Dark matter could be composed by black-hole remnants formed before the big-bang era in a bouncing cosmology. This hypothetical scenario has major implications on the issue of the arrow of time: it would upset a common attribution of past low entropy to the state of the geometry, and provide a concrete realisation to the perspectival interpretation ...
Dark matter could be composed by black-hole remnants formed before the big-bang era in a bouncing cosmology. This hypothetical scenario has major implications on the issue of the arrow of time: it would upset a common attribution of past low entropy to the state of the geometry, and provide a concrete realisation to the perspectival interpretation ...
Dark matter could be composed by black-hole remnants formed before the big-bang era in a bouncing cosmology. This hypothetical scenario has major implications on the issue of the arrow of time: it would upset a common attribution of past low entropy to the state of the geometry, and provide a concrete realisation to the perspectival interpretation ...
Dark matter could be composed by black-hole remnants formed before the big-bang era in a bouncing cosmology. This hypothetical scenario has major implications on the issue of the arrow of time: it would upset a common attribution of past low entropy to the state of the geometry, and provide a concrete realisation to the perspectival interpretation ...
We propose a method for transforming probability distributions so that parameters of interest are forced into a specified distribution. We prove that this approach is the maximum entropy choice, and provide a motivating example applicable to neutrino hierarchy inference.
We propose a method for transforming probability distributions so that parameters of interest are forced into a specified distribution. We prove that this approach is the maximum entropy choice, and provide a motivating example applicable to neutrino hierarchy inference.
We propose a method for transforming probability distributions so that parameters of interest are forced into a specified distribution. We prove that this approach is the maximum entropy choice, and provide a motivating example applicable to neutrino hierarchy inference.
We propose a method for transforming probability distributions so that parameters of interest are forced into a specified distribution. We prove that this approach is the maximum entropy choice, and provide a motivating example applicable to neutrino hierarchy inference.
We propose a method for transforming probability distributions so that parameters of interest are forced into a specified distribution. We prove that this approach is the maximum entropy choice, and provide a motivating example applicable to neutrino hierarchy inference.
The Medical Research Council funded my PhD studies.
