Noise in physics is related to a variety of domains, such as information theory, statistical physics, probability, stochastic processes and statistics. Noise Theory and Application to Physics provides a general background on noise theory, along with techniques to describe and extract information in the presence of fluctuations, with the goal of featuring noise in the context of its connection with other domains. Readers will gain a deep understanding of noise theory, while acquiring systematic techniques for describing and extracting noise data.

1. The Binomial No-Arbitrage Pricing Model
1.1. One-Period Binomial Model
1.2. Multiperiod Binomial Model
1.3. Computational Considerations
1.4. Summary
1.5. Notes
1.6. Exercises 2. Probability Theory on Coin Toss Space
2.1. Finite Probability Spaces
2.2. Random Variables, Distributions, and Expectations
2.3. Conditional Expectations
2.4. Martingales
2.5. Markov Processes
2.6. Summary
2.7. Notes
2.8. Exercises 3. State Prices
3.1. Change of Measure
3.2. Radon-Nikod'ym Derivative Process
3.3. Capital Asset Pricing Model
3.4. Summary
3.5. Notes
3.6. Exercises 4. American Derivative Securities
4.1. Introduction
4.2. Non-Path-Dependent American Derivatives
4.3. Stopping Times
4.4. General American Derivatives
4.5. American Call Options
4.6. Summary
4.7. Notes
4.8. Exercises 5. Random Walk
5.1. Introduction
5.2. First Passage Times
5.3. Reflection Principle
5.4. Perpetual American Put: An Example
5.5. Summary
5.6. Notes
5.7. Exercises 6. Interest-Rate-Dependent Assets
6.1. Introduction
6.2. Binomial Model for Interest Rates
6.3. Fixed-Income Derivatives
6.4. Forward Measures
6.5. Futures
6.6. Summary
6.7. Notes
6.8. Exercises
Proof of Fundamental Properties of Conditional Expectations
References
Index