The following are some of the best books to own to learn Monte Carlo methods for sampling and estimation problems

Monte Carlo Methods in Statistics (Springer)

This is a good book which discusses both Bayesian methods from a practical point of view as well as theoretical point of view with integrations. The explanations given are also fairly comprehensive. There are also a fair amount of examples in this text. Overall, this is an excellent book to own if you want to understand Monte Carlo sampling methods and algorithms at an intermediate to graduate level.

Explorations in Monte Carlo Methods (Undergraduate Texts in Mathematics)

This is good book to own to get you started on Monte Carlo methods. It starts with fairly simple and basic examples and illustrations. The mathematics used is also fairly basic. Buy this if you are at an undergraduate level and want to get into using Monte Carlo methods but have only a basic knowledge of statistics and probability.

Monte Carlo Methods in Financial Engineering (Stochastic Modelling and Applied Probability) (v. 53)

Another excellent book to own if you are curious to learn about the methods of Monte Carlo in the finance industry. Some really nice areas that are covered in the book include variance reduction techniques, diffusion equations, change point detections, Option pricing methods etc. Ideal for students of financial engineering or ones wanting to break into it. The book tends to overtly rate MC methods (well its a book on MC!).

Monte Carlo Simulation and Resampling Methods for Social Science

This book gives a good introduction and goes over some basic probability theory, statistics and distributions before it hops on to the Monte Carlo methods. This makes it a good introductory book for sampling methods. Recommended for undergraduates with minimal statistical background.

Simulation and Monte Carlo Method

An excellent book to own at the intermediate to graduate level. The text provides a good course in simulation and Monte Carlo methods. Some interesting topics covered in the text include rare-event simulation. The book assumes you have a background in statistics and probability theory.

Monte Carlo Methods in Statistics (Springer)

This is a good book which discusses both Bayesian methods from a practical point of view as well as theoretical point of view with integrations. The explanations given are also fairly comprehensive. There are also a fair amount of examples in this text. Overall, this is an excellent book to own if you want to understand Monte Carlo sampling methods and algorithms at an intermediate to graduate level.

Explorations in Monte Carlo Methods (Undergraduate Texts in Mathematics)

This is good book to own to get you started on Monte Carlo methods. It starts with fairly simple and basic examples and illustrations. The mathematics used is also fairly basic. Buy this if you are at an undergraduate level and want to get into using Monte Carlo methods but have only a basic knowledge of statistics and probability.

Monte Carlo Methods in Financial Engineering (Stochastic Modelling and Applied Probability) (v. 53)

Another excellent book to own if you are curious to learn about the methods of Monte Carlo in the finance industry. Some really nice areas that are covered in the book include variance reduction techniques, diffusion equations, change point detections, Option pricing methods etc. Ideal for students of financial engineering or ones wanting to break into it. The book tends to overtly rate MC methods (well its a book on MC!).

Monte Carlo Simulation and Resampling Methods for Social Science

This book gives a good introduction and goes over some basic probability theory, statistics and distributions before it hops on to the Monte Carlo methods. This makes it a good introductory book for sampling methods. Recommended for undergraduates with minimal statistical background.

Simulation and Monte Carlo Method

An excellent book to own at the intermediate to graduate level. The text provides a good course in simulation and Monte Carlo methods. Some interesting topics covered in the text include rare-event simulation. The book assumes you have a background in statistics and probability theory.

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