Home / AIO Classes / Network / Markov Chain

Markov Chain

Network AIO:MarkovChain NOT_REVIEWED

A network that is a stochastic model describing a sequence of possible events where the probability of each event depends only on the previous event's state.

Ontology IRI: https://w3id.org/aio/MarkovChain

Ontology Structure & Relationships

Parent Classes

AIO:Network (Network)

Branches

Network

Axiomatic Relationships

BFO:0000051 → AIO:ProbabilisticHiddenLayer (Probabilistic Hidden Layer)

Annotations & Sources

Synonyms:

MC exact
MP exact
Markov Process exact

Definition Sources:

https://en.wikipedia.org/wiki/Markov_chain

Subsets:

https://w3id.org/aio/ModelSubset

Curated AIMech Concepts Grounded to this Class

70 Concepts

Curated Concept	Kind	Mapping Relation	Rationale
Adaptive Metropolis `AIMech:AdaptiveMetropolis`	Algorithms	related_match	This procedure adapts a family of Markov transition kernels that are target-invariant at frozen settings. Its sampled-state history is not assumed to be a homogeneous Markov chain, and adaptive validity requires separate assumptions. AIO MarkovChain is a process/model beneath Network; this is a related method-to-process mapping, not equivalence, an is-a assertion or a convergence guarantee.
Adaptive parallel tempering `AIMech:AdaptiveParallelTempering`	Algorithms	related_match	This procedure adapts a family of Markov transition kernels that are target-invariant at frozen settings. Its sampled-state history is not assumed to be a homogeneous Markov chain, and adaptive validity requires separate assumptions. AIO MarkovChain is a process/model beneath Network; this is a related method-to-process mapping, not equivalence, an is-a assertion or a convergence guarantee. The frozen joint population target depends on the ladder; the cold marginal remains pi. This does not assert one invariant joint law throughout adaptation.
Adaptive scaling Metropolis `AIMech:AdaptiveScalingMetropolis`	Algorithms	related_match	This procedure adapts a family of Markov transition kernels that are target-invariant at frozen settings. Its sampled-state history is not assumed to be a homogeneous Markov chain, and adaptive validity requires separate assumptions. AIO MarkovChain is a process/model beneath Network; this is a related method-to-process mapping, not equivalence, an is-a assertion or a convergence guarantee.
Adaptive scaling within adaptive Metropolis `AIMech:AdaptiveScalingWithinAdaptiveMetropolis`	Algorithms	related_match	This procedure adapts a family of Markov transition kernels that are target-invariant at frozen settings. Its sampled-state history is not assumed to be a homogeneous Markov chain, and adaptive validity requires separate assumptions. AIO MarkovChain is a process/model beneath Network; this is a related method-to-process mapping, not equivalence, an is-a assertion or a convergence guarantee.
Annealed importance sampling `AIMech:AnnealedImportanceSampling`	Algorithms	related_match	AIS uses Markov kernels preserving successive auxiliary distributions to construct weighted paths. The local estimation algorithm is related to the target process/model; it is not asserted to be a neural network, hidden layer or equivalent Markov-chain class.
Barker acceptance rule `AIMech:BarkerAcceptanceRule`	Mathematical Methods	related_match	The acceptance rule constructs target-invariant Markov transitions when paired with a proposal and rejection self-transition. AIO MarkovChain describes the stochastic process/model, not this scalar acceptance function. This is a rule-to-process related match, not equivalence or an is-a assertion.
Boltzmann chain `AIMech:BoltzmannChain`	Model Families	related_match	The strictly linear hidden-chain structure has a qualified relation to HMMs and their latent Markov transitions. Coupled and looped energy graphs, plus terminal normalization conditions, preclude treating the whole family or its observations as the pinned first-order process.
Circular weight-shift sampler `AIMech:CircularWeightShiftSampler`	Algorithms	related_match	This procedure constructs target-preserving finite Markov transitions. AIO MarkovChain denotes a stochastic process/model; this method-to-process association does not assert equivalence, subclass membership or inheritance of Network and ProbabilisticHiddenLayer assertions.
Conditional Distribution Prediction for IDA* `AIMech:ConditionalDistributionPredictionIDAStar`	Algorithms	related_match	The one-step conditional child-heuristic table acts as a transition law on heuristic contexts; richer parent-history contexts enlarge that state description. This is a curator-inferred component relationship. The complete estimator multiplies by offspring counts and applies threshold pruning, so it is not equivalent to a probability-preserving Markov chain or an asserted subclass of the pinned network class. The mapping does not certify that real search states are Markov under a chosen compressed context.
Conjugate-gradient Monte Carlo `AIMech:ConjugateGradientMonteCarlo`	Algorithms	related_match	The procedure defines target-invariant Markov transitions under its stated assumptions, including a population state for CGMC. AIO MarkovChain describes a stochastic process/model and is exported beneath Network. This method-to-process related match is not equivalence, an is-a assertion or a claim of independent samples or a trained network.
Context-sensitive emission hidden Markov model `AIMech:ContextSensitiveEmissionHiddenMarkovModel`	Model Families	related_match	The first-order hidden prior is a Markov chain. The whole latent-observation model and its hidden posterior are not identified with the pinned first-order process; this is a component-structure relation.
Continuous-time Bayesian network `AIMech:ContinuousTimeBayesianNetwork`	Model Families	related_match	The complete finite-state model defines a continuous-time Markov process, and the pinned target includes Markov Process as an exact synonym. This related mapping does not equate the specific CIM factorization with the broad AIO node or assert inheritance of its Network classification or ProbabilisticHiddenLayer has-part relation. The separate initial BN does not make the possibly cyclic dynamics graph an AIO BayesianNetwork DAG.
Contrastive divergence `AIMech:ContrastiveDivergence`	Algorithms	related_match	Markov transitions supply the negative-phase configurations used by the parameter-update procedure. The AIO target denotes the stochastic process/model, not the learning algorithm or a guarantee of equilibrium sampling during adaptation.
Crank–Nicolson Langevin sampling `AIMech:CrankNicolsonLangevin`	Algorithms	related_match	This algorithm constructs target-invariant Markov transitions under its stated assumptions. The exact AIO CLASS denotes a stochastic process/model and is exported beneath Network. This is a method-to-process related match, not equivalence, subclass membership, independence of retained draws or a claim that the algorithm is a trained network.
Crank–Nicolson Metropolis sampling `AIMech:CrankNicolsonMetropolis`	Algorithms	related_match	This algorithm constructs target-invariant Markov transitions under its stated assumptions. The exact AIO CLASS denotes a stochastic process/model and is exported beneath Network. This is a method-to-process related match, not equivalence, subclass membership, independence of retained draws or a claim that the algorithm is a trained network.
Delayed-rejection adaptive Metropolis `AIMech:DelayedRejectionAdaptiveMetropolis`	Algorithms	related_match	This procedure adapts a family of Markov transition kernels that are target-invariant at frozen settings. Its sampled-state history is not assumed to be a homogeneous Markov chain, and adaptive validity requires separate assumptions. AIO MarkovChain is a process/model beneath Network; this is a related method-to-process mapping, not equivalence, an is-a assertion or a convergence guarantee.
Delayed-rejection Metropolis `AIMech:DelayedRejectionMetropolis`	Algorithms	related_match	The procedure defines target-invariant Markov transitions under its stated assumptions. AIO MarkovChain describes a stochastic process/model and is exported beneath Network. This method-to-process related match is not equivalence, an is-a assertion or a claim of independent samples or a trained network.
Diaconis–Holmes–Neal fiber sampler `AIMech:DiaconisHolmesNealFiberSampler`	Algorithms	related_match	The fiber procedure constructs a finite Markov chain on state-and-direction pairs with a specified target marginal. AIO MarkovChain describes a stochastic process/model; this is a method-to-process association without equivalence, subclass status or inheritance of Network and ProbabilisticHiddenLayer assertions.
Doubling slice sampling `AIMech:DoublingSliceSampling`	Algorithms	related_match	The procedure constructs Markov transitions for sampling a specified target, with the invariance and convergence qualifications stated in this record. AIO MarkovChain denotes the stochastic process/model and is exported beneath Network. This is a method-to-process related match, not equivalence, subclass membership or a claim that the algorithm is a trained network.
Dual-averaging step-size adaptation for Hamiltonian samplers `AIMech:DualAveragingHamiltonianStepSize`	Algorithms	related_match	The procedure tunes the integration step size used by a Markov-chain sampler during warmup, after which the fixed sampling transition is used. AIO MarkovChain describes the stochastic process/model, not its tuning procedure. The relation does not assert that a history-adaptive warmup sequence itself is a homogeneous Markov chain on positions alone.
Elliptical slice sampling `AIMech:EllipticalSliceSampling`	Algorithms	related_match	This algorithm constructs target-invariant Markov transitions under its stated assumptions. The exact AIO CLASS denotes a stochastic process/model and is exported beneath Network. This is a method-to-process related match, not equivalence, subclass membership, independence of retained draws or a claim that the algorithm is a trained network.
Epsilon-truncation for Conditional Distribution Prediction `AIMech:EpsilonTruncationCDP`	Algorithms	related_match	The method truncates and renormalizes the conditional transition law on CDP type contexts, potentially using a different law at each prediction level. This is a curator-inferred component relationship. Branching-count propagation and threshold pruning are not probability-preserving chain evolution, and compressed contexts need not make the actual search process Markov. The procedure is not asserted equivalent to or a subclass of the pinned network class.
Factorial hidden Markov model `AIMech:FactorialHiddenMarkovModel`	Model Families	related_match	The model contains multiple latent Markov chains; shared emissions and marginalization do not make its observations a first-order Markov chain. This is a component association.
Fast persistent contrastive divergence `AIMech:FastPersistentContrastiveDivergence`	Algorithms	related_match	Markov transitions supply the negative-phase configurations used by the parameter-update procedure. The AIO target denotes the stochastic process/model, not the learning algorithm or a guarantee of equilibrium sampling during adaptation.
Flattened slice sampling `AIMech:FlattenedSliceSampling`	Algorithms	related_match	The algorithm specifies a conditional Markov transition with a preserved target distribution. AIO MarkovChain describes a stochastic process/model; this is a method-to-process association without equivalence, subclass membership or inheritance of its exported Network and ProbabilisticHiddenLayer assertions.
Gibbs sampling `AIMech:GibbsSampling`	Algorithms	related_match	The procedure constructs Markov transitions for sampling a specified target, with the invariance and convergence qualifications stated in this record. AIO MarkovChain denotes the stochastic process/model and is exported beneath Network. This is a method-to-process related match, not equivalence, subclass membership or a claim that the algorithm is a trained network.
Gustafson guided-walk Metropolis sampler `AIMech:GustafsonGuidedWalkMetropolis`	Algorithms	related_match	The guided-walk procedure constructs a Markov chain on position and binary direction with a specified target marginal. AIO MarkovChain denotes a stochastic process/model. This is a method-to-process association, without equivalence, subclass status or inheritance of Network and hidden-layer assertions.
Hamiltonian Monte Carlo `AIMech:HamiltonianMonteCarlo`	Algorithms	related_match	This procedure constructs transitions of a Markov chain to sample a specified target distribution. The exact AIO CLASS describes a stochastic process/model with dependence on the preceding state and is exported beneath Network. The method-to-process relationship is related_match, not equivalence, subclass membership or a claim that the sampler is a trained network.
Hidden Markov model with auxiliary memory `AIMech:HiddenMarkovModelWithAuxiliaryMemory`	Model Families	related_match	The model extends Markov hidden-state sequencing with memory-dependent emissions and transitions. A Markov description requires the effective state to include auxiliary memory; the finite control state or observed string alone is not the pinned first-order process.
Hidden semi-Markov model `AIMech:HiddenSemiMarkovModel`	Model Families	related_match	The embedded state-entry process and the state-plus-residual-duration process are Markov representations. The hidden state label at each observation time generally is not first-order Markov, so the mapping does not assert that marginal process as a Markov chain.
Independence Metropolis–Hastings sampling `AIMech:IndependenceMetropolisHastings`	Algorithms	related_match	This procedure constructs transitions of a Markov chain to sample a specified target distribution. The exact AIO CLASS describes a stochastic process/model with dependence on the preceding state and is exported beneath Network. The method-to-process relationship is related_match, not equivalence, subclass membership or a claim that the sampler is a trained network.
Liu modified discrete-state Gibbs sampler `AIMech:LiuModifiedGibbsSampler`	Algorithms	related_match	This procedure constructs conditional Markov transitions for sampling a specified target. AIO MarkovChain denotes a stochastic process/model and is exported beneath Network. The relation is method-to-process association, not equivalence, subclass membership or inheritance of network and hidden-layer assertions.
Lookup Conditional Distribution Prediction `AIMech:LookupConditionalDistributionPrediction`	Algorithms	related_match	The reused CDP recurrence contains conditional transition laws on type contexts. This is a curator-inferred component relationship, not an assertion that count propagation with branching and pruning is a probability-preserving Markov chain. Lookup reuse neither guarantees Markov sufficiency of compressed search types nor changes the abstraction level of the pinned network class.
Markov Chain Monte Carlo Stochastic Approximation Expectation Maximization `AIMech:MarkovChainMonteCarloStochasticApproximationExpectationMaximization`	Algorithms	related_match	A conditional-target Markov transition is an explicit component of this fitting algorithm. The AIO node denotes a stochastic process/model beneath Network; the algorithm-to-process connection is relatedness, not equivalence or an is-a assertion. A changing parameter and finite run do not make the retained latent state an independent equilibrium draw.
Metropolis-adjusted Langevin algorithm `AIMech:MetropolisAdjustedLangevinAlgorithm`	Algorithms	related_match	The procedure constructs Markov transitions for sampling a specified target, with the invariance and convergence qualifications stated in this record. AIO MarkovChain denotes the stochastic process/model and is exported beneath Network. This is a method-to-process related match, not equivalence, subclass membership or a claim that the algorithm is a trained network.
Metropolis–Hastings sampling `AIMech:MetropolisHastings`	Algorithms	related_match	This procedure constructs transitions of a Markov chain to sample a specified target distribution. The exact AIO CLASS describes a stochastic process/model with dependence on the preceding state and is exported beneath Network. The method-to-process relationship is related_match, not equivalence, subclass membership or a claim that the sampler is a trained network.
Metropolis-within-Gibbs sampling `AIMech:MetropolisWithinGibbs`	Algorithms	related_match	The procedure constructs Markov transitions for sampling a specified target, with the invariance and convergence qualifications stated in this record. AIO MarkovChain denotes the stochastic process/model and is exported beneath Network. This is a method-to-process related match, not equivalence, subclass membership or a claim that the algorithm is a trained network.
Mixed memory Markov model `AIMech:MixedMemoryMarkovModel`	Model Families	related_match	The multicomponent case has a first-order joint vector state, while the scalar case can use multiple predecessor lags and the hidden extension marginalizes states. The pinned first-order MarkovChain definition is related temporal structure, not an exact identity or an unqualified parent for every observed process.
Multiple-try Metropolis `AIMech:MultipleTryMetropolis`	Algorithms	related_match	The procedure defines target-invariant Markov transitions under its stated assumptions. AIO MarkovChain describes a stochastic process/model and is exported beneath Network. This method-to-process related match is not equivalence, an is-a assertion or a claim of independent samples or a trained network.
Multiple-try Metropolis within Gibbs `AIMech:MultipleTryMetropolisWithinGibbs`	Algorithms	related_match	The procedure defines target-invariant Markov transitions under its stated assumptions. AIO MarkovChain describes a stochastic process/model and is exported beneath Network. This method-to-process related match is not equivalence, an is-a assertion or a claim of independent samples or a trained network.
Neal backtracking-suppressed MCMC `AIMech:NealBacktrackingSuppressedMCMC`	Algorithms	related_match	The algorithm constructs a transition on ordered state pairs with a specified target marginal. The AIO target describes a stochastic process/model; this is a method-to-process association without equivalence, subclass status, or inheritance of the exported Network and ProbabilisticHiddenLayer assertions.
Nested antithetic modification `AIMech:NestedAntitheticModification`	Algorithms	related_match	The construction defines conditional Markov transitions preserving a target. AIO MarkovChain denotes a stochastic process/model, so this is a method-to-process association without equivalence, subclass status or inheritance of Network and ProbabilisticHiddenLayer assertions.
Noh–Rieger random-walk centrality `AIMech:NohRiegerRandomWalkCentrality`	Mathematical Methods	related_match	The score is constructed from stationary and return probabilities of the specified Markov walk. The AIO target denotes the stochastic process/model; the local scalar centrality is a property of that process, not an equivalent process or subclass network. The target’s Network parent and probabilistic-hidden-layer relationship are retained as ontology context, not asserted as properties of this score.
Nonreversible Metropolis uniform update `AIMech:NonreversibleMetropolisUniformUpdate`	Algorithms	related_match	The algorithm constructs a Markov transition on an augmented state with a specified target marginal. The AIO target describes a stochastic process/model; this is a method-to-process relation, without equivalence, subclass status, or inheritance of its Network and ProbabilisticHiddenLayer assertions.
No-U-Turn sampler (original slice formulation) `AIMech:OriginalNoUTurnSampler`	Algorithms	related_match	This procedure constructs transitions of a Markov chain to sample a specified target distribution. The exact AIO CLASS describes a stochastic process/model with dependence on the preceding state and is exported beneath Network. The method-to-process relationship is related_match, not equivalence, subclass membership or a claim that the sampler is a trained network.
Pairwise Markov chain `AIMech:PairwiseMarkovChain`	Model Families	broad_match	The defining stochastic state is the complete pair Z_t=(X_t,Y_t), whose next-state law depends only on Z_t. The AIO Markov-chain family is broader than this hidden–observed joint-state construction; the mapping says nothing equivalent about either marginal.
Papadimitriou 2-SAT random walk `AIMech:Papadimitriou2SATRandomWalk`	Algorithms	related_match	The primary paper explicitly develops a Markov-chain interpretation and uses a reflecting random-walk comparison for the 2-SAT theorem. This is an algorithm-to-stochastic-process association, not equivalence or an is-a mapping to the pinned Network hierarchy. A clause-selection policy based only on the current assignment gives a state-based chain; history-dependent choices require additional state and are not silently assumed memoryless.
Parallel tempering `AIMech:ParallelTempering`	Algorithms	related_match	At fixed temperatures and proposal settings, the complete replica population follows a Markov transition preserving a product of tempered targets. AIO MarkovChain denotes the stochastic process/model beneath Network. This is a related algorithm-to-process mapping, not equivalence, subclass membership or an assertion of independent cold-replica samples.
Persistent contrastive divergence `AIMech:PersistentContrastiveDivergence`	Algorithms	related_match	Markov transitions supply the negative-phase configurations used by the parameter-update procedure. The AIO target denotes the stochastic process/model, not the learning algorithm or a guarantee of equilibrium sampling during adaptation.
Preconditioned Crank–Nicolson sampling `AIMech:PreconditionedCrankNicolson`	Algorithms	related_match	This algorithm constructs target-invariant Markov transitions under its stated assumptions. The exact AIO CLASS denotes a stochastic process/model and is exported beneath Network. This is a method-to-process related match, not equivalence, subclass membership, independence of retained draws or a claim that the algorithm is a trained network.
Preconditioned Crank–Nicolson Langevin sampling `AIMech:PreconditionedCrankNicolsonLangevin`	Algorithms	related_match	This algorithm constructs target-invariant Markov transitions under its stated assumptions. The exact AIO CLASS denotes a stochastic process/model and is exported beneath Network. This is a method-to-process related match, not equivalence, subclass membership, independence of retained draws or a claim that the algorithm is a trained network.
Product of hidden Markov models `AIMech:ProductOfHiddenMarkovModels`	Model Families	related_match	Each expert contains a latent Markov chain, while the normalized product model includes multiple chains and observation factors. This is a component association, not an assertion that the observed sequence is a first-order Markov chain.
Random-walk Metropolis–Hastings sampling `AIMech:RandomWalkMetropolisHastings`	Algorithms	related_match	The procedure constructs Markov transitions for sampling a specified target, with the invariance and convergence qualifications stated in this record. AIO MarkovChain denotes the stochastic process/model and is exported beneath Network. This is a method-to-process related match, not equivalence, subclass membership or a claim that the algorithm is a trained network.
Rejection-driven independence Metropolis sampling `AIMech:RejectionDrivenIndependenceMetropolis`	Algorithms	related_match	This algorithm constructs target-invariant Markov transitions under its stated assumptions. The exact AIO CLASS denotes a stochastic process/model and is exported beneath Network. This is a method-to-process related match, not equivalence, subclass membership, independence of retained draws or a claim that the algorithm is a trained network.
Robust adaptive Metropolis `AIMech:RobustAdaptiveMetropolis`	Algorithms	related_match	RAM adapts a family of random-walk Metropolis transition kernels, each target-invariant at a fixed shape. Its history-dependent sampled-state sequence is not assumed to be a homogeneous Markov chain. AIO MarkovChain describes the underlying process/model concept beneath Network, so this is a related method-to-process mapping, not equivalence, a subclass assertion or a proof of adaptive convergence.
Simulated annealing `AIMech:SimulatedAnnealing`	Algorithms	related_match	Simulated annealing uses Metropolis transitions at a sequence of temperatures. The pinned AIO target denotes a stochastic process/model and is exported beneath Network; this algorithm-to-process relation is not equivalence or an is-a assertion. A fixed cooling schedule gives time-dependent transitions, and changing temperature does not preserve a single stationary Boltzmann target throughout the run.
Slice sampling `AIMech:SliceSampling`	Algorithms	related_match	The procedure constructs Markov transitions for sampling a specified target, with the invariance and convergence qualifications stated in this record. AIO MarkovChain denotes the stochastic process/model and is exported beneath Network. This is a method-to-process related match, not equivalence, subclass membership or a claim that the algorithm is a trained network.
Sparse pachinko allocation inference `AIMech:SparsePachinkoAllocationInference`	Algorithms	related_match	The procedure constructs full and pruned sampling transitions, with explicit limitations on target invariance. The pinned class denotes a process/model, not this fitting algorithm or a guarantee of exact posterior sampling.
Stepping-out slice sampling `AIMech:SteppingOutSliceSampling`	Algorithms	related_match	The procedure constructs Markov transitions for sampling a specified target, with the invariance and convergence qualifications stated in this record. AIO MarkovChain denotes the stochastic process/model and is exported beneath Network. This is a method-to-process related match, not equivalence, subclass membership or a claim that the algorithm is a trained network.
Structured variational inference for factorial HMMs `AIMech:StructuredVariationalInferenceForFactorialHMMs`	Algorithms	related_match	The approximation explicitly retains tractable Markov-chain path structure and uses forward–backward within each chain. The inference algorithm is not equivalent to the stochastic process it exploits.
Symmetric delayed rejection `AIMech:SymmetricDelayedRejection`	Algorithms	related_match	The procedure defines target-invariant Markov transitions under its stated assumptions. AIO MarkovChain describes a stochastic process/model and is exported beneath Network. This method-to-process related match is not equivalence, an is-a assertion or a claim of independent samples or a trained network.
Tempered transitions `AIMech:TemperedTransitions`	Algorithms	related_match	Tempered transitions constructs a Markov kernel preserving a specified target distribution by an up/down path and acceptance test. This procedure is related to the AIO process/model; it is not asserted to be equivalent to that class or to inherit its Network parent or hidden-layer relation.
Triplet Markov chain `AIMech:TripletMarkovChain`	Model Families	broad_match	The complete triplet T_t=(X_t,U_t,Y_t) is a first-order stochastic state by definition, making the AIO Markov-chain family broader. The mapping applies to this augmented joint process, not its hidden–observed projection or the target marginal.
Unadjusted Langevin algorithm `AIMech:UnadjustedLangevinAlgorithm`	Algorithms	related_match	ULA constructs a discrete Markov process intended to approximate sampling from a target. At fixed positive step size its invariant law generally differs from that target. The AIO CLASS denotes the stochastic process/model, so this is a method-to-process related match, not equivalence, subclass membership or an assertion of exact target stationarity.
Upward-downward nested antithetic mixture `AIMech:UpwardDownwardNestedAntitheticMixture`	Algorithms	related_match	The algorithm constructs target-preserving finite Markov transitions. AIO MarkovChain denotes the stochastic process/model; this method-to-process association does not assert equivalence, subclass membership or inheritance of Network and ProbabilisticHiddenLayer assertions.
Upward-downward shifted tower mixture `AIMech:UpwardDownwardShiftedTowerMixture`	Algorithms	related_match	The algorithm constructs target-preserving finite Markov transitions. AIO MarkovChain denotes a stochastic process/model; this association does not assert equivalence, subclass membership or inheritance of Network and ProbabilisticHiddenLayer assertions.
Zero-self downward nested antithetic modification `AIMech:ZeroSelfDownwardNestedAntitheticModification`	Algorithms	related_match	The procedure constructs reversible conditional Markov transitions with a specified target. AIO MarkovChain denotes a stochastic process/model. The relation is method-to-process association without equivalence, subclass membership or inheritance of Network and ProbabilisticHiddenLayer assertions.
Zero-self flattened slice sampling `AIMech:ZeroSelfFlattenedSliceSampling`	Algorithms	related_match	The algorithm defines a conditional Markov transition preserving a specified target. AIO MarkovChain denotes a stochastic process/model; this association does not assert equivalence, subclass membership or inheritance of the exported Network and ProbabilisticHiddenLayer assertions.
Zhou first-passage attractor community detection `AIMech:ZhouAttractorCommunityDetection`	Algorithms	related_match	First-passage attractors are derived from a specified Markov walk. The local grouping algorithm is distinct from the target process/model; the target’s Network parent and hidden-layer relation are not imported into this graph algorithm.
Zhou random-walk distance-profile dissimilarity `AIMech:ZhouRandomWalkDissimilarity`	Mathematical Methods	related_match	The compared profiles are mean passage times of a specified Markov walk. This relates the pairwise functional to its generating process, without asserting process equivalence or inheriting the target’s Network/hidden-layer context.

Import Provenance & Upstream Synchronization

Source Repo: AIO GitHub

Release Version: aio.json

Commit: 397851c547…

License: CC-BY-4.0

Imported At: 2026-10-10T07:34:10Z

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Source File: data/aio/MarkovChain.yaml