How to derive $P(PH)$ without using a joint distribution table?

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In summary, probability is a measure of the likelihood of an event occurring, expressed as a number between 0 and 1. It is calculated by dividing the number of favorable outcomes by the total number of possible outcomes. Theoretical probability is based on mathematical calculations, while experimental probability is based on actual data. Sample size affects probability by increasing accuracy. Probability is used in various real-life situations, such as sports predictions, weather forecasting, and risk assessment. It is also used in scientific research and decision-making processes.
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Given $P(PH | H) = 0.8$ and $P(PH | \lnot H) = 0.3 $ and $P(H) = 0.1$ how can I derive $P(PH)$ without resorting to a joint distribution table?
 
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tmt said:
Given $P(PH | H) = 0.8$ and $P(PH | \lnot H) = 0.3 $ and $P(H) = 0.1$ how can I derive $P(PH)$ without resorting to a joint distribution table?

Hi tmt,

We can use that (applying sum rule and general product rule):
$$P(A) = P((A\land B)\lor (A\land \lnot B)) = P(A\land B) + P(A\land\lnot B) = P(A\mid B)P(B) + P(A\mid \lnot B)P(\lnot B)$$
 

FAQ: How to derive $P(PH)$ without using a joint distribution table?

What is probability?

Probability is a measure of the likelihood of an event occurring. It is expressed as a number between 0 and 1, with 0 representing impossibility and 1 representing certainty.

How is probability calculated?

The formula for probability is: P(E) = Number of favorable outcomes / Total number of possible outcomes. This means that the probability of an event is equal to the number of ways that event can occur divided by the total number of possible outcomes.

What is the difference between theoretical and experimental probability?

Theoretical probability is based on mathematical calculations and assumes that all outcomes are equally likely. Experimental probability is based on actual data from experiments or observations and may vary from the theoretical probability.

How does sample size affect probability?

The larger the sample size, the more accurate the probability will be. This is because a larger sample size reduces the effects of chance and random variations in the data.

How is probability used in real life?

Probability is used in many real-life situations, such as predicting the outcome of sports games, weather forecasting, and risk assessment in insurance and finance. It is also used in scientific research to determine the likelihood of certain outcomes and to make informed decisions based on evidence.

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