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Markov process / chain
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Ross indexed the following pages under the keyword: "Markov process / chain".


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1950
Summary: A list of actions in which some object has to be avoided.
Summary: Some items of information theory.
Markov process / chain
Stochastic processes
Transition probability
2815 2816
Group (mathematical) and pattern
Pattern (in general) as group-structure
Summary: Example of pattern and group.
Markov process / chain predicting words and letters
2823 2824
Summary: Redundancy and information.
Markov process / chain examples
Memory sentence with short memory
Redundancy organisms prefer highly redundant information
Sentence with limited memory
The Multistable System [15]: Human beings like information to be highly redundant: does this link with replication in multistable systems? 2993.
2993 2994

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1951
Markov process / chain equilibrium in ensemble
Transition probability eqilibrium of ensemble
3083 3084
Summary: Switches that see a Markoff process only through themselves: consequent bias in their settings. (Theory in metric-less states, 4527)
Markov process / chain seen through a gate or switch
Stochastic processes seen through a gate or switch
Information in machines
3163 3164
Summary: Information in a conjoined system. 3274
Information in machines
Information organisms aim to destroy it
Information in machines
Markov process / chain passing through transducer
3177 3178
Information in machines
Information in machines
Markov process / chain affecting a machine
3195 3196
Information in machines
Markov process / chain affecting a machine
Information in machines
Markov process / chain affecting a machine
3197 3198
Information in machines
Markov process / chain affecting a machine
Summary: Shannon and I.
Chasing equation of
Coding in machine
Constant intrinsic stability definition
Equilibrium 'constant intrinsic'
Information in machines
Markov process / chain affecting a machine
Output as function of input
Transformation function-forming
3199 3200
Summary: A variable of constant intrinsic stability and one that always moves towards some function of its neighbours' states are identical. (Cf. 3110) (Behaviour 3134, 3239)
Information in machines
Markov process / chain affecting a machine
Information in a disturbed system
Information in machines
Markov process / chain affecting a machine
Parameter as source of information
3201 3202
Summary: Passing information from parameter into machine. The previous theorem can be improved. Here is a better statement...
Information in machines
Markov process / chain affecting a machine
Information in machines
Markov process / chain affecting a machine
3203 3204
Summary: Accurate statement of the amount of information that can be put into a machine by arbitrary interference. (3275)
Information in machines
Markov process / chain affecting a machine
Summary: A physical example of habituation.
Habituation physical example
Information in machines
Markov process / chain affecting a machine
3205 3206
Summary: In the field of an absolute system, every convergent junction acts as a sink for information.
Information in machines
Information lost by convergence in field
Markov process / chain affecting a machine
Information in machines
Markov process / chain affecting a machine
3207 3208
Summary: Maximal loss at a convergent point in a field. Table of log2[(aa bb)/(a+b)a+b].
Information in machines
Markov process / chain affecting a machine
Summary: We cannot measure information by finding contributions from sub-ensembles and adding. (Another example 3249)
Information belongs to the whole system
Information in machines
Markov process / chain affecting a machine
3209 3210
Information in machines
Markov process / chain affecting a machine
Summary: An absolute machine can never gain more information than is put into it.
Information in machines
Markov process / chain affecting a machine
3211 3212
Information in machines
Markov process / chain affecting a machine
Summary: When a parameter affects a machine, the gain in information is stationary (and a maximum) if the parameter's values are distributed independently of the machine's.
Information in machines
Markov process / chain affecting a machine
3213 3214
Information in machines
Markov process / chain affecting a machine
Summary: Passage of information as machine dominates machine. (See 3298, 3218, 3275)
Information in machines
Markov process / chain affecting a machine
3215 3216
Capacity information
Channel capacity of absolute systems
Information in machines
Markov process / chain affecting a machine
Transmission capacity of absolute systems
Information in machines
Markov process / chain affecting a machine
3217 3218
Information in machines
Markov process / chain affecting a machine
Summary: (Stated at the front - on 3218): If a machine is driven by an absolute system, the duration of coupling makes no difference to the amount of information received.
Information in machines
Markov process / chain affecting a machine
3219 3220
Information in machines
Markov process / chain affecting a machine
Summary: An information source controlling an otherwise absolute system raises it to a definite information content at which it is in stable equilibrium. (3086) (Canonical equations next page)
Information in machines
Markov process / chain affecting a machine
3221 3222
Information in machines
Markov process / chain affecting a machine
Markov process / chain equilibrium in ensemble
Parameter as source of information
Resting state of system with Markoff parameter
Summary: Canonical equations of the densities in state of a system disturbed by an information source. (See 3227)
Information in machines
Markov process / chain affecting a machine
3223 3224
Information in machines
Information of transition
Markov process / chain affecting a machine
Summary: Another measure of information applicable to a machine.
Information in machines
Markov process / chain affecting a machine
3225 3226
Information in machines
Markov process / chain affecting a machine
Summary: When driven by a steady statistical source, the information in a machine does not tend to a minimum.
Information in machines
Markov process / chain affecting a machine
3227 3228
Information in machines
Markov process / chain affecting a machine
Statistical mechanics states that cannot be escaped from
Summary: States that lock accumulate all the members of the ensemble. 3233, 3291, 4524
Information in machines
Markov process / chain affecting a machine
3229 3230
Information in machines
Markov process / chain affecting a machine
Information in machines
Markov process / chain affecting a machine
Transition probability between resting states
3231 3232
Information in machines
Markov process / chain affecting a machine
Summary: Information when a stochastic parameter changes infrequently.
Information in machines
Information ways of loosing
Markov process / chain affecting a machine
3233 3234
Markov process / chain information from
Parameter as source of information
Resting state and information
Transition probability and information
3275 3276
Entropy calculation of
Information calculation of
Markov process / chain information from
Transducer theory of
3368 3369

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1953
Summary: Adaptation needs a transducer between the prime (statistical) mover and the useful result. 4583
Summary: Markov processes in machines.
Markov process / chain in machine
4538 4539
Markov process / chain in chain of machines
Markov process / chain in machine
Markov process / chain in machine
4540 4541
Markov process / chain in machine
Markov process / chain in machine
4542 4543
Markov process / chain in machine
Summary: Markoff process in machinery. 4671
Summary: The human brain may not be optimal for inclusion in computer.
Markov process / chain in machine
Society [52]: The human brain may not be an optimal component 4545.
4544 4545
Homeostasis in homeostat
Homeostat two regulators in
Levels in homeostat
Regulation in homeostat
Summary: All regulations are included in the Markov chain's progression to an absorbing state. [deleted] 4676, 4678, 4683, 4695, 4701
Markov process / chain in machine
4660 4661
Markov process / chain dominating Markov process
Transition probability with Markov parameter
Summary: Markovian parameters to Markov chain. 4700
Summary: Meaning of "output".
Output meaning of
4672 4673
Summary: In information, the point of view of the element is very different from that of the set.
Searching variety in
Homeostat its discontinuous 2nd order process can be changed through intermediates to that of thermostat
Markov process / chain can be modified to give a field
Regulation discontinuous and continuous are related
Trial and error relation to continuous stability
4694 4695
Homeostat and Markov process
Joining two Markov chains
Markov process / chain building 'machine of'
Summary: Markov chains as component parts for building a machine. 4703, 4770
4700 4701
Summary: More homomorphisms quotient machines (4778) 4777
Markov process / chain not provided by determinate machines
Variety in Braille
4730 4731

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1954
Dynamic system stochastic
Machine stochastic machine
Stochastic processes in machine
Input defined
Markov process / chain the Markov machine
Output defined
Transition probability in Markov machine
4768 4769
Summary: Equilibrium of Markov chain.
Markov process / chain finding equilibrium density
Dynamic system compatible with equivalence relation
Lattice Riguer on
Machine compatible with equivalence relation
4776 4777
Hunt and stick in machine
Markov process / chain producing one state
Summary: Hunt-and-stick is a more general form of stability. 4881, 5046
Equilibrium "hunt and stick" as
Hunt and stick as stability
Regulation fundamental process
4842 4843
Summary: On identifying an optimum in a set.
Absorbing state, in Markov chain coupled
Markov process / chain ultrastability in
Ultrastability with Markov systems
Veto in Markov machine
Summary: Equilibrium in coupled Markov systems.
Homeostat other forms of
Ultrastability with mixed systems
4904 4905
Aging (as process)
Experience law of
History aging processes
Machine Markovian, aging in
Markov process / chain aging in
Variety in Markov machines
Habituation in Markov machines
4916 4917
Summary: Discriminative feedback. 4963
Summary: Information repair.
Information "repair" (MacKay)
Information and discriminative feedback
Summary: Letters as Markov chain.
Adaptation strategy of
Markov process / chain English as
Maximal likelihood and adaptation
4946 4947

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1955
Summary: Problem solving.
Markov process / chain as problem solver
Summary: Another example of a self-locking system, this one harmful.
Chain on wheel, as self locking system
Self-locking system example of chain on wheel
5068 5069
Summary: Variety in mathematical forms. (Continued 5167.6)
Variety shown by family
Equivalence relation on Markov chain
Lattice of Markov simplifications
Markov process / chain equivalence relation on
5164 5165

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1957
Summary: "Confluent" defined (as noun). 5512
Machine Markovian
Markov process / chain in machine
Summary: The theory of the determinate machine includes the practical aspects of the theory of the Markovian machine.
Joining 'random'
Random coupling
5498 5499

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1960
Summary: No regulator (other things being equal) can give performance better than the machine with input.
Entropy zero entropy
Markov process / chain of zero entropy
6160 6161

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1961
Summary: Construction to get many compact confluents. 6347, 6362.9
Dispersion small confluents
Summary: Convergence to equilibrium in Markov chain.
Convergence (of lines of behaviour) Markov non-convergence
Equilibrium even distribution, Markov
Markov process / chain convergence (or bunching)
6344 6345

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1962
Summary: To show anticipation, the operations must be such as lead to a unique state; by what route, whether quickly or slowly, are irrelevant 6389
Markov process / chain uncertainty analysis
Uncertainty analysis of Markov chain
Summary: How a Markov chain shows in Uncertainty Analysis of the triples.
6374 6375

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1966
Summary: "I", in a dream, is not necessarily the site of intellectual activity.
Archer dream of
Dream and "self"
Self-awareness impossible
Markov process / chain all operators
Matrix all operators
Operator all as transition matrices
6592 6593

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1968
Summary: Optimal regulation and homomorphism.
Linearly dependent process defined
Machine Turing-type
Markov process / chain and linearly dependent process
Turing machine and machine with input
Transmission in real systems
6936 6937

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