JSTOR Daily: Stochastic Algorithms for Markov Models Estimation with Intermittent Missing Data
Multistate Markov models are frequently used to characterize disease processes, but their estimation from longitudinal data is often hampered by complex patterns of incompleteness. Two algorithms for ...
Risk: Estimating risks of European option books using neural stochastic differential equation market models
In this paper we examine the capacity of arbitrage-free neural stochastic differential equation market models to produce realistic scenarios for the joint dynamics of multiple European options on a ...
Estimating risks of European option books using neural stochastic differential equation market models
Stochastic Calculus for Finance I: Binomial asset pricing model and Stochastic Calculus for Finance II: tochastic Calculus for Finance II: Continuous-Time Models. These two books are very good if you want to apply the theory to price derivatives. Stochastic Differential Equations: An Introduction with Applications Bernt Oksanda.
Cellular dynamics are intrinsically noisy, so mechanistic models must incorporate stochasticity if they are to adequately model experimental observations. As well as intrinsic stochasticity in gene ...
Stochastic differential equations (SDEs) provide a powerful framework for modelling systems where randomness plays a crucial role. Estimation methods for SDEs seek to infer underlying parameters that ...
A stochastic process is a colection of random variables defined on the same probability space. Please explain further what parts of this definition are escaping you.
What's the difference between stochastic and random? There is an anecdote about the notion of stochastic processes. They say that when Khinchin wrote his seminal paper "Correlation theory for stationary stochastic processes", this did not go well with Soviet authorities. The reason is that the notion of random process used by Khinchin contradicted dialectical materialism. In diamat, all ...