Übersetzung für 'Markov process' von Englisch nach Deutsch

Markov process

Markow-Prozess {m}electr.engin.stat.

Markovprozess {m} [auch: Markov-Prozess]math.

---

Werbung

Markov property

Markov-Eigenschaft {f}math.stat.

Markov chain

Markovkette {f} [auch: Markov-Kette]math.

Markov kernel

Markow-Kern {m}

Markov's inequality

Markow-Ungleichung {f}math.stat.

hidden Markov model <HMM>

Hidden-Markov-Modell {n} <HMM>electr.telecom.

Hidden-Markow-Modell {n} <HMM>electr.telecom.

Gauss-Markov theorem [also: theorem of Gauss and Markov]

Satz {m} von Gauß-Markowmath.

Markov chain Monte Carlo method <MCMC method>

Markov-Ketten-Monte-Carlo-Verfahren {n} <MKMC-Verfahren>math.stat.

process-oriented quality management <PQM, process-oriented QM>

prozessorientiertes Qualitätsmanagement {n} <PQM, prozessorientiertes QM>QM

lead-to-order process <LTO process>

Lead-to-Order-Prozess {m}comm.comp.QM

basic oxygen steelmaking process <BOS process>

Linz-Donawitz-Verfahren {n} <LD-Verfahren>ind.

continual improvement process <CIP, CI process>

kontinuierlicher Verbesserungs­prozess {m} <KVP>QM

quality management process <QM process>

Qualitätsmanagementprozess {m} <QM-Prozess>QM

proton capture process <p-process>

Protonenanlagerungs­prozess {m} <p-Prozess>phys.

process software <process SW>

Prozess-Software {f} <Prozess-SW>comp.econ.

Prozesssoftware {f} <Prozess-SW>comp.econ.

gas-to-liquid process <GtL, GTL process> [gas to liquids]

GtL-Verfahren {n} [Gas to Liquids]engin.tech.

Umklapp process <U-process, u-process>

Umklappprozess {m} <U-Prozess>phys.

hole-hole-electron process <h-h-e process>

Loch-Loch-Elektron-Prozess {m} <h-h-e-Prozess>phys.

electron-electron-hole process <e-e-h process>

Elektron-Elektron-Loch-Prozess {m} <e-e-h-Prozess>phys.

• He realized and demonstrated that the lengths of remissions in schizophrenia form a Markov process, i.e.
• is assumed to be an unobserved Markov process. The following picture presents a Bayesian network of a HMM.
• In other words, he showed that depending on the choice of the time scale for the corresponding approximations the same stochastic process can be regarded as both dynamical and Markovian, and in the general case as a non-Markov process.
• A Markov chain or Markov process is a stochastic model describing a sequence of possible events in which the probability of each event depends only on the state attained in the previous event.
• Rates are drawn from the exponential distribution and PEPA models are finite-state and so give rise to a stochastic process, specifically a continuous-time Markov process (CTMC).

• A strictly stationary Markov process is "β"-mixing if and only if it is an aperiodic recurrent Harris chain.
• In probability theory, this identifies the evolution as a continuous-time Markov process, with the integrated master equation obeying a Chapman–Kolmogorov equation.
• In general this gives a non-stationary Markov process, but each individual step will still be reversible, and the overall process will still have the desired stationary distribution (as long as the chain can access all states under the fixed ordering).
• Examples include a stochastic matrix, which describes a stochastic process known as a Markov process, and stochastic calculus, which involves differential equations and integrals based on stochastic processes such as the Wiener process, also called the Brownian motion process.
• (for example, that they are generated by a finite Markov process), if the set of hypotheses being considered is small enough, the minimizer of the empirical risk will closely approximate the minimizer of the expected risk as [...] grows large.

• In essence, the Poisson process is a continuous-time Markov process on the positive integers (usually starting at zero) which has independent exponentially distributed holding times at each integer [...] before advancing to the next integer, [...].
• To accomplish this, the algorithm uses a Markov process, which asymptotically reaches a unique stationary distribution [...] such that [...].
• Gauss–Markov stochastic processes (named after Carl Friedrich Gauss and Andrey Markov) are stochastic processes that satisfy the requirements for both Gaussian processes and Markov processes.
• The particle filter associated with the Markov process [...] given the partial observations [...] is defined in terms of particles evolving in [...] with a likelihood function given with some obvious abusive notation by [...].