Applications Of Probability In Probability

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In definition, probability refers to the measure of the likelihood of an event happening. The probability for any event occurring falls between 1 percent and 100 percent thus meaning that the interpreted meaning of a probability equals the subject meaning held of the probability (Grinstead et al, 1997). However, it is worth noting that the application of probability or assigning of probability to the events in the effort to gratifying the axioms of probability follows some rules or basics (Grinstead et al, 1997).
One of the basics is the random variable that refers to the quantity with uncertain expected future values. For instance, it is of uncertainty to determine the price of the products in the near future given the changing dynamic
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Here, for instance, the range of outcomes in an expected rate of return is naturally dependent on the particular investment or proposition (Grinstead et al, 1997).That explains why persons involved in the betting activities stand a high chance of losing on their money or possession as a result of the minimal chance of winning the betting.
The third critical basic concept for probability is events in that a mutually exclusive list if events will results in a possibility of one of the events taking place and not all of them. That in turn means that exhaustive events results in the incorporation of all the potential outcomes in the defined events (Grinstead et al, 1997).
It thus inferable that the definition of probability falls into two primary categories namely the probability of any event is a number between 0 and 1 and the sum of probabilities of all events equals 1, provided the events are both mutually exclusive and exhaustive (Grinstead et al,
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Therefore, the calculation of subjective probability follows experience and judgment in making forecasts or modifying the probabilities indicated from a purely empirical approach (Grinstead, Snell, & Grinstead, 2006).
Thirdly, Priori Probabilities are a representation of probabilities that are objective and based on deduction and reasoning about a particular case. That explains why in one instance one may forecast say a 60 percent likelihood of an event occurring and at the same time forecast a 40 percent likelihood of a contrary event occurring (Grinstead, Snell, & Grinstead, 2006). For instance, the probability that it will rain tomorrow may raise another probability of there being no rain tomorrow.
Fourthly, the unconditional probability is the likelihood of one event occurring in that, for instance, the probability of event 1 will be an unconditional probability P (1). Practically, the belief by one that is 50% investment will yield a return of 10% in future, and then the unconditional probability of that event will be 0.5 (Grinstead, Snell, & Grinstead, 2006). Fifthly, conditional probability covers the probability of one event occurring given that another event has already taken

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