Estimating Scale-Invariant Future in Continuous Time
نویسندگان
چکیده
منابع مشابه
Estimating scale-invariant future in continuous time
Natural learners must compute an estimate of future outcomes that follow from a stimulus in continuous time. Critically, the learner cannot in general know a priori the relevant time scale over which meaningful relationships will be observed. Widely used reinforcement learning algorithms discretize continuous time and use the Bellman equation to estimate exponentially-discounted future reward. ...
متن کاملEstimating Continuous-Time Income Models
While earning processes are commonly unobservable income flows which evolve in continuous time, observable income data are usually discrete, having been aggregated over time. We consider continuous-time earning processes, specifically (non-linearly) transformed Ornstein-Uhlenbeck processes, and the associated integrated, i.e. time aggregated process. Both processes are characterised, and we sho...
متن کاملEstimating joinpoints in continuous time scale for multiple change-point models
Joinpoint models have been applied to the cancer incidence and mortality data with continuous change points. The current estimation method [Lerman, P.M., 1980. Fitting segmented regression models by grid search. Appl. Statist. 29, 77–84] assumes that the joinpoints only occur at discrete grid points. However, it is more realistic that the joinpoints take any value within the observed data range...
متن کاملA Scale-Invariant Internal Representation of Time
We propose a principled way to construct an internal representation of the temporal stimulus history leading up to the present moment. A set of leaky integrators performs a Laplace transform on the stimulus function, and a linear operator approximates the inversion of the Laplace transform. The result is a representation of stimulus history that retains information about the temporal sequence o...
متن کاملDiscrete Time Scale Invariant Markov Processes
In this paper we consider a discrete scale invariant Markov process {X(t), t ∈ R} with scale l > 1. We consider to have some fix number of observations in every scale, say T , and to get our samples at discrete points α, k ∈ W, where α is obtained by the equality l = α and W = {0, 1, . . .}. So we provide a discrete time scale invariant Markov (DT-SIM) process X(·) with parameter space {α, k ∈ ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Neural Computation
سال: 2019
ISSN: 0899-7667,1530-888X
DOI: 10.1162/neco_a_01171