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利用汇总数据估计时变马尔可夫过程AI 解读

The exact stochastic character of observed data from a Markov process is derived for the case where only aggregate stocks, as opposed to individual transitions, are observed. Particular attention is devoted to the distinction between data generated by a panel study, where a single group of individuals is followed over time, and that generated by random sampling, where the observed groups are not identical over time. Several alternative estimators are developed which take…

多期决策问题的自适应学习规则AI 解读

Zellner [11], and Chow [3], is to deal with the uncertainty by treating the model parameters as independent, identically distributed random variables in each period,' yielding what Zellner calls rules. Although the sequential updating rules do capture the uncertainty, they ignore the possibility of ongoing estimation in the formulation of decision rules. The purpose of this paper is to develop an adaptive learning decision rule for the multiperiod problem. This rule…

矩阵求导及其在自适应线性决策问题中的应用AI 解读

A theory of matrix differentiation is presented which uses the concept of a matrix of derivative operators. This theory allows matrix techniques to be used in both the derivation and the description of results. Several new operations and identities are presented which facilitate the process of matrix differentiation. The derivative theorems and new operations are then applied to the problem of determining optimal policies in a linear decision model with unknown coefficients,…