Inference with Constrained Hidden Markov Models in PRISM

A Hidden Markov Model (HMM) is a common statistical model which is widely used for analysis of biological sequence data and other sequential phenomena. In the present paper we show how HMMs can be extended with side-constraints and present constraint solving techniques for efficient inference. Defining HMMs with side-constraints in Constraint Logic Programming have advantages in terms of more compact expression and pruning opportunities during inference. We present a PRISM-based framework for extending HMMs with side-constraints and show how well-known constraints such as cardinality and all different are integrated. We experimentally validate our approach on the biologically motivated problem of global pairwise alignment

RECONSTRUCTING DISAGGREGATE PRODUCTION FUNCTIONS

This paper demonstrates a method for reconstructing flexible form production functions using minimal disaggregated data sets. The policy focus of our approach puts emphasis on the ability of the model to reproduce the existing production system and predict the disaggregate outcomes of policy changes. We combine Positive Mathematical Programming (PMP) with Generalized Maximum Entropy (GME) estimation to capture the individual heterogeneity of the local production environment, and allow the reconstructed production function to precisely replicate the input usage and outputs produced in the base year. Since we can generate demand, supply and substitution elasticities from the reconstructed model we can represent a wide range of policy responses. The empirical application used in this paper is a production model of California's irrigated crop sector that was constructed to measure the economic effect of environmental policy changes to irrigation water supplies, as part of a joint State and Federal program termed CalFed. We demonstrate that the disaggregate regional models give greater predictive precision, when compared with the model reconstructed on the aggregate data, and that they show a significant variation in the calculated regional elasticities of input demand and output response. From this, we conclude that any gains from aggregation - namely the reduction of small sample bias of the parameter estimates - would be swamped by the distortion of production response to policy changes, given the heterogeneity of the regions and the resultant bias.Production Economics,

Resource dedication problem in a multi-project environment

There can be different approaches to the management of resources within the context of multi-project scheduling problems. In general, approaches to multiproject scheduling problems consider the resources as a pool shared by all projects. On the other hand, when projects are distributed geographically or sharing resources between projects is not preferred, then this resource sharing policy may not be feasible. In such cases, the resources must be dedicated to individual projects throughout the project durations. This multi-project problem environment is defined here as the resource dedication problem (RDP). RDP is defined as the optimal dedication of resource capacities to different projects within the overall limits of the resources and with the objective of minimizing a predetermined objective function. The projects involved are multi-mode resource constrained project scheduling problems with finish to start zero time lag and non-preemptive activities and limited renewable and nonrenewable resources. Here, the characterization of RDP, its mathematical formulation and two different solution methodologies are presented. The first solution approach is a genetic algorithm employing a new improvement move called combinatorial auction for RDP, which is based on preferences of projects for resources. Two different methods for calculating the projects’ preferences based on linear and Lagrangian relaxation are proposed. The second solution approach is a Lagrangian relaxation based heuristic employing subgradient optimization. Numerical studies demonstrate that the proposed approaches are powerful methods for solving this problem