Conditional probability
Conditional probability is a mathematical concept used to calculate the probability of an event occurring, given that another event has already happened
Conditional probability is a mathematical concept used to calculate the probability of an event occurring, given that another event has already happened. It measures the likelihood of an outcome, taking into account some prior knowledge or information about the situation at hand.
In order to calculate conditional probability, we use the formula:
P(A|B) = P(A and B) / P(B)
Here, P(A|B) represents the probability of event A occurring given that event B has already happened. P(A and B) refers to the probability of both events A and B happening together. P(B) represents the probability of event B occurring.
To better understand conditional probability, let’s consider an example. Suppose we have a bag that contains 4 red marbles and 6 blue marbles. We randomly select a marble from the bag, without replacing it, and then select a second marble.
Now, let’s define two events:
A: The first marble selected is red.
B: The second marble selected is blue.
We want to find the probability of event A happening given that event B has already occurred, or P(A|B). To calculate this, we would first determine the probability of both events happening at the same time, P(A and B). In this case, since we are selecting marbles without replacement, the probability of event A and B occurring together is:
P(A and B) = (4/10) * (6/9) = 24/90 = 4/15
Next, we need to calculate the probability of event B happening, which is selecting a blue marble as the second marble. Since we have already selected a red marble, there are now 9 marbles remaining in the bag, of which 6 are blue. Therefore, P(B) = 6/9.
Finally, we can use these values to find P(A|B) using the formula:
P(A|B) = P(A and B) / P(B) = (4/15) / (6/9) = (4/15) * (9/6) = 2/5
So, the probability of selecting a red marble as the first marble given that the second marble is blue is 2/5.
Conditional probability allows us to update our understanding of probabilities as more information becomes available. It is often used in various fields, such as statistics, machine learning, and decision-making, to make more accurate predictions and draw conclusions in real-life situations.
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