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Dynamic modeling uses sequence and state diagrams to create a collection of multiple state diagrams with one state chart existing for each class with dynamic behavior. The functional model, on the other hand, provides a structured model of all of the functions within the system, where sequences lead to differing states are shown on one diagram, rather than as a series of charts.
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What is the difference between static white box testing and dynamic white box testing?
1.Static testing involves verification of activities of the developed software where as dynamic testing involves working with the software, giving input values and checking if the output is as expected. 2.Review's, Inspection's and Walkthrough's are static testing methodologies.Unit Tests, Integration Tests, System Tests and Acceptance Tests are few of the Dynamic Testing methodologies. 3.Static testing is done before the code deployment (verification stage) where as dynamic testing is done after code deployment(validation stage). 4.Static testing is more cost effective then dynamic testing.
What is the difference between branch and bound and greedy method?
Branch and bound method is used for optimisation problems. It can prove helpful when greedy approach and dynamic programming fails. Also Branch and Bound method allows backtracking while greedy and dynamic approaches doesnot.However it is a slower method.
What type of population (open vs closed) is most common?
Open populations are more common in nature, as they allow for immigration and emigration, enabling individuals to move in and out of the population. This dynamic can lead to greater genetic diversity and adaptability. Closed populations, on the other hand, are more isolated and experience limited interaction with outside individuals, which can lead to inbreeding and reduced genetic variation. Overall, the fluidity of open populations makes them more prevalent in many ecosystems.
Why would it be difficult to include water vapor on a pie chart?
It would be difficult to include water vapor on a pie chart because water vapor is a gas and not a tangible substance that can be visually represented in a chart format. Pie charts are typically used to show proportions of discrete categories or values, and water vapor is a continuous variable that does not fit into this framework. Additionally, water vapor is often present in varying and dynamic amounts in the atmosphere, making it challenging to accurately quantify and represent on a static chart.
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
What is difference between dynamic and volumetric compressor?
what is difference between dynamic and volumatic compressor
Is there a functional difference between DRAM and SDRAM memory, and if so, which is best for the price?
It's the same thing, Synchronous Dynamic RAM (SDRAM) is typically just a different name for DRAM.
What is the difference between static and dynamic fighting?
Static stays the same and dynamic is always different.
What is the difference between a stable system and dynamic system?
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What is Difference between static and dynamic list?
A static one cannot change, while a dynamic one can.
What is the general difference between a static IP and a dynamic IP?
The general difference between a static IP and dynamic IP is that a static IP is reserved and does not change. A dynamic IP on the other hand changes each time one logs on.
Why are they call Dynamical Systems as opposed to Dynamic Systems. What is the difference between the words Dynamic and Dynamical?
See What_is_the_difference_between_dynamical_and_dynamic
What is the difference between static pressure and dynamic pressure?
The main difference of static pressure and dynamic pressure is:- static pressure is exerted by fluid at rest but dynamic pressure is pressure exerted by fluid in motion.
Difference between static multiplier dynamic multiplier?
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What is dynamic business?
Dynamic Business Modeling ("DBM") describes the ability to automate business models within an open framework. The independent analyst firm Gartner has recently called Dynamic Business Modeling "critical for BSS solutions to succeed".
Difference between greedy algorithm and dynamic programming?
the basic difference between them is that in greedy algorithm only one decision sequence is ever generated. where as in dynamic programming many decision sequences are generated.
What are the difference between static and dynamic hashing in DBMS?
Search operation in static hashing is time consuming, but in dynamic hashing it is not.
