Read the related blog, Probably Overthinking It. Or if you are using Python 3, mathematics made simple pdf can use this updated code. Roger Labbe has transformed Think Bayes into IPython notebooks where you can modify and run the code. Think Bayes is an introduction to Bayesian statistics using computational methods.
The premise of this book, and the other books in the Think X series, is that if you know how to program, you can use that skill to learn other topics. Most books on Bayesian statistics use mathematical notation and present ideas in terms of mathematical concepts like calculus. This book uses Python code instead of math, and discrete approximations instead of continuous mathematics. As a result, what would be an integral in a math book becomes a summation, and most operations on probability distributions are simple loops. I think this presentation is easier to understand, at least for people with programming skills. It is also more general, because when we make modeling decisions, we can choose the most appropriate model without worrying too much about whether the model lends itself to conventional analysis. Also, it provides a smooth development path from simple examples to real-world problems.
Think Bayes is a Free Book. 0 Unported License, which means that you are free to copy, distribute, and modify it, as long as you attribute the work and don’t use it for commercial purposes. Other Free Books by Allen Downey are available from Green Tea Press. If you face any problem in downloading, please inform us through feedback form available in the site. This page was being slower due to excess use by the visitors. That is why the page was taking time to open.
The changes on this page is made to overcome this problem. Mathematics 12 by R D Sharma is a very good book for the concepts and practice material including a lots of solved questions with proper explanation. If you have done a chapter, the Together With Maths is very good to get proper practice. Types of relations: reflexive, symmetric, transitive and equivalence relations. One to one and onto functions, composite functions, inverse of a function. Definition, range, domain, principal value branch.
Elementary properties of inverse trigonometric functions. Concept, notation, order, equality, types of matrices, zero and identity matrix, transpose of a matrix, symmetric and skew symmetric matrices. Operation on matrices: Addition and multiplication and multiplication with a scalar. Simple properties of addition, multiplication and scalar multiplication. Adjoint and inverse of a square matrix. Continuity and differentiability, derivative of composite functions, chain rule, derivatives of inverse trigonometric functions, derivative of implicit functions. Derivatives of logarithmic and exponential functions.