Search results
Results from the WOW.Com Content Network
A convex function of a martingale is a submartingale, by Jensen's inequality. For example, the square of the gambler's fortune in the fair coin game is a submartingale (which also follows from the fact that X n 2 − n is a martingale). Similarly, a concave function of a martingale is a supermartingale.
Markov-chains have been used as a forecasting methods for several topics, for example price trends, [8] wind power [9] and solar irradiance. [10] The Markov-chain forecasting models utilize a variety of different settings, from discretizing the time-series [ 9 ] to hidden Markov-models combined with wavelets [ 8 ] and the Markov-chain mixture ...
Many components, such as spark plugs or a gas-powered engine, can make maintenance costs expensive. “Without spark plugs to replace or oil to change, electric vehicles have a clear leg up on ...
Energy converter is an example of an energy transformation. For example, a light bulb falls into the categories energy converter. η = P o u t P i n {\displaystyle \eta ={\frac {P_{\mathrm {out} }}{P_{\mathrm {in} }}}} Even though the definition includes the notion of usefulness, efficiency is considered a technical or physical term.
Gas cars also refuel more quickly than even the fastest-charging EVs, reducing downtime and overall trip length on longer journeys. Fixing a gas car tends to be less expensive as well, despite the ...
The drawback of using a shared platform is cost inefficiencies. While the up-front cost is likely much higher for dedicated EV and gas-powered platforms, down-the-line efficiencies of scale and ...
The martingale representation theorem can be used to establish the existence of a hedging strategy. Suppose that ( M t ) 0 ≤ t < ∞ {\displaystyle \left(M_{t}\right)_{0\leq t<\infty }} is a Q-martingale process, whose volatility σ t {\displaystyle \sigma _{t}} is always non-zero.
Markov processes are stochastic processes, traditionally in discrete or continuous time, that have the Markov property, which means the next value of the Markov process depends on the current value, but it is conditionally independent of the previous values of the stochastic process. In other words, the behavior of the process in the future is ...