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What is a Probability is used to estimate probabilities by making certain assumptions about an experiment?
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Gwerth Probablitiy
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Frequency approach for probability?
Well, that's not much of a question. Perhaps you are asking: What is the frequency interpretation of probability? This is called the classical interpretation of probability. Given n independent and identical trials with m occurrences of of a particular outcome, then the probability of this outcome, is equal to the limit of m/n as n goes to infinity. If you are asking: How can probabilities be estimated given data, based on frequency approach? A table is constructed, with intervals, and the number of events in each interval is calculated. The number of events divided by the total number of data is the relative frequency and an estimate of probability for the particular interval.
How can a probability be estimated?
One way to estimate the probability of an event is to use a theoretical model to compare the relative likelihood of the event compared to all possible outcomes.
What circumstances can a small treatment effect be statistically significant?
For a given experiment, and a given sample size, there is a probability that a treatment effect of a given size will yield a statistically significant finding. That is, if the treatment effect is 1 unit, then that probability (the power) might be 50%, and the power for a treatment effect of 2 units might be 75%, etc. Unfortunately, before the experiment, we don't know the treatment effect size, and indeed after the experiment we can only estimate it. So a statistically significant result means that, whatever the treatment effect size happens to be, Mother Nature gave you a "thumbs up" sign. That is more likely to happen with a large effect than with a small one.
What is Risk neutral probability measure?
A probability measure allocates a non-negative probability to each possible outcome. All individual probabilities together add up to 1. The "risk-neutral probability measure" is used in mathematical finance. Generally, risk-neutral probabilities are used for the arbitrage-free pricing of assets for which replication strategies exist. This is about relative pricing, based on possible replication strategies. The first argument is that a complete and arbitrage-free market setting is characterised by unique state prices. A state price is the price of a security which has a payoff of 1 unit only if a particular state is reached (these securities are called Arrow securities). In a complete market, every conceivable Arrow security can be traded. It is more easy to visualise these securities in terms of discrete scenarios. (On a continuous range of scenarios we would have to argue in terms of state price density.) The arbitrage-free price of every asset is the sum (over all scenarios) of the scenario-payoff weighted with its state price. Any pricing discrepancy with regards to an implicit state price would enable arbitrage in a complete market. The assumption is that the pursuit of such opportunities drives the prices towards the arbitrage-free levels. Hence the state prices are unique. Since the whole set of Arrow securities is the same as a risk-free bond (sure payoff of 1 unit at maturity), the price of the whole set of Arrow securities must be e^(-rt) (assuming we are now at maturity minus t). Risk-neutral probabilities can then be defined in terms of state prices, or vice versa. A probability measure has to fulfil the condition that the sum of all individual probabilities adds up to 1. Therefore, if we want to create an artificial probability distribution based on the state price distribution, we have to multiply each state price with e^(rt) in order to obtain its probability equivalent. It is not surprising then that any expectation taken under the risk-neutral probability measure grows at the risk-free rate. This is an artificial probability measure, why should we create such a construct? This connection allows us to exploit mathematical tools in probability theory for the purpose of arbitrage-free pricing. The main difficulty about risk-neutral probabilities is that the probability concepts used have not initially been developped for the purpose of financial pricing, therefore, two different languages are used, which can easily be confusing. The economic interpretation of a risk-neutral probability is a state price compounded at the risk-free rate. Anything that has an effect on a state price (preferences, real probability, ...), has an effect on the risk-neutral probability. So now we have a bridge to go from state prices to risk-neutral probabilities and back again. What is this good for? According to the second argument, we can, under certain conditions, specify the unique risk-neutral probability distribution of an underlying asset price with the help of an only incomplete specification of its real probability distribution, thanks to the Girsanov Theorem. If the innovation in the price of the underlying asset is driven by a Brownian motion, then all we need to obtain the risk-neutral probability distribution is the volatility parameter. What can we now do with this risk-neutral probability distribution? We can use the first argument to convert the obtained risk-neutral probability distribution back to a state price distribution, and the state price distribution applied to the payoff distribution (i.e. taking the sum over all scenarios) leads to the arbitrage-free price. These arguments save us a lot of trouble when trying to calculate the arbitrage-free price of an asset. They allow us to avoid the estimation of risk premia, by implicitly using those incorporated in the underlying asset price. The arbitrage-free price is, however, NOT independent of risk-premia. The price of the underlying asset is part of the pricing equation, and the risk-premia are inherent in this price, but because the price of the underlying asset is known to us, we obviously do not need estimate it. It is important to emphasise that the risk-neutral valuation approach only works if the asset to be priced can be perfectly replicated. This is often not true in reality, especially when dynamic replication strategies are involved. Paper explaining risk-neutral probabilities: http://ssrn.com/abstract=1395390
You randomly select 500 students and observe that 85 of them smoke Estimate the probability that a randomly selected student smokes?
85/500 = 17%
what is the difference between prior and posterior probability?
A posterior probability is the probability of assigning observations to groups given the data. A prior probability is the probability that an observation will fall into a group before you collect the data. For example, if you are classifying the buyers of a specific car, you might already know that 60% of purchasers are male and 40% are female. If you know or can estimate these probabilities, a discriminant analysis can use these prior probabilities in calculating the posterior probabilities. When you don't specify prior probabilities, Minitab assumes that the groups are equally likely.
How can you use simulations to estimate probability?
Experimental or empirical probability is estimated from repeated trials of an experiment. However, instead of actually carrying out the experiment a very large number of times, it may be possible to simulate them.
How can probabilities determined?
objective probabilities: For example, flip a coin one hundred times counting the number of 'heads' that occur and using this to estimate the probability that a 'head' will occur on any given single flip.subjective probabilities: For example, as an experienced weather forecaster one might take into consideration, as far as possible, all information available about one's locale and offer the 'probability of precipitation'. This is often called a degree of belief.
What does expermenal probabiliy mean?
It means that the probability is calculated (or more precisely, estimated) based on experiment. For example, if a certain event occurs 70 times in 1000 tries, you can estimate the probability to be approximately 7%.
What happens when experimental probability is repeated?
You obtain an estimate of the probability that will usually be different from previous result(s).You obtain an estimate of the probability that will usually be different from previous result(s).You obtain an estimate of the probability that will usually be different from previous result(s).You obtain an estimate of the probability that will usually be different from previous result(s).
An approximation of a number based on reasonable assumptions?
estimate
What is the estimate of the likelihood that a hazard will cause an impact on an operation?
Probability
What is an approximation of a number based on reasonable assumptions is called?
estimate
Frequency approach for probability?
Well, that's not much of a question. Perhaps you are asking: What is the frequency interpretation of probability? This is called the classical interpretation of probability. Given n independent and identical trials with m occurrences of of a particular outcome, then the probability of this outcome, is equal to the limit of m/n as n goes to infinity. If you are asking: How can probabilities be estimated given data, based on frequency approach? A table is constructed, with intervals, and the number of events in each interval is calculated. The number of events divided by the total number of data is the relative frequency and an estimate of probability for the particular interval.
Is probability an observation?
No. A probability means, how likely it is for something to happen. An observation of SEVERAL similar events can give you a good ESTIMATE of the probability.
What does estimated experimental probability mean please someone answer?
One way of finding the probability is to carry out an experiment repeatedly. Then the estimated experimental probability is the proportion of the total number of repeated trials in which the desired outcome occurs.Suppose, for example you have a loaded die and want to find the probability of rolling a six. You roll it again and again keeping a count of the total number of rolls (n) and the number of rolls which resulted in a six, x. The estimated experimental probability of rolling a six is x/n.
Relationship between relative frequency and probability of an event?
The relative frequency is an estimate of the probability of an event.
