Dependencies between two probability sample spaces OmegaA, OmegaB

In summary: This approach is used in many different fields, including statistics, economics, and machine learning. In summary, the conversation discusses two sample spaces, their outcomes, and the dependencies between them, which can be studied using probability theory, specifically conditional and joint probabilities.
  • #1
Grag
1
0
Suppose you have two sample spaces [itex]\Omega_A[/itex] and [itex]\Omega_B[/itex]. First space contain two outcomes A and A', where A = [itex]\Omega_A[/itex] - A', i.e. the opposite outcomes.

Second space also contains two opposite outcomes B = [itex]\Omega_B[/itex] - B'.

Now, suppose that there are dependencies between A, A' and B, B' like below:
A [itex]\rightarrow[/itex] B'
A'[itex]\rightarrow[/itex] B v B' (logical alternative)
B [itex]\rightarrow[/itex] A'
B'[itex]\rightarrow[/itex] A v A' (logical alternative)


So, occurrence of non-negative (i.e. A or B) event excludes such (i.e. non negative) event in the other space. Occurrence of a negative event (A' or B') does not imply anything in the other space. So the two "logical alternative" implications could be in fact skipped:
A [itex]\rightarrow[/itex] B'
B [itex]\rightarrow[/itex] A'


My question:
- how to approach this?
- is this covered in some math topic?

Best Regards,
Greg
 
Physics news on Phys.org
  • #2
orThis type of problem is often studied in the field of probability theory. The concept of conditional probability can help you understand the relationships between the two events, and how they affect each other. The conditional probability of event A given event B is the probability that A occurs given that B has already occurred. You can also calculate the probability of both events occurring together, which is known as the joint probability. With these tools, you can analyze how the different events interact with each other and how their probabilities are affected by one another.
 

1. What is the definition of probability sample spaces OmegaA and OmegaB?

Probability sample spaces OmegaA and OmegaB refer to two separate sets of all possible outcomes of a random experiment or process. These outcomes are mutually exclusive and exhaustive, meaning that they cover all possible outcomes and cannot occur simultaneously.

2. How are the probabilities of outcomes in OmegaA and OmegaB related?

The probabilities of outcomes in OmegaA and OmegaB are related through their shared dependencies. This means that the occurrence of an outcome in one sample space may affect the occurrence of an outcome in the other sample space. These dependencies can be represented using conditional probability or joint probability.

3. Can two events be independent if they have dependencies between their respective sample spaces?

No, two events cannot be considered independent if there are dependencies between their respective sample spaces. Independence between events means that the occurrence of one event does not affect the probability of the other event occurring. However, if there are dependencies between the sample spaces, then the events are not truly independent.

4. What is the difference between joint probability and conditional probability?

Joint probability refers to the probability of two events occurring simultaneously. It is calculated by multiplying the individual probabilities of each event. Conditional probability, on the other hand, refers to the probability of one event occurring given that another event has already occurred. It is calculated by dividing the joint probability by the probability of the conditioning event.

5. How can dependencies between two sample spaces affect the overall probability of an event?

Dependencies between two sample spaces can affect the overall probability of an event in several ways. If the two events are positively dependent, meaning that the occurrence of one event increases the probability of the other event occurring, then the overall probability of the event will increase. If the two events are negatively dependent, meaning that the occurrence of one event decreases the probability of the other event occurring, then the overall probability of the event will decrease. In both cases, the dependencies between the sample spaces can provide valuable information for predicting the outcome of an event.

Similar threads

  • Set Theory, Logic, Probability, Statistics
Replies
6
Views
1K
  • Set Theory, Logic, Probability, Statistics
Replies
10
Views
2K
  • Set Theory, Logic, Probability, Statistics
Replies
3
Views
998
  • Set Theory, Logic, Probability, Statistics
2
Replies
36
Views
3K
Replies
12
Views
724
  • Set Theory, Logic, Probability, Statistics
Replies
6
Views
2K
  • Set Theory, Logic, Probability, Statistics
Replies
16
Views
1K
  • Set Theory, Logic, Probability, Statistics
Replies
10
Views
2K
  • Set Theory, Logic, Probability, Statistics
Replies
7
Views
4K
  • Set Theory, Logic, Probability, Statistics
Replies
4
Views
1K
Back
Top