markov property 中文意思是什麼
markov property
解釋
markov性質-
In the third section, the asymptotic equipartition property ( aep ) of second order markov chain field on a class of tree is studied by using the analytic approach. a net is constructed on a product space, then some strong laws of large numbers of second order markov chain field are proved by using the differentiation of measure on a net. furthermore, shannon - mcmillan theorem is extended to the case of second order markov chain field
本文的第三章是用分析的方法研究一類樹上的二重馬氏鏈場的漸進均分割性，為此先構造了一個乘積空間上的網，然後利用網微分法證明了一些二重馬氏鏈場上的大數定律，並將shannon - mcmillan定理推廣到二重馬氏鏈場上。 -
For instance, a new property of network traffic ( self - similarity ) has been observed in diverse networking contexts. some old protocols, policies and evaluate methods which are based on previous markov network model are not suitable for this self - similar traffic. and when tcp data share bandwidth with udp data that is popular for multimedia applications over the internet, it also causes fairness problem
多媒體業務流會給整個網路的性能帶來極大的影響，如：多媒體業務導致網路流自相似現象加重，使網路的原有的markov模型基礎上的協議、策略，及其評價方法不夠準確，導致丟失率上升，網路性能下降；多媒體業務中使用的大量udp包與普通數據流tcp包共存時對公平性的影響等等。 -
For quantitative analysis of the combat platform fire application, the markov chain model of combat platform with reciprocal striking, hasty break through and shooting to dense target is studied by setting up markov chain which state and time are discrete according to the markov property in this process
摘要針對定量分析戰斗平臺火力運用問題，根據該過程所具有的馬爾可夫性特點，將其描述為狀態離散、時間離散的馬爾可夫鏈，由此研究了一對一格鬥、倉促突破戰斗、對密集目標群射擊等情況下的馬爾可夫鏈模型。 -
Invariant distribution is a greatly important property of standard transition function in continuous - time markov chains and jump processes, it is of considerably significnce to study it
不變分佈是連續時間馬氏鏈中標準轉移函數及跳過程的一個重要性質，對不變分佈的討論有著十分重要的意義。 -
Markov subshift and pseudo orbit tracing property
鏈與偽軌追蹤性 -
On markov property of the risk reserve processes and continuous - time risk models with discete - type inter - arrival times
盈餘過程的馬氏性與索賠到達間隔分佈為離散型的連續時間風險模型 -
Subsequently we make use of the isomorphism property between the behavior of petri nets with exponentially distributed transition rates and markov process to acquire markov chain, and compute the subsystem ' s mean time to delay and transfer probability of subsequencial state, which present the theoretical evidence for intrusion detection system ' s design
隨后利用隨機petrinet和連續時間的馬爾可夫鏈同構的性質，應用所獲得的同構馬爾可夫鏈對求得穩定狀態概率的子系統的平均延時時間和后繼狀態轉移概率進行了詳細的計算，從而為入侵檢測系統的設計提供理論根據。 -
The introduction black - scholes models still assumed, namely the introduction of modern process ( wiener process, also called brownian motion ) to save the stock yield random fluctuations, weak markets and the effectiveness of the use of consistent share of the techniques ( ( markov property ) to describe the stock price change random process, the use of risk - neutral pricing theory through the analysis of the nature of asset price process martingale, established european style to the value of stock options with mathematical models
本文仍然引入black - scholes的模型假定，也即引入維納過程（ wienerprocess ， alsocalledbrownianmotion ）來刻畫股票收益率的隨機波動，採用與弱型市場有效性相一致的股價的馬爾可夫性（ markovproperty ）來描述股票價格變化的隨機過程，運用風險中性定價理論，通過分析資產價格過程鞅的性質，建立了歐式再裝股票期權價值的數學模型。 -
The property of states for skew product markov chains
繞積馬氏鏈的狀態性質 -
This article gets some good results on the two - order markov chains on the base of the studies of one - order markov chains : in infinite experiment, the frequency of times of stationary state is accessing to transition probability. the indication function which is the times of appearance is a special function, so this paper in forth chapter continue to study more general function regarding to two - order markov chains, which is the property of the function of two - order markov chains. in chapter five, this paper study the convergence of cesaro averages for two - order morkov chains
在大量試驗中，固定狀態出現次數的相對頻率可以用條件概率來加以說明，它是對一重馬氏鏈強極限性質的一個推廣；本論文進一步引入了有關二重馬氏鏈更廣泛的函數即二重馬氏鏈泛函，並研究了其強極限的性質；最後研究了二重馬氏鏈泛函的平均收斂性。 -
At last, several examples of genetic algorithm improved by simulated annealing algorithm, which adopted different functions deciding whether to accept a state and different markov chains, are used to analyze the property of this algorithm
最後，用計算試驗對比了遺傳演算法和遺傳模擬退火演算法的收斂性能，對比了採用不同狀態函數和不同markov鏈的遺傳模擬退火演算法的收斂性能，並對結果進行了分析。
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