Understanding Stochastic Calculus and Expected Value Formulas

In summary, the expected value for a Bernoulli Random Variable is 0, the expected value for a Random Variable with two outcomes is 1/2, and the expected value for a Random Variable with three outcomes is 1.
  • #1
courtrigrad
1,236
2
Hello all

If you throw a head I give you $1. If you throw a tail you give me $1. If [tex] R_i [/tex] is the random amount ($1 or -$1) you make on the [tex] ith [/tex] toss then why is: [tex] E[R_i] = 0, E[R^2_i]=1, E[R_iR_j] = 0 [/tex]? If [tex] S_i = \sum^i_{j=1} R_j [/tex] which represents the total amount of money you have won up to and including the ith toss, then why does [tex] E[S_i] = 0, E[S_i^2] = E[R_1^2 + 2R_1R_2 + ...] = i [/tex]? I know that if there had been five tosses already then [tex] E[S_6|R_1,...,R_5] = S_5 [/tex]

Any help is appreciated

Thanks
 
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  • #2
I know that Expected Value is defined as: [tex] E[X] = \sum_x f(x)P(x) [/tex] for a discrete variable.
 
  • #3
courtrigrad said:
I know that Expected Value is defined as: [tex] E[X] = \sum_x f(x)P(x) [/tex] for a discrete variable.

Right, and Ri is what is called (in my probability theory class I'm taking anyways) a Bernoulli Random Variable. There are only two possible outcomes: success, or failure, usually denoted by 1 or 0 respectively. However, since Ri represents the winnings, and they are -1 for tails, the expectation, given your definition is (letting H denote a heads and T a tails):

[tex] E[X] = \sum_x xp(x) = 1\cdot P(H) -1 \cdot P(T) = 1/2 - 1/2 = 0 [/tex]

The expectation can be interpreted as the average value (ie the value expected over time) that the random variable takes on. For a random variable that can only take on the values 1 and -1, what is the average? Since both outcomes have equal weighting (p = 1-p = 1/2), on average you'd expect no net winnings, since the chances of getting a dollar are about equal to that of having to pay up. I hope this gets you started. Now consider the other cases: what is Ri squared? Well, what is 1^2? What is (-1)^2?
ETC...
 

1. What is stochastic calculus and why is it important?

Stochastic calculus is a branch of mathematics that deals with the study of random processes. It is used to model and analyze systems that involve uncertainty, such as financial markets, weather patterns, and population growth. Stochastic calculus is important because it provides a framework for understanding and predicting the behavior of these complex systems.

2. How is stochastic calculus different from traditional calculus?

Traditional calculus deals with deterministic functions, where the output is completely determined by the input. In stochastic calculus, the output is a random variable that can take on different values with a certain probability. This makes stochastic calculus more suitable for modeling and analyzing systems with uncertainty.

3. What is the expected value formula and how is it used in stochastic calculus?

The expected value formula is a mathematical concept that calculates the average value of a random variable. In stochastic calculus, this formula is used to determine the expected value of a system over time, taking into account the various possible outcomes and their probabilities. This allows us to make predictions about the future behavior of the system.

4. What are some practical applications of stochastic calculus?

Stochastic calculus has many practical applications, particularly in the fields of finance, economics, and engineering. It is used to model and analyze stock prices, interest rates, and other financial instruments. It is also used in risk management and optimization problems, such as portfolio management and hedging strategies.

5. How can understanding stochastic calculus benefit me as a scientist?

As a scientist, understanding stochastic calculus can help you better analyze and predict the behavior of complex systems that involve uncertainty. It can also assist in making data-driven decisions and designing experiments with more accurate and reliable outcomes. Additionally, many scientific fields, such as biology and ecology, use stochastic models to study natural systems, making knowledge of stochastic calculus essential for researchers in these areas.

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