markov programming 中文意思是什麼
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In the study of risk theory, a class of continuous time risk process with deficit - time geometry distribution of claim inter - occurrence time was made into a strong piecewise - deterministic markov process with the theory of piecewise - deterministic markov process and by introducing a supplementary variable. martingale approach is one of the most powerful methods of pdmp. the programming process is getting the ruin probability from the martingale construction. we use the idea of change of measure in the programming process and find the result and the function of adjustment coefficient
本文應用逐段決定馬爾可夫過程理論及補充變量技巧，使索賠到達間隔服從虧時幾何分佈的連續時間風險過程成為齊次強馬爾可夫過程，然後利用pdmp中的鞅方法（用廣義生成運算元得出鞅）推導了鞅的形式，作為該風險模型索賠額分佈為一般分佈下的破產概率的一般表達式，其中用到了測度變換的思想。 -
Markov decision process, in short mdp, is also called sequential stochastic optimization stochastic optimum control. the controlled markov process or stochastic dynamic programming is the theory on stochastic sequential decision
馬爾可夫決策過程（ markovdecisionprocesses ，簡稱mdp ，又稱序貫隨機最優化、隨機最優控制、受控的馬爾可夫過程或隨機動態規劃）是研究隨機序貫決策的問題的理論。 -
It can take advantage of the advancement of hmm and gmm, utilize dynamic programming technique to realize the nonlinear time alignment between speech feature vectors and markov state sequences, use expectation - maximum algorithm to re - estimate the gmm parameters and finally employ levenshtein distance to calculate the word error rate between the recognized and expected results
它將隱markov模型和gaussian混合密度分佈緊密聯系，結合動態規劃演算法對時間序列和markov狀態鏈進行非線性時間對齊，並運用em演算法對gaussian混合模型的參數進行重新估計，識別出來的結果與期望結果採用levenshtein距離進行比較並得出其字誤差率。
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