Conditional probability and time series

In summary, the conversation revolves around calculating the probability of an earthquake occurring during a positive tidal stress. The speaker has calculated the probability of positive and negative tidal stress, but is seeking further information and clarification on the connection between tidal stress and earthquakes. They also mention a previous discussion on this topic.
  • #1
sthoriginal
9
0
I really need some help here, will appreciate any effort. I calculated time series of tidal stresses. It turned out that the probability of having positive tidal stress is 0.4 and negative - 0.6 (I counted up number of hours when the stress was positive/negative and divided by the total number of hours). I want to find out how to calculate probability of the earthquake happening during 1h around the maximum (that is when the stress is positive)? Many thanks
 
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  • #2
The information seems too meager. You need more information about earthquake probability and the connection (if any) between earthquake and tidal stress.
 

FAQ: Conditional probability and time series

What is conditional probability?

Conditional probability is the likelihood of an event occurring given that another event has already occurred. It is calculated by dividing the probability of the two events occurring together by the probability of the first event occurring. In other words, it is the probability of event A given that event B has occurred.

How is conditional probability used in time series analysis?

In time series analysis, conditional probability is used to predict future events based on past events. By analyzing the relationship between different events over time, we can calculate the conditional probability of future events occurring and make more accurate predictions.

What is the difference between conditional probability and joint probability?

Conditional probability is the likelihood of an event occurring given that another event has already occurred, while joint probability is the likelihood of two events occurring together. In other words, conditional probability takes into account additional information, while joint probability does not.

How can we calculate conditional probability in a time series?

To calculate conditional probability in a time series, we first need to identify the two events that we are interested in. Then, we can use the formula P(A|B) = P(A and B) / P(B), where P(A|B) is the conditional probability of event A given that event B has occurred, P(A and B) is the joint probability of the two events occurring together, and P(B) is the probability of event B occurring.

What are some common applications of conditional probability and time series analysis?

Conditional probability and time series analysis are commonly used in fields such as finance, economics, and weather forecasting. They can help us make more accurate predictions and decisions based on historical data and patterns. For example, in finance, conditional probability can be used to predict stock market trends and make investment decisions. In weather forecasting, time series analysis can be used to predict future weather patterns based on past data.

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