In probability theory, a piecewise-deterministic Markov process (PDMP) is a process whose behaviour is governed by random jumps at points in time, but...

including Lévy processes, stochastic networks (Kelly's lemma), birth and death processes, Markov chains, and piecewise deterministic Markov processes. Time reversal...

hidden Markov model Maximum-entropy Markov model Variable-order Markov model Markov renewal process Markov chain mixing time Markov kernel Piecewise-deterministic...

statistics, econometrics, and signal processing, an autoregressive (AR) model is a representation of a type of random process; as such, it can be used to describe...

the posterior probability model. Jump process, an example of jump diffusion Piecewise-deterministic Markov process (PDMP), an example of jump diffusion...

observable Markov decision process (POMDP) is a generalization of a Markov decision process (MDP). A POMDP models an agent decision process in which it...

of statistics Pickands–Balkema–de Haan theorem Pie chart Piecewise-deterministic Markov process Pignistic probability Pinsker's inequality Pitman closeness...

property of the value process. In a 1984 paper he introduced the concept of Piecewise deterministic Markov process, a class of Markov models which have been...

balance in kinetics seem to be clear. A Markov process is called a reversible Markov process or reversible Markov chain if there exists a positive stationary...

statistics, diffusion processes are a class of continuous-time Markov process with almost surely continuous sample paths. Diffusion process is stochastic in...

values. The model is a particular type of piecewise deterministic Markov process and can also be viewed as a Markov reward model with boundary conditions...

mixing time Markov partition Markov process Continuous-time Markov process Piecewise-deterministic Markov process Martingale Doob martingale Optional...

such as piecewise constant, left continuous and adapted processes where the integral can be written explicitly. Such simple predictable processes are linear...

process (in the context of probability), an example of a (stochastic) hybrid system with zero flow component Piecewise-deterministic Markov process (PDMP)...

polynomial-time and robust envy-free. For deterministic mechanisms, the results are mostly negative, even when all agents have piecewise-constant valuations. Kurokawa...

interpolation, however, yield a piecewise continuous function composed of many polynomials to model the data set. Extrapolation is the process of estimating, beyond...

functions of the variables. A one-dimensional GRF is also called a Gaussian process. An important special case of a GRF is the Gaussian free field. With regard...

lower levels, and making more deterministic decisions only at the highest level, speech recognition by a machine is a process broken into several phases...

statistics, a continuous-time stochastic process, or a continuous-space-time stochastic process is a stochastic process for which the index variable takes a...

{\displaystyle \max(F_{T}-K,\;0)} under the probability distribution of the process F t {\displaystyle F_{t}} . Except for the special cases of β = 0 {\displaystyle...

2012-10-12. Ortner, R. (2010). "Online regret bounds for Markov decision processes with deterministic transitions". Theoretical Computer Science. 411 (29):...

ones. The value function v ( x , y ) {\displaystyle v(x,y)} is defined piecewise depending on whether y {\displaystyle y} is desirable ( λ D {\displaystyle...

Dynamic Time Warping (DTW) to Hidden Markov Model (HMM)" (PDF). Juang, B. H. (September 1984). "On the hidden Markov model and dynamic time warping for...

graphical representation of state and transition relations Markov chain: a stochastic process in which the future will be determined by the current state...

in different clusters (between-cluster sum of squares, BCSS). This deterministic relationship is also related to the law of total variance in probability...

equations driven by non-semimartingale paths, such as Gaussian processes and Markov processes. Rough paths are paths taking values in the truncated free tensor...

constraints Approaches to deal with uncertainty: Markov decision process Partially observable Markov decision process Robust optimization Wald's maximin model...

and the distributional form of the stochastic fluctuations around the deterministic number μ {\displaystyle \mu } during this convergence. More precisely...

himself was influenced by Hédi Soula. Galves and Löcherbach referred to the process that Cessac described as "a version in a finite dimension" of their own...