Table of Contents

- 1 How do you solve conditional expectations?
- 2 Is conditional expectation random?
- 3 How do you find conditional probability?
- 4 What is conditional expectation in probability?
- 5 How do you write probability as an expectation?
- 6 What are the 5 rules of probability?
- 7 How to find an expectation in conditional expectation?
- 8 Which is the partition theorem for conditional expectation?

## How do you solve conditional expectations?

The conditional expectation, E(X |Y = y), is a number depending on y. If Y has an influence on the value of X, then Y will have an influence on the average value of X. So, for example, we would expect E(X |Y = 2) to be different from E(X |Y = 3).

**How do you solve a conditional probability distribution?**

First, to find the conditional distribution of X given a value of Y, we can think of fixing a row in Table 1 and dividing the values of the joint pmf in that row by the marginal pmf of Y for the corresponding value. For example, to find pX|Y(x|1), we divide each entry in the Y=1 row by pY(1)=1/2.

### Is conditional expectation random?

1. Conditional expectation, given the discrete random variable Y , is a lin- ear operator on the vector space of random variables that are defined on the same sample space as Y and have finite expected values.

**Do probabilities have expectations?**

If a random variable has exclusively numeric outcomes, then we can talk about its expected value, or expectation. The expected value of a numeric random variable is a weighted average of its possible numeric outcomes, where the weights are the probabilities of the outcome occurring.

#### How do you find conditional probability?

Conditional probability is defined as the likelihood of an event or outcome occurring, based on the occurrence of a previous event or outcome. Conditional probability is calculated by multiplying the probability of the preceding event by the updated probability of the succeeding, or conditional, event.

**Is conditional probability linear?**

With C1 = σ(Θ) and C2 = σ(Y, Θ), we see that E(r(X)|C1) will be a version of E(r(X)|C2) for every function r(X) with defined mean. The next lemma shows that conditional expectation is linear. Lemma 19 (Linearity). If E(X), E(Y ), and E(X + Y ) all exist, then E(X|C) + E(Y |C) is a version of E(X + Y |C).

## What is conditional expectation in probability?

In probability theory, the conditional expectation, conditional expected value, or conditional mean of a random variable is its expected value – the value it would take “on average” over an arbitrarily large number of occurrences – given that a certain set of “conditions” is known to occur.

**Is expectation same as probability?**

Mathematical expectation, also known as the expected value, which is the summation of all possible values from a random variable. It is also known as the product of the probability of an event occurring, denoted by P(x), and the value corresponding with the actually observed occurrence of the event.

### How do you write probability as an expectation?

The basic expected value formula is the probability of an event multiplied by the amount of times the event happens: (P(x) * n).

**Why do you think you need conditional probability?**

There are often only a handful of possible classes or results. For a given classification, one tries to measure the probability of getting different evidence or patterns. Using Bayes rule, we use this to get what is desired, the conditional probability of the classification given the evidence.

#### What are the 5 rules of probability?

Basic Probability Rules

- Probability Rule One (For any event A, 0 ≤ P(A) ≤ 1)
- Probability Rule Two (The sum of the probabilities of all possible outcomes is 1)
- Probability Rule Three (The Complement Rule)
- Probabilities Involving Multiple Events.
- Probability Rule Four (Addition Rule for Disjoint Events)

**How are conditional expectations related to random variables?**

And so conditional PMFs also have associated expectations, which we call conditional expectations. The case where we condition on random variables is exactly the same. We let the event, A, be the event that Y takes on a specific value.

## How to find an expectation in conditional expectation?

We know that an expectation can be found by taking the conditional expectations under each one of the scenarios and weighing them according to the probabilities of the different scenarios. Again, let the event that Y takes on a specific value be a different scenario.

**Which is the Tower property of conditional expectation?**

This is called the “tower” (or sometimes “smoothing”) property ofconditional expectation. It’s especially useful when we have entirenested families (calledﬁltrations) of σ-algebras{Fn}withn < m⇒Fn ⊆ Fm; for example,Fn=σ{Xj : j≤n}for a family{Xn}of(non-necessarily-independent) random variables.

### Which is the partition theorem for conditional expectation?

xfXjY(xjy) and the partition theorem is E[X] = X. y. E[XjY = y]P(Y = y) A.2 Conditional expectation as a Random Variable. Conditional expectations such as E[XjY = 2] or E[XjY = 5] are numbers. If we consider E[XjY = y], it is a number that depends on y. So it is a function of y.