Advanced search    

Search: authors:"Robert Gardner"

4 papers found.
Use AND, OR, NOT, +word, -word, "long phrase", (parentheses) to fine-tune your search.

Translation invariance and finite additivity in a probability measure on the natural numbers

IJMMS TRANSLATION INVARIANCE AND FINITE ADDITIVITY IN A PROBABILITY MEASURE ON THE NATURAL NUMBERS ROBERT GARDNER ROBERT PRICE Inspired by the ?two envelopes exchange paradox,? a finitely additive ... propose that, in the setting of the two envelopes problem, probabilities and expected values be computed as above. Robert Gardner: Department of Mathematics, Box 70663, East Tennessee State University

Translation invariance and finite additivity in a probability measure on the natural numbers

IJMMS TRANSLATION INVARIANCE AND FINITE ADDITIVITY IN A PROBABILITY MEASURE ON THE NATURAL NUMBERS ROBERT GARDNER ROBERT PRICE Inspired by the ?two envelopes exchange paradox,? a finitely additive ... propose that, in the setting of the two envelopes problem, probabilities and expected values be computed as above. Robert Gardner: Department of Mathematics, Box 70663, East Tennessee State University

Translation invariance and finite additivity in a probability measure on the natural numbers

IJMMS TRANSLATION INVARIANCE AND FINITE ADDITIVITY IN A PROBABILITY MEASURE ON THE NATURAL NUMBERS ROBERT GARDNER ROBERT PRICE Inspired by the “two envelopes exchange paradox,” a finitely additive ... propose that, in the setting of the two envelopes problem, probabilities and expected values be computed as above. Robert Gardner: Department of Mathematics, Box 70663, East Tennessee State University

Quantum generalisation of feedforward neural networks

We propose a quantum generalisation of a classical neural network. The classical neurons are firstly rendered reversible by adding ancillary bits. Then they are generalised to being quantum reversible, i.e., unitary (the classical networks we generalise are called feedforward, and have step-function activation functions). The quantum network can be trained efficiently using...