822 research outputs found
What if Critical Race Theory Were Just a Legal Theory? A Christian Critique
The national debate over Critical Race Theory (CRT) continues to grow and deepen. Some Christians seemingly find CRT legitimate, useful, and nonthreatening to Christian theological commitments. This view is incorrect. CRT is in fundamental conflict with Christianity due to its misguided perspectives on law, morality, truth, and justice. Although CRT is more than “just a legal theory,” this article examines CRT’s legal origins and outlook, showing the inevitable tension between its claims and a Christian understanding of reality. This article also calls attention to several policy proposals suggested by CRT scholars to demonstrate how they are incompatible with Christian views of divine moral law and procedural justice
Beyond Conjugacy for Chain Event Graph Model Selection
Chain event graphs are a family of probabilistic graphical models that
generalise Bayesian networks and have been successfully applied to a wide range
of domains. Unlike Bayesian networks, these models can encode context-specific
conditional independencies as well as asymmetric developments within the
evolution of a process. More recently, new model classes belonging to the chain
event graph family have been developed for modelling time-to-event data to
study the temporal dynamics of a process. However, existing model selection
algorithms for chain event graphs and its variants rely on all parameters
having conjugate priors. This is unrealistic for many real-world applications.
In this paper, we propose a mixture modelling approach to model selection in
chain event graphs that does not rely on conjugacy. Moreover, we also show that
this methodology is more amenable to being robustly scaled than the existing
model selection algorithms used for this family. We demonstrate our techniques
on simulated datasets
Beyond Conjugacy for Chain Event Graph Model Selection
Chain event graphs are a family of probabilistic graphical models that generalise Bayesian networks and have been successfully applied to a wide range of domains. Unlike Bayesian networks, these models can encode context-specific conditional independencies as well as asymmetric developments within the evolution of a process. More recently, new model classes belonging to the chain event graph family have been developed for modelling time-to-event data to study the temporal dynamics of a process. However, existing Bayesian model selection algorithms for chain event graphs and its variants rely on all parameters having conjugate priors. This is unrealistic for many real-world applications. In this paper, we propose a mixture modelling approach to model selection in chain event graphs that does not rely on conjugacy. Moreover, we show that this methodology is more amenable to being robustly scaled than the existing model selection algorithms used for this family. We demonstrate our techniques on simulated datasets
Quantum walks on two-dimensional grids with multiple marked locations
The running time of a quantum walk search algorithm depends on both the
structure of the search space (graph) and the configuration of marked
locations. While the first dependence have been studied in a number of papers,
the second dependence remains mostly unstudied.
We study search by quantum walks on two-dimensional grid using the algorithm
of Ambainis, Kempe and Rivosh [AKR05]. The original paper analyses one and two
marked location cases only. We move beyond two marked locations and study the
behaviour of the algorithm for an arbitrary configuration of marked locations.
In this paper we prove two results showing the importance of how the marked
locations are arranged. First, we present two placements of marked
locations for which the number of steps of the algorithm differs by
factor. Second, we present two configurations of and
marked locations having the same number of steps and probability to
find a marked location
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