explores the concept of random variables, properties of discrete and continuous random variables, probability mass functions, probability density functions, and cumulative distribution functions. Scenarios involving univariate, bivariate, and multivariate random variables are thoroughly analyzed. Additionally, the text covers independence of random variables, conditional probability functions, quantiles, and key statistical measures associated with probability distributions including expected value, variance, moments, moment-generating functions, covariance, correlation, characteristic functions, and factorial moment-generating functions. Essential inequalities such as Markov, Chebyshev, and Cauchy–Schwarz, along with the Central Limit Theorem, are presented with comprehensive exercises and R-based solutions. The book also provides an in-depth examination of commonly used discrete and continuous probability distributions, including: Bernoulli, Binomial, Multinomial, Geometric, Negative Binomial, Hypergeometric, Generalized Hypergeometric, Poisson, Discrete Uniform, Continuous Uniform, Normal, Standard Normal, Bivariate Normal, Log-Normal, Exponential, Gamma, Beta, and Cauchy distributions. For each distribution, the probability and distribution functions, distributional shapes, expected values, moments, and moment-generating functions are derived and illustrated with examples and R programming applications.
The content encompasses the foundational concepts of set theory, construction of sample spaces, conditional probability, independence, and Bayes' theorem. It further explores the concept of random variables, properties of discrete and continuous random variables, probability mass functions, probability density functions, and cumulative distribution functions. Scenarios involving univariate, bivariate, and multivariate random variables are thoroughly analyzed. Additionally, the text covers independence of random variables, conditional probability functions, quantiles, and key statistical measures associated with probability distributions including expected value, variance, moments, moment-generating functions, covariance, correlation, characteristic functions, and factorial moment-generating functions. Essential inequalities such as Markov, Chebyshev, and Cauchy–Schwarz, along with the Central Limit Theorem, are presented with comprehensive exercises and R-based solutions. The book also provides an in-depth examination of commonly used discrete and continuous probability distributions, including: Bernoulli, Binomial, Multinomial, Geometric, Negative Binomial, Hypergeometric, Generalized Hypergeometric, Poisson, Discrete Uniform, Continuous Uniform, Normal, Standard Normal, Bivariate Normal, Log-Normal, Exponential, Gamma, Beta, and Cauchy distributions. For each distribution, the probability and distribution functions, distributional shapes, expected values, moments, and moment-generating functions are derived and illustrated with examples and R programming applications.
Yorum yaz
Bu kitabı henüz kimse eleştirmemiş.
