2nd Edition.| eBay! Email to friends Share on Facebook - opens in a new window or tab Share on Twitter - opens in a new window or tab Share on Pinterest - opens in a new window or tab. List of Available Solution Manuals. More Coming Sooon. # solution # solutionManual # solutions # mathematics # engineering # discreteMath # discreteMathematics # Computer # Accounting # calculus # howardAnton # physics Solution Manuals 1. Free download ebook – solution of Introductory circuit analysis.
- 32 Flavors By Yfb
- Probability Concepts In Engineering 2nd Edition Pdf Download Pdf
- Probability Concepts In Engineering 2nd Edition Pdf Download Windows 10
- Probability Concepts In Engineering 2nd Edition Pdf Download Windows 7
Apply the principles of probability and statistics to realistic engineering problems
The easiest and most effective way to learn the principles of probabilistic modeling and statistical inference is to apply those principles to a variety of applications. That's why Ang and Tang's Second Edition of Probability Concepts in Engineering (previously titled Probability Concepts in Engineering Planning and Design) explains concepts and methods using a wide range of problems related to engineering and the physical sciences, particularly civil and environmental engineering.
Now extensively revised with new illustrative problems and new and expanded topics, this Second Edition will help you develop a thorough understanding of probability and statistics and the ability to formulate and solve real-world problems in engineering. The authors present each basic principle using different examples, and give you the opportunity to enhance your understanding with practice problems. The text is ideally suited for students, as well as those wishing to learn and apply the principles and tools of statistics and probability through self-study.
Key Features in this 2nd Edition:
* A new chapter (Chapter 5) covers Computer-Based Numerical and Simulation Methods in Probability, to extend and expand the analytical methods to more complex engineering problems.
* New and expanded coverage includes distribution of extreme values (Chapter 3), the Anderson-Darling method for goodness-of-fit test (Chapter 6), hypothesis testing (Chapter 6), the determination of confidence intervals in linear regression (Chapter 8), and Bayesian regression and correlation analyses (Chapter 9).
* Many new exercise problems in each chapter help you develop a working knowledge of concepts and methods.
* Provides a wide variety of examples, including many new to this edition, to help you learn and understand specific concepts.
* Illustrates the formulation and solution of engineering-type probabilistic problems through computer-based methods, including developing computer codes using commercial software such as MATLAB and MATHCAD.
* Introduces and develops analytical probabilistic models and shows how to formulate engineering problems under uncertainty, and provides the fundamentals for quantitative risk assessment.
Sample questions asked in the 2nd edition of Probability Concepts in Engineering:
Drinking water may be contaminated by two pollutants. In a given community, the probability of its drinking water containing excessive amount of pollutant A is 0.1, whereas that of pollutant B is 0.2. When pollutant A is excessive, it will definitely cause health problems; however, when pollutant B is excessive, it will cause health problems in only 20% of the population who has low natural resistance to that pollutant. Also, data from many similar communities reveal that the presence of these two pollutants in drinking water is not independent; half of those communities whose drinking water contain excessive amounts of pollutant A will also contain excessive amounts of pollutant B. Suppose a resident is selected at random from this community, what is the probability that he or she will suffer health problems from drinking the water? Assume that a person’s resistance to pollutant B is innate, which is independent of the event of having excessive pollutant in the drinking water.
Data for the observed settlements of piles and the corresponding calculated settlements are compiled in Table E8.3 of Chapter 8. Based on these data, we can calculate the ratios of the observed to the corresponding calculated settlements; the results are as follows: Ratios of Observed Settlement to Calculated Settlement 0.12 0.97 0.86 1.14 0.94 2.37 0.88 0.92 1.01 0.99 1.02 1.04 0.99 0.87 0.52 0.94 1.06 1.38 1.04 1.18 1.00 0.86 0.82 0.84 1.09 The ratio of the observed to the calculated settlements is a measure of the accuracy of the calculational method. From the above data, we can observe that this ratio has considerable variability. (a) Assuming that the ratio is a Gaussian random variable, plot the above data on a normal probability paper and observe if there is a linear trend of the data points. (b) If a linear trend is observed, draw a straight line through the data points and estimate the mean and standard deviation from the straight line. Perform a chi-square goodness-of-fit test for the normal distribution at the 5% significance level. Also, do the same with the Anderson–Darling test. (c) Otherwise, if no linear trend can be observed in Part (b), plot the same data on another probability paper, such as the lognormal paper, and determine the suitability of this alternative distribution to model the relevant ratio, including a goodness-of-fit test at the 2% significance level to verify its suitability. TABLE E8.3 Summary of Data and Calculations for Example 8.3 EXAMPLE 8.3 Table E8.3 shows a set of data of observed settlements of pile groups (Col. 3), reported by Viggiani (2001), under the respective loads; also shown in the same table (Col. 4) are the corresponding calculated settlements using a nonlinear model proposed by Viggiani (2001). We may perform the regression of the observed settlement, Y , on the calculated settlement, X ; the calculations are summarized in Table E8.3. From Table E8.3, we obtain the sample means and sample standard deviations of Y and X , respectively, as follows: ? According to Eqs. 8.3 and 8.4, we obtain the corresponding regression coefficients for the regression of Y on X as follows: ? The data given in Columns 3 and 4 of Table E8.3 are plotted in Fig. E8.3. Therefore, the linear regression equation of Y on X is ? Figure E8.3 Data points and regression of observed settlement on calculated settlement. and with Eq. 8.9, we obtain the correlation coefficient ? which shows a very high correlation between the observed and calculated settlements. The conditional standard deviation of Y given X , according to Eq. 8.6a, is ? We may observe that this S Y|x is much smaller than the unconditional standard deviation s Y = 37.44 mm. We may also construct the 95% confidence interval of the regression line following Eq. 8.8. For this purpose, we first evaluate the corresponding 95% confidence intervals at the following selected discrete values of x i : 25 mm, 50 mm, 100 mm, 150 mm, and 180 mm. From Table A.3, t 0.975,23 = 2.069. ? By connecting two lines through the respective lower-bound and upper-bound values, as calculated above, we obtain the 95% confidence interval of the regression line as shown in dash lines in Fig. E8.3. TABLE A.3 Critical Values of t -Distribution at Confidence Level (1 ? ? ) = p d.o.f. p = 0.900 p = 0.950 p = 0.975 p = 0.990 p = 0.995 p = 0.999 1 3.0777 6.3138 12.7062 31.8205 63.6567 318.3088 2 1.8856 2.9200 4.3027 6.9646 9.9248 22.3271 3 1.6377 2.3534 3.1824 4.5407 5.8409 10.2145 4 1.5332 2.1318 2.7764 3.7469 4.6041 7.1732 5 1.4759 2.0150 2.5706 3.3649 4.0321 5.8934 6 1.4398 1.9432 2.4469 3.1427 3.7074 5.2076 7 1.4149 1.8946 2.3646 2.9980 3.4995 4.7853 8 1.3968 1.8595 2.3060 2.8965 3.3554 4.5008 9 1.3803 1.8331 2.2622 2.8214 3.2498 4.2968 10 1.3722 1.8125 2.2281 2.7638 3.1693 4.1437 11 1.3634 1.7959 2.2001 2.7181 3.1058 4.0247 12 1.3562 1.7823 2.1788 2.6810 3.0545 3.9296 13 1.3502 1.7709 2.1604 2.6503 3.0123 3.8520 14 1.3450 1.7613 2.1448 2.6245 2.9768 3.7874 15 1.3406 1.7531 2.1314 2.6025 2.9467 3.7328 16 1.3368 1.7459 2.1199 2.5835 2.9208 3.6862 17 1.3334 1.7396 2.1098 2.5669 2.8982 3.6458 18 1.3304 1.7341 2.1009 2.5524 2.8784 3.6105 19 1.3277 1.7291 2.0930 2.5395 2.8609 3.5794 20 1.3253 1.7247 2.0860 2.5280 2.8453 3.5518 21 1.3232 1.7207 2.0796 2.5176 2.8314 3.5272 22 1.3212 1.7171 2.0739 2.5083 2.8188 3.5050 23 1.3195 1.7139 2.0687 2.4999 2.8073 3.4850 24 1.3178 1.7109 2.0639 2.4922 2.7969 3.4668 25 1.3163 1.7081 2.0595 2.4851 2.7874 3.4502 26 1.3150 1.7056 2.0555 2.4786 2.7787 3.4350 27 1.3137 1.7033 2.0518 2.4727 2.7707 3.4210 28 1.3125 1.7011 2.0484 2.4671 2.7633 3.4082 29 1.3114 1.6991 2.0452 2.4620 2.7564 3.3962 30 1.3104 1.6973 2.0423 2.4573 2.7500 3.3852 31 1.3095 1.6955 2.0395 2.4528 2.7440 3.3749 32 1.3086 1.6939 2.0369 2.4487 2.7385 3.3653 33 1.3077 1.6924 2.0345 2.4448 2.7333 3.3563 34 1.3070 1.6909 2.0322 2.4411 2.7284 3.3479 35 1.3062 1.6896 2.0301 2.4377 2.7238 3.3400 36 1.3055 1.6883 2.0281 2.4345 2.7195 3.3326 37 1.3049 1.6871 2.0262 2.4314 2.7154 3.3256 38 1.3042 1.6806 2.0244 2.4286 2.7116 3.3190 39 1.3036 1.6849 2.0227 2.4258 2.7079 3.3128 40 1.3031 1.6839 2.0211 2.4233 2.7045 3.3069 45 1.3006 1.6794 2.0141 2.4121 2.6896 3.2815 50 1.2987 1.6759 2.0086 2.4033 2.6778 3.2614 55 1.2971 1.6703 2.0040 2.3961 2.6682 3.2451 60 1.2958 1.6706 2.0003 2.3901 2.6603 3.2317 70 1.2938 1.6794 1.9944 2.3808 2.6479 3.2108 80 1.2922 1.6759 1.9901 2.3739 2.6387 3.1953 90 1.2910 1.6750 1.9867 2.3685 2.6316 3.1833 ? 1.2824 1.6449 1.9600 2.3264 2.5759 3.0903
A contractor submits bids to three highway jobs and two building jobs. The probability of winning each job is 0.6. Assume that winning among the jobs is statistically independent. (a) What is the probability that the contractor will win at most one job? (b) What is the probability that the contractor will win at least two jobs? (c) What is the probability that he or she will win exactly one highway job, but none of the building jobs?
Download Book Introduction To Probability 2nd Edition in PDF format. You can Read Online Introduction To Probability 2nd Edition here in PDF, EPUB, Mobi or Docx formats.The easiest and most effective way to learn the principles of probabilistic modeling and statistical inference is to apply those principles to a variety of applications. That's why Ang and Tang's Second Edition of Probability Concepts in Engineering (previously titled Probability Concepts in Engineering Planning and Design) explains concepts and methods using a wide range of problems related to engineering and the physical sciences, particularly civil and environmental engineering.
Now extensively revised with new illustrative problems and new and expanded topics, this Second Edition will help you develop a thorough understanding of probability and statistics and the ability to formulate and solve real-world problems in engineering. The authors present each basic principle using different examples, and give you the opportunity to enhance your understanding with practice problems. The text is ideally suited for students, as well as those wishing to learn and apply the principles and tools of statistics and probability through self-study.
Key Features in this 2nd Edition:
* A new chapter (Chapter 5) covers Computer-Based Numerical and Simulation Methods in Probability, to extend and expand the analytical methods to more complex engineering problems.
* New and expanded coverage includes distribution of extreme values (Chapter 3), the Anderson-Darling method for goodness-of-fit test (Chapter 6), hypothesis testing (Chapter 6), the determination of confidence intervals in linear regression (Chapter 8), and Bayesian regression and correlation analyses (Chapter 9).
* Many new exercise problems in each chapter help you develop a working knowledge of concepts and methods.
* Provides a wide variety of examples, including many new to this edition, to help you learn and understand specific concepts.
* Illustrates the formulation and solution of engineering-type probabilistic problems through computer-based methods, including developing computer codes using commercial software such as MATLAB and MATHCAD.
* Introduces and develops analytical probabilistic models and shows how to formulate engineering problems under uncertainty, and provides the fundamentals for quantitative risk assessment.
Sample questions asked in the 2nd edition of Probability Concepts in Engineering:
Drinking water may be contaminated by two pollutants. In a given community, the probability of its drinking water containing excessive amount of pollutant A is 0.1, whereas that of pollutant B is 0.2. When pollutant A is excessive, it will definitely cause health problems; however, when pollutant B is excessive, it will cause health problems in only 20% of the population who has low natural resistance to that pollutant. Also, data from many similar communities reveal that the presence of these two pollutants in drinking water is not independent; half of those communities whose drinking water contain excessive amounts of pollutant A will also contain excessive amounts of pollutant B. Suppose a resident is selected at random from this community, what is the probability that he or she will suffer health problems from drinking the water? Assume that a person’s resistance to pollutant B is innate, which is independent of the event of having excessive pollutant in the drinking water.
Data for the observed settlements of piles and the corresponding calculated settlements are compiled in Table E8.3 of Chapter 8. Based on these data, we can calculate the ratios of the observed to the corresponding calculated settlements; the results are as follows: Ratios of Observed Settlement to Calculated Settlement 0.12 0.97 0.86 1.14 0.94 2.37 0.88 0.92 1.01 0.99 1.02 1.04 0.99 0.87 0.52 0.94 1.06 1.38 1.04 1.18 1.00 0.86 0.82 0.84 1.09 The ratio of the observed to the calculated settlements is a measure of the accuracy of the calculational method. From the above data, we can observe that this ratio has considerable variability. (a) Assuming that the ratio is a Gaussian random variable, plot the above data on a normal probability paper and observe if there is a linear trend of the data points. (b) If a linear trend is observed, draw a straight line through the data points and estimate the mean and standard deviation from the straight line. Perform a chi-square goodness-of-fit test for the normal distribution at the 5% significance level. Also, do the same with the Anderson–Darling test. (c) Otherwise, if no linear trend can be observed in Part (b), plot the same data on another probability paper, such as the lognormal paper, and determine the suitability of this alternative distribution to model the relevant ratio, including a goodness-of-fit test at the 2% significance level to verify its suitability. TABLE E8.3 Summary of Data and Calculations for Example 8.3 EXAMPLE 8.3 Table E8.3 shows a set of data of observed settlements of pile groups (Col. 3), reported by Viggiani (2001), under the respective loads; also shown in the same table (Col. 4) are the corresponding calculated settlements using a nonlinear model proposed by Viggiani (2001). We may perform the regression of the observed settlement, Y , on the calculated settlement, X ; the calculations are summarized in Table E8.3. From Table E8.3, we obtain the sample means and sample standard deviations of Y and X , respectively, as follows: ? According to Eqs. 8.3 and 8.4, we obtain the corresponding regression coefficients for the regression of Y on X as follows: ? The data given in Columns 3 and 4 of Table E8.3 are plotted in Fig. E8.3. Therefore, the linear regression equation of Y on X is ? Figure E8.3 Data points and regression of observed settlement on calculated settlement. and with Eq. 8.9, we obtain the correlation coefficient ? which shows a very high correlation between the observed and calculated settlements. The conditional standard deviation of Y given X , according to Eq. 8.6a, is ? We may observe that this S Y|x is much smaller than the unconditional standard deviation s Y = 37.44 mm. We may also construct the 95% confidence interval of the regression line following Eq. 8.8. For this purpose, we first evaluate the corresponding 95% confidence intervals at the following selected discrete values of x i : 25 mm, 50 mm, 100 mm, 150 mm, and 180 mm. From Table A.3, t 0.975,23 = 2.069. ? By connecting two lines through the respective lower-bound and upper-bound values, as calculated above, we obtain the 95% confidence interval of the regression line as shown in dash lines in Fig. E8.3. TABLE A.3 Critical Values of t -Distribution at Confidence Level (1 ? ? ) = p d.o.f. p = 0.900 p = 0.950 p = 0.975 p = 0.990 p = 0.995 p = 0.999 1 3.0777 6.3138 12.7062 31.8205 63.6567 318.3088 2 1.8856 2.9200 4.3027 6.9646 9.9248 22.3271 3 1.6377 2.3534 3.1824 4.5407 5.8409 10.2145 4 1.5332 2.1318 2.7764 3.7469 4.6041 7.1732 5 1.4759 2.0150 2.5706 3.3649 4.0321 5.8934 6 1.4398 1.9432 2.4469 3.1427 3.7074 5.2076 7 1.4149 1.8946 2.3646 2.9980 3.4995 4.7853 8 1.3968 1.8595 2.3060 2.8965 3.3554 4.5008 9 1.3803 1.8331 2.2622 2.8214 3.2498 4.2968 10 1.3722 1.8125 2.2281 2.7638 3.1693 4.1437 11 1.3634 1.7959 2.2001 2.7181 3.1058 4.0247 12 1.3562 1.7823 2.1788 2.6810 3.0545 3.9296 13 1.3502 1.7709 2.1604 2.6503 3.0123 3.8520 14 1.3450 1.7613 2.1448 2.6245 2.9768 3.7874 15 1.3406 1.7531 2.1314 2.6025 2.9467 3.7328 16 1.3368 1.7459 2.1199 2.5835 2.9208 3.6862 17 1.3334 1.7396 2.1098 2.5669 2.8982 3.6458 18 1.3304 1.7341 2.1009 2.5524 2.8784 3.6105 19 1.3277 1.7291 2.0930 2.5395 2.8609 3.5794 20 1.3253 1.7247 2.0860 2.5280 2.8453 3.5518 21 1.3232 1.7207 2.0796 2.5176 2.8314 3.5272 22 1.3212 1.7171 2.0739 2.5083 2.8188 3.5050 23 1.3195 1.7139 2.0687 2.4999 2.8073 3.4850 24 1.3178 1.7109 2.0639 2.4922 2.7969 3.4668 25 1.3163 1.7081 2.0595 2.4851 2.7874 3.4502 26 1.3150 1.7056 2.0555 2.4786 2.7787 3.4350 27 1.3137 1.7033 2.0518 2.4727 2.7707 3.4210 28 1.3125 1.7011 2.0484 2.4671 2.7633 3.4082 29 1.3114 1.6991 2.0452 2.4620 2.7564 3.3962 30 1.3104 1.6973 2.0423 2.4573 2.7500 3.3852 31 1.3095 1.6955 2.0395 2.4528 2.7440 3.3749 32 1.3086 1.6939 2.0369 2.4487 2.7385 3.3653 33 1.3077 1.6924 2.0345 2.4448 2.7333 3.3563 34 1.3070 1.6909 2.0322 2.4411 2.7284 3.3479 35 1.3062 1.6896 2.0301 2.4377 2.7238 3.3400 36 1.3055 1.6883 2.0281 2.4345 2.7195 3.3326 37 1.3049 1.6871 2.0262 2.4314 2.7154 3.3256 38 1.3042 1.6806 2.0244 2.4286 2.7116 3.3190 39 1.3036 1.6849 2.0227 2.4258 2.7079 3.3128 40 1.3031 1.6839 2.0211 2.4233 2.7045 3.3069 45 1.3006 1.6794 2.0141 2.4121 2.6896 3.2815 50 1.2987 1.6759 2.0086 2.4033 2.6778 3.2614 55 1.2971 1.6703 2.0040 2.3961 2.6682 3.2451 60 1.2958 1.6706 2.0003 2.3901 2.6603 3.2317 70 1.2938 1.6794 1.9944 2.3808 2.6479 3.2108 80 1.2922 1.6759 1.9901 2.3739 2.6387 3.1953 90 1.2910 1.6750 1.9867 2.3685 2.6316 3.1833 ? 1.2824 1.6449 1.9600 2.3264 2.5759 3.0903
A contractor submits bids to three highway jobs and two building jobs. The probability of winning each job is 0.6. Assume that winning among the jobs is statistically independent. (a) What is the probability that the contractor will win at most one job? (b) What is the probability that the contractor will win at least two jobs? (c) What is the probability that he or she will win exactly one highway job, but none of the building jobs?
An Introduction To Probability And Statistics 2nd Ed
Author : Vijay K. RohatgiISBN : 8126519266
Genre :
File Size : 66. 15 MB
Format : PDF
Download : 902
Read : 874
Market_Desc: This book is intended for Upper Seniors and Beginning Graduate Students in Mathematics, as well as Students in Physics and Engineering with strong mathematical backgrounds. It was designed for a three-quarter course meeting four hours per week or a two-semester course meeting three hours per week. Special Features: · An excellent introduction to the field of statistics organized in three parts: probability, foundations of statistical inference, and special topics. The Second Edition boasts a completely updated statistical inference section as well as many new problems, examples, and figures. It omits the introduction section and the chapter on sequential statistical inference. Includes over 350 worked examples.· Offers the proof of the central limit theorem by the method of operators and proof of the strong law of large numbers.· Contains a section on minimal sufficient statistics.· Carefully presents the theory of confidence intervals, including Bayesian intervals and shortest-length confidence intervals. About The Book: The second edition now has an updated statistical inference section (chapters 8 to 13). Many revisions have been made, the references have been updated, and many new problems and worked examples have been added.
An Introduction To Probability Theory And Its Applications 2nd Ed
Author : Willliam FellerISBN : 8126518065
Genre :
File Size : 82. 94 MB
Format : PDF, Docs
Download : 894
Read : 945
· The Exponential and the Uniform Densities· Special Densities. Randomization· Densities in Higher Dimensions. Normal Densities and Processes· Probability Measures and Spaces· Probability Distributions in Rr· A Survey of Some Important Distributions and Processes· Laws of Large Numbers. Applications in Analysis· The Basic Limit Theorems· Infinitely Divisible Distributions and Semi-Groups· Markov Processes and Semi-Groups· Renewal Theory· Random Walks in R1· Laplace Transforms. Tauberian Theorems. Resolvents· Applications of Laplace Transforms· Characteristic Functions· Expansions Related to the Central Limit Theorem,· Infinitely Divisible Distributions· Applications of Fourier Methods to Random Walks· Harmonic Analysis
A Concise Handbook Of Mathematics Physics And Engineering Sciences
Author : Andrei D. PolyaninISBN : 1439806403
Genre : Mathematics
File Size : 53. 1 MB
Format : PDF, ePub, Docs
Download : 661
Read : 842
A Concise Handbook of Mathematics, Physics, and Engineering Sciences takes a practical approach to the basic notions, formulas, equations, problems, theorems, methods, and laws that most frequently occur in scientific and engineering applications and university education. The authors pay special attention to issues that many engineers and students find difficult to understand. The first part of the book contains chapters on arithmetic, elementary and analytic geometry, algebra, differential and integral calculus, functions of complex variables, integral transforms, ordinary and partial differential equations, special functions, and probability theory. The second part discusses molecular physics and thermodynamics, electricity and magnetism, oscillations and waves, optics, special relativity, quantum mechanics, atomic and nuclear physics, and elementary particles. The third part covers dimensional analysis and similarity, mechanics of point masses and rigid bodies, strength of materials, hydrodynamics, mass and heat transfer, electrical engineering, and methods for constructing empirical and engineering formulas. The main text offers a concise, coherent survey of the most important definitions, formulas, equations, methods, theorems, and laws. Numerous examples throughout and references at the end of each chapter provide readers with a better understanding of the topics and methods. Additional issues of interest can be found in the remarks. For ease of reading, the supplement at the back of the book provides several long mathematical tables, including indefinite and definite integrals, direct and inverse integral transforms, and exact solutions of differential equations.
Ruin Probabilities
Author :ISBN : 9789814466929
Genre :
File Size : 40. 33 MB
Format : PDF
Download : 563
Read :
1094
Basic Probability Theory With Applications
Author : Mario LefebvreISBN : 9780387749952
Genre :
32 Flavors By Yfb
MathematicsFile Size : 63. 1 MB
Format : PDF, ePub, Mobi
Download : 776
Read : 974
The main intended audience for this book is undergraduate students in pure and applied sciences, especially those in engineering. Chapters 2 to 4 cover the probability theory they generally need in their training. Although the treatment of the subject is surely su?cient for non-mathematicians, I intentionally avoided getting too much into detail. For instance, topics such as mixed type random variables and the Dirac delta function are only brie?y mentioned. Courses on probability theory are often considered di?cult. However, after having taught this subject for many years, I have come to the conclusion that one of the biggest problems that the students face when they try to learn probability theory, particularly nowadays, is their de?ciencies in basic di?erential and integral calculus. Integration by parts, for example, is often already forgotten by the students when they take a course on probability. For this reason, I have decided to write a chapter reviewing the basic elements of di?erential calculus. Even though this chapter might not be covered in class, the students can refer to it when needed. In this chapter, an e?ort was made to give the readers a good idea of the use in probability theory of the concepts they should already know. Chapter 2 presents the main results of what is known as elementary probability, including Bayes’ rule and elements of combinatorial analysis.
An Introduction To Measure And Probability
Author : J.C. TaylorISBN : 9781461206590
Genre : Mathematics
File Size : 72. 10 MB
Format : PDF, Docs
Download : 944
Read : 881
Assuming only calculus and linear algebra, Professor Taylor introduces readers to measure theory and probability, discrete martingales, and weak convergence. This is a technically complete, self-contained and rigorous approach that helps the reader to develop basic skills in analysis and probability. Students of pure mathematics and statistics can thus expect to acquire a sound introduction to basic measure theory and probability, while readers with a background in finance, business, or engineering will gain a technical understanding of discrete martingales in the equivalent of one semester. J. C. Taylor is the author of numerous articles on potential theory, both probabilistic and analytic, and is particularly interested in the potential theory of symmetric spaces.
A First Look At Rigorous Probability Theory
Author : Jeffrey Seth RosenthalISBN : 9789812703705
Genre : Mathematics
File Size : 50. 66 MB
Format : PDF, ePub, Mobi
Download : 748
Read : 463
Features an introduction to probability theory using measure theory. This work provides proofs of the essential introductory results and presents the measure theory and mathematical details in terms of intuitive probabilistic concepts, rather than as separate, imposing subjects.
Schaum S Outline Of Probability Second Edition
Author : Seymour LipschutzISBN : 9780071816588
Genre : Study Aids
File Size : 67. 64 MB
Format : PDF, Kindle
Download : 638
Read : 1270
***IF YOU WANT TO UPDATE THE INFORMATION ON YOUR TITLE SHEET, THEN YOU MUST UPDATE COPY IN THE 'PRODUCT INFORMATION COPY' FIELD. COPY IN THE 'TIPSHEET COPY' FIELD DOES NOT APPEAR ON TITLE SHEETS.*** A classic Schaum's Outline, thoroughly updated to match the latest course scope and sequence. The ideal review for the thousands of college students who enroll in Probability courses. About the Book An update of this successful outline in probability, modified to conform to the current curriculum. Schaum’s Outline of Probability mirrors the course in scope and sequence to help enrolled students understand basic concepts and offer extra practice on topics such as finite and countable sets, binomial coefficients, axioms of probability, conditional probability, expectation of a finite random variable, Poisson distribution, and probability of vectors and Stochastic matrices. Coverage will also include finite Stochastic and tree diagrams, Chebyshev’s Inequality and the Law of Large Numbers, calculations of binomial probabilities using the normal approximation, and regular Markov processes & stationary state distributions. Key Selling Features Outline format supplies a concise guide to the standard college course in Probability 430 solved problems Easily-understood review of Probability Supports all the major textbooks for Probability courses Clear, concise explanations of all Probability concepts Appropriate for the following courses: Elementary Probability & Statistics; Data Analysis; Finite Mathematics; Introduction to Mathematical Statistics; Mathematics for Biological Sciences; Introductory Statistics; Discrete Mathematics; Probability for Applied Science; Introduction to Probability Theory Record of Success: Schaum’s Outline of Probability is a solid selling title in the series—with previous edition having sold over 12,500 copies since 2002. Supports the following bestselling textbooks: Bluman, Elementary Statistics: A Step by Step Approach, 4ed, 0073347140, $92.22, MGH, 2006. (MIR: 7,265 units) Hungerford, Mathematics with Applications, 9ed, 0321334337, $129.48, PEG, 2006. (MIR: 2,731 units) Rosen, Discrete Mathematics and Its Applications, 6ed, 0073229725, $151.76, MGH, 2006. (MIR: 2,866 units) Market / Audience Primary: For all students of mathematics who need to learn or refresh Probability skills. Secondary: Graduate students and professionals looking for a tool for review Enrollment: Elementary Probability and Statistics – 504,600; Data Analysis – 16,820; Finite Mathematics – 106,732; Introductory Statistics – 38,657; Discrete Mathematics – 50,592; Introduction to Probability Theory – 3,196 Author Profiles Seymour Lipschutz (Philadelphia, PA) who is presently on the mathematics faculty of Temple University, formerly taught at the Polytechnic Institute of Brooklyn and was visiting professor in the Computer Science Department of Brooklyn College. He received his Ph.D. in 1960 at the Courant Institute of mathematical Sciences of New York University. Some of his other books in the Schaum's Outline Series are Beginning Linear Algebra; Discrete Mathematics, 3ed; and Linear Algebra, 4ed. Marc Lipson (Charlottesville, VA) is on the faculty of the University of Virginia. He formerly taught at the University of Georgia, Northeastern University, and Boston University. He received his Ph..D. in finance in 1994 from the University of Michigan. He is also coauthor of the Schaum;s Outline of Discrete Mathematics, 3ed with Seymour Lipschutz.
Introduction To Probability Models Eighth Edition
Author : Sheldon M. RossISBN : 0125980566
Genre : Probabilities
File Size : 50. 7 MB
Format : PDF
Download : 268
Read : 162
Probability Concepts In Engineering 2nd Edition Pdf Download Pdf
Introduction to Probability Models, 8th Edition, continues to introduce and inspire readers to the art of applying probability theory to phenomena in fields such as engineering, computer science, management and actuarial science, the physical and social sciences, and operations research. Now revised and updated, this best-selling book retains its hallmark intuitive, lively writing style, captivating introduction to applications from diverse disciplines, and plentiful exercises and worked-out examples. The 8th Edition includes five new sections and numerous new examples and exercises, many of which focus on strategies applicable in risk industries such as insurance or actuarial work. The five new sections include: * Section 3.6.4 presents an elementary approach, using only conditional expectation, for computing the expected time until a sequence of independent and identically distributed random variables produce a specified pattern. * Section 3.6.5 derives an identity involving compound Poisson random variables and then uses it to obtain an elegant recursive formula for the probabilities of compound Poisson random variables whose incremental increases are nonnegative and integer valued * Section 5.4.3 is concerned with a conditional Poisson process, a type of process that is widely applicable in the risk industries * Section 7.10 presents a derivation of and a new characterization for the classical insurance ruin probability. * Section 11.8 presents a simulation procedure known as coupling from the past; its use enables one to exactly generate the value of a random variable whose distribution is that of the stationary distribution of a given Markov chain, evenin cases where the stationary distribution cannot itself be explicitly determined. Other Academic Press books by Sheldon Ross: Simulation 3rd Ed., ISBN: 0-12-598053-1 Probability Models for Computer Science, ISBN 0-12-598051-5 Introduction to Probability and Statistics for Engineers and Scientists, 2nd Ed., ISBN: 0-12-598472-3 * Classic text by best-selling author * Continues the tradition of expository excellence * Contains compulsory material for Exam 3 of the Society of ActuariesAn Introduction To Probability And Statistics
Author : Vijay K. RohatgiISBN : 9781118799642
Genre : Mathematics
File Size : 60. 40 MB
Format : PDF
Download : 206
Read : 765
This Third Edition provides a solid and well-balancedintroduction to probability theory and mathematicalstatistics. The book is divided into three parts: Chapters1-6 form the core of probability fundamentals and foundations;Chapters 7-11 cover statistics inference; and the remainingchapters focus on special topics. For course sequences thatseparate probability and mathematics statistics, the first part ofthe book can be used for a course in probability theory, followedby a course in mathematical statistics based on the second part,and possibly, one or more chapters on special topics. Thebook contains over 550 problems, 350 worked-out examples, and 200side notes for reader reference. Numerous figures have beenadded to illustrate examples and proofs, and answers to selectproblems are now included. Many parts of the book haveundergone substantial rewriting, and the book has also beenreorganized. Chapters 6 and 7 have been interchanged to emphasizethe role of asymptotics in statistics, and the new Chapter 7contains all of the needed basic material on asymptotics. Chapter 6 also includes new material on resampling, specificallybootstrap. The new Further Results chapter include someestimation procedures such as M-estimatesand bootstrapping. A new chapter on regression analysishas also been added and contains sections on linear regression,multiple regression, subset regression, logistic regression, andPoisson regression.