Abstract Energy risk management requires an efficient prediction of CO2 future prices. We apply a neural network calibrated stochastic model to predict day ahead future prices suitable for risk management with a given time horizon (e.g. 10 business days). The model is training neural perceptron layers to learn the weights of historical time series points within a past horizon equal to the the m...

Gossip protocols are designed to operate in very large, decentralised networks. A node in such a network bases its decision to interact (gossip) with another node on its partial view of the global system. Because of the size of these networks, analysis of gossip protocols is mostly done using simulations, but even they tend to be expensive in computation time and memory consumption. We employ m...

We consider an Ito stochastic differential equation with delay, driven by brownian motion, whose solution, by an appropriate reformulation, defines a Markov process X with values in a space of continuous functions C, with generator L. We then consider a backward stochastic differential equation depending on X , with unknown processes (Y, Z), and we study properties of the resulting system, in p...

In this paper we consider a portfolio optimization problem of the Merton’s type with complete memory over a finite time horizon. The problem is formulated as a stochastic control problem on a finite time horizon and the state evolves according to a process governed by a stochastic process with memory. The goal is to choose investment and consumption controls such that the total expected discoun...

objective: children with attention deficit hyperactivity disorder (adhd) do not process most information due to inattention and loss of the opportunity to save and retrieve information. therefore, these children experience memory impairment. although visual memory has been previously studied in children with adhd, iconic memory in these children has been less evaluated. we aimed to study the po...

The objects studied in survival and event history analysis are stochastic phenomena developing over time. It is therefore natural to use the highly developed theory of stochastic processes. We argue that this theory should be used more in event history analysis. Some specific examples are treated: Markov chains, martingale-based counting processes, birth type processes, diffusion processes and ...

A method for large scale Gaussian process classification has been recently proposed based on expectation propagation (EP). Such a method allows Gaussian process classifiers to be trained on very large datasets that were out of the reach of previous deployments of EP and has been shown to be competitive with related techniques based on stochastic variational inference. Nevertheless, the memory r...

Aiming to augment generative models with external memory, we interpret the output of a memory module with stochastic addressing as a conditional mixture distribution, where a read operation corresponds to sampling a discrete memory address and retrieving the corresponding content from memory. This perspective allows us to apply variational inference to memory addressing, which enables effective...