Water withdrawals around the world have increased almost twice as fast as the population during the last century. Higher than expected water demand is leading to water scarcity and causing rapid depletion of water tables around the world. One reason behind the higher than expected demand is the inefficient use of water. Inefficient use of water affects the well-being of society, the economic stability of countries, and environmental health. Indeed, water use efficiency (WUE) is one of the pillars of sustainable development goals (SDG 6.4.1). However, progress towards achieving WUE is slow, especially for many developing countries where the degradation of natural resources is critical, economic growth is slow, and there are few strong institutions to coordinate actions. One reason behind inefficient water use is human behaviour. A variety of contextual and psychological factors underlie the behaviour. The contextual factors include socioeconomic, technical, institutional and environment
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Summary:
For decision makers grappling with data, Bayesian Networks are an overlooked asset. Affordable? Yes. Performance and applicability to edge devices? Yes again. Here s a practical guide to how Bayes Nets can solve enterprise problems.
In part one of this series, we covered some basic probability theory principles - and compared Machine Learning approaches to Bayesian Belief Nets (Can Bayesian Networks provide answers when Machine Learning comes up short?). In this article, we ll dig a little deeper into Bayesian Belief Networks and how they can be applied to complex decisions.
Understanding Bayesian Inference
In my practice, I find most people involved with advanced analytics, such as predictive, data science, and ML, are familiar with the name Bayes, and can even reproduce the simple theorem below. Still, very few have any experience implementing Judea Pearl s Bayesian Belief Networks: