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In [[mathematics]], the '''Bernoulli scheme''' or '''Bernoulli shift''' is a generalization of the [[Bernoulli process]] to more than two possible outcomes.<ref>P. Shields, ''The theory of Bernoulli shifts'' , Univ. Chicago Press  (1973)</ref><ref>Michael S. Keane, "Ergodic theory and subshifts of finite type", (1991), appearing as Chapter 2 in ''Ergodic Theory, Symbolic Dynamics and Hyperbolic Spaces'', Tim Bedford, Michael Keane and Caroline Series, Eds. Oxford University Press, Oxford (1991). ISBN 0-19-853390-X</ref> Bernoulli schemes are important in the study of [[dynamical system]]s, as most such systems (such as [[Axiom A system]]s) exhibit a [[repellor]] that is the product of the [[Cantor set]] and a [[smooth manifold]], and the dynamics on the Cantor set are isomorphic to that of the Bernoulli shift.<ref>Pierre Gaspard, ''Chaos, scattering and statistical mechanics''(1998), Cambridge University press</ref> This is essentially the [[Markov partition]]. The term ''shift'' is in reference to the [[shift operator]], which may be used to study Bernoulli schemes. The [[Ornstein isomorphism theorem]]<ref>{{springer|author=D.S. Ornstein|title=Ornstein isomorphism theorem|id=Ornstein_isomorphism_theorem&oldid=17997}}</ref> shows that Bernoulli shifts are isomorphic when their [[Kolmogorov entropy|entropy]] is equal. Finite [[stationary stochastic process]]es are isomorphic to the Bernoulli shift; in this sense, Bernoulli shifts are [[universal property|universal]].
'''Service level''' measures the performance of a system. Certain goals are defined and the service level gives the percentage to which those goals should be achieved. [[Fill rate]] is different from service level.


Examples of service level:
==Definition==
* Percentage of calls answered in a [[call center]].
A Bernoulli scheme is a [[discrete-time]] [[stochastic process]] where each [[statistical independence|independent]] [[random variable]] may take on one of ''N'' distinct possible values, with the outcome ''i'' occurring with probability <math>p_i</math>, with ''i''&nbsp;=&nbsp;1,&nbsp;...,&nbsp;''N'', and
* Percentage of customers waiting less than a given fixed time.
* Percentage of customers that do not experience a [[stockout]].


==Service level==
:<math>\sum_{i=1}^N p_i = 1. </math>
Service level is used in [[supply chain management]] and in [[inventory management]] to measure the performance of inventory replenishment policies. Under consideration, from the optimal solution of such a model also the optimal size of backorders can be derived.


Unfortunately, this optimization approach requires that the planner know the optimal value of the backorder costs. As these costs are difficult to quantify in practice, the logistical performance of an inventory node in a supply network is measured with the help of technical [[performance metric|performance measures]]. The target values of these measures are set by the decision maker.
The [[sample space]] is usually denoted as


Several definitions of service levels are used in the literature as well as in practice. These may differ not only with respect to their scope and to the number of considered products but also with respect to the time interval they are related to. These [[performance metric|performance measure]]s are the ''[[Performance indicator|Key Performance Indicators]]'' (KPI) of an inventory node which must be regularly monitored. If the [[Control (management)|controlling]] of the performance of an inventory node is neglected, the decision maker will not be able to optimize the processes within a supply chain.
:<math>X=\{1,\ldots,N \}^\mathbb{Z}</math>


===α service level (type 1)===
as a short-hand for
The α service level is an '''event-oriented''' performance criterion. It measures the [[probability]] that
''all'' customer orders arriving within a given time interval will be completely delivered from stock on hand, i.e. without delay.


Two versions are discussed in the literature differing with respect to the time interval within which the customers arrive.
:<math>X=\{ x=(\ldots,x_{-1},x_0,x_1,\ldots) :
With reference to a ''demand period'', α denotes the probability that an arbitrarily arriving customer order will be completely served from stock on hand, i.e. without an inventory-related waiting time (period <math>\alpha_p</math> service level):
x_k \in \{1,\ldots,N\} \; \forall k \in \mathbb{Z} \}.</math>


<math>
The associated [[measure (mathematics)|measure]] is called the '''Bernoulli measure'''<ref>Achim Klenke, ''Probability Theory'' (2006) Springer-Verlag, ISBN 978-1-848000-047-6 doi:10.1007/978-1-848000-048-3</ref>
\alpha_p = Prob\{Period~demand
                      \le \; {Inventory~on~hand~at~the~beginning~of~a~period}\}


</math>.
:<math>\mu = \{p_1,\ldots,p_N\}^\mathbb{Z}</math>


In order to determine the safety stock that guarantees a target <math>\alpha_p</math> service
The [[sigma-algebra|&sigma;-algebra]] <math>\mathcal{A}</math> on ''X'' is the product sigma algebra; that is, it is the (countable) [[direct product]] of the σ-algebras of the finite set {1,&nbsp;...,&nbsp;''N''}. Thus, the triplet
level, the [[steady state|stationary]] probability distribution of the inventory on hand must be known. This version of α is also called ''ready rate''.


If an ''order cycle'' is considered as the standard period of reference, then α denotes the probability of no [[stockout]] within an order cycle which is equal to the proportion of all order cycles with no [[stockout]]s (cycle <math>\alpha_c</math> service level):
:<math>(X,\mathcal{A},\mu)</math>


<math>
is a [[measure space]].  The elements of <math>\mathcal{A}</math> are commonly called [[cylinder set]]s. Given a cylinder set <math>[x_0, x_1,\ldots,x_n]</math>, its measure is
\alpha_c = Prob\{Demand~during~replenishment~lead~time
\le  Inventory~on~hand~at~the~beginning~of~the~lead~time\}
</math>


This second definition, which is often used in operations management textbooks, is based on the idea of not running out of stock during the time between re-ordering and order arrival (the leadtime).  That is, the probability of demand during that leadtime being less than or equal to the amount of stock you had left when you ordered.  It assumes your reorder point is positive, that orders are in unit increments and inventory is monitored continuously so you cannot stock out prior to reordering.
:<math>\mu\left([x_0, x_1,\ldots,x_n]\right)=
\prod_{i=0}^n p_{x_i}</math>
The equivalent expression, using the notation of probability theory, is
:<math>\mu\left([x_0, x_1,\ldots,x_n]\right)=
\mathrm{Pr}(X_0=x_0, X_1=x_1, \ldots, X_n=x_n)</math>
for the random variables <math>\{X_k\}</math>


===β service level (type 2)===
The Bernoulli scheme, as any stochastic process, may be viewed as a [[dynamical system]] by endowing it with the [[shift operator]] ''T'' where
The β service level is a '''quantity-oriented''' performance measure describing the
proportion of total demand within a reference period which is delivered without delay from stock on hand:


<math>
:<math>Tx_k = x_{k+1}.</math>
\beta = 1- \frac{ Expected~backorders~per~time~period}
{Expected~period~demand}
</math>


This is equal to the probability that an arbitrary demand unit is delivered without delay. This approach usually involves calculating a loss integral, whose values are tabulated for the normal distribution.<ref>Donald Bowersox, David Closs, M. Bixby Cooper, Supply Chain Logistics Management, McGraw-Hill 2012</ref>
Since the outcomes are independent, the shift preserves the measure, and thus ''T'' is a [[measure-preserving transformation]]. The quadruplet


Because, contrary to the variations of the <math>\alpha</math> service level, the
:<math>(X,\mathcal{A},\mu, T)</math>
<math>\beta</math> service level does not only reflect the [[stockout]] ''event'' but also the
''amount backordered'', it is widely used in industrial practice.


Also, by the definitions, comparing service levels we have <math>\alpha \le \beta</math> whenever the probability of zero demand equals 0.
is a [[measure-preserving dynamical system]], and is called a '''Bernoulli scheme''' or a '''Bernoulli shift'''. It is often denoted by


===γ service level===
:<math>BS(p)=BS(p_1,\ldots,p_N).</math>
The γ service level, a time- and quantity-related performance criterion, serves to reflect not only the amount of backorders but also the waiting times of the demands backordered. The γ service level is defined as follows:


<math>
The ''N'' = 2 Bernoulli scheme is called a [[Bernoulli process]].  The Bernoulli shift can be understood as a special case of the [[Markov shift]], where all entries in the [[adjacency matrix]] are one, the corresponding graph thus being a [[clique (graph theory)|clique]].
\gamma= 1- \frac{ Expected~backorder~level~per~time~period}
{Expected~period~demand}
</math>


The γ service level is rarely used in industrial practice.
==Generalizations==
Most of the properties of the Bernoulli scheme follow from the countable [[direct product]], rather than from the finite base space.  Thus, one may take the base space to be any [[standard probability space]] <math>(Y,\mathcal{B},\nu)</math>, and define the Bernoulli scheme as
:<math>(X, \mathcal{A}, \mu)=(Y,\mathcal{B},\nu)^\mathbb{Z}</math>
This works because the countable direct product of a standard probability space is again a standard probability space.


==Service rate==
As a further generalization, one may replace the in integers <math>\mathbb{Z}</math> by a [[countable]] [[discrete group]] <math>G</math>, so that
*In business, service rate is a [[performance metric]] used to measure the [[customer service]] in a [[supply (economics)|supply]] organization. One example of a service rate measures the number of units filled as a percentage of the total ordered and is known as fill rate. If customer orders total 1000 units, and you can only meet 900 units of that order, your fill rate is 90%.
:<math>(X, \mathcal{A}, \mu)=(Y,\mathcal{B},\nu)^G</math>
For this last case, the shift operator is replaced by the [[group action]]
:<math>gx(f)=x(g^{-1}f)</math>
for group elements <math>f,g\in G</math> and <math>x\in Y^G</math> understood as a function <math>x:G\to Y</math> (any direct product <math>Y^G</math> can be understood to be the set of functions <math>[G\to Y]</math>, as this is the [[exponential object]]). The measure <math>\mu</math> is taken as the [[Haar measure]], which is invariant under the group action:
:<math>\mu(gx)=\mu(x). \, </math>
These generalizations are also commonly called Bernoulli schemes, as they still share most properties with the finite case.


*In statistics, notably in queuing theory, service rate denotes the rate at which customers are being served in a system. It is the reciprocal of the service time. For example, a supermarket cash desk with an average service time of 30 seconds per customer would have an average service rate of 2 per minute. In statistics the Greek letter <math>\mu</math> is used for the service rate.
==Properties==
[[Ya. Sinai]] demonstrated that the [[Kolmogorov entropy]] of a Bernoulli scheme is given by<ref>Ya.G. Sinai, (1959) "On the Notion of Entropy of a Dynamical System", ''Doklady of Russian Academy of Sciences'' '''124''', pp. 768–771.</ref><ref>Ya. G. Sinai, (2007) "[http://web.math.princeton.edu/facultypapers/Sinai/MetricEntropy2.pdf Metric Entropy of Dynamical System]"</ref>
 
:<math>H = -\sum_{i=1}^N p_i \log p_i .</math>
 
This may be seen as resulting from the general definition of the entropy of a [[Cartesian product]] of probability spaces, which follows from the [[asymptotic equipartition property]].  For the case of a general base space <math>(Y, \mathcal{B}, \nu)</math> (''i.e.'' a base space which is not countable), one typically considers the [[relative entropy]]. So, for example, if one has a countable [[partition of a set|partition]] <math>Y'\subset Y</math> of the base ''Y'', such that <math>\nu(Y')=1</math>, one may define the entropy as
 
:<math>H_{Y'} = -\sum_{y'\in Y'} \nu(y') \log \nu(y') .</math>
 
In general, this entropy will depend on the partition; however, for many [[dynamical system]]s, it is the case that the [[symbolic dynamics]] is independent of the partition (or rather, there are isomorphisms connecting the symbolic dynamics of different partitions, leaving the measure invariant), and so such systems can have a well-defined entropy independent of the partition.
 
The [[Ornstein isomorphism theorem]] states that two Bernoulli schemes with the same entropy are [[isomorphism of dynamical systems|isomorphic]].<ref>Donald Ornstein, "Bernoulli shifts with the same entropy are isomorphic", ''Advances in Math.'' '''4''' (1970), pp.337–352</ref> The result is sharp<ref>Christopher Hoffman, "[http://www.ams.org/journals/tran/1999-351-10/S0002-9947-99-02446-0/ A K counterexample machine]", ''Trans. Amer. Math. Soc.'' '''351''' (1999), pp 4263–4280 </ref>, in that very similar, non-scheme systems, such as [[Kolmogorov automorphism]]s, do not have this property.
 
The Ornstein isomorphism theorem is in fact considerably deeper: it provides a simple criterion by which many different [[measure-preserving dynamical system]]s can be judged to be isomorphic to Bernoulli schemes.  The result was surprising, as many systems previously believed to be unrelated proved to be isomorphic. These include all finite{{clarify|date=November 2010}} [[stationary stochastic process]]es, [[subshifts of finite type]], finite [[Markov chain]]s, [[Anosov flow]]s, and [[Sinai's billiards]]: these are all isomorphic to Bernoulli schemes.
 
For the generalized case, the Ornstein isomorpism theorem still holds if the group ''G'' is a countably infinite [[amenable group]].
<ref>D. Ornstein and B. Weiss. "Entropy and isomorphism theorems for actions of amenable groups." ''J. Analyse Math.'' '''48''' (1987), pp. 1–141. </ref><ref>Lewis Bowen (2011), "[http://arxiv.org/abs/1103.4424 Every countably infinite group is almost Ornstein]", ArXiv abs/1103.4424</ref>
 
==Bernoulli automorphism==
An invertible, [[measure-preserving transformation]] of a [[standard probability space]] (Lebesgue space) is called a '''Bernoulli automorphism''' if it [[isomorphism of dynamical systems|isomorphic]] to a Bernoulli shift.<ref>Peter Walters (1982) ''An Introduction to Ergodic Theory'', Springer-Verlag, ISBN 0-387-90599-5</ref>


==See also==
==See also==
* [[Inventory]]
* [[Shift of finite type]]
* [[Service level agreement]]
* [[Markov chain]]
* [[Stockout]]
* [[Hidden Bernoulli model]]


==References==
==References==
{{Reflist}}
<references/>
 
{{DEFAULTSORT:Bernoulli Scheme}}
[[Category:Markov models]]
[[Category:Ergodic theory]]
[[Category:Stochastic processes]]
[[Category:Symbolic dynamics]]


==Further reading==
* Tempelmeier, Horst, ''Inventory Management in Supply Networks'', Norderstedt (Books on Demand) 2006, ISBN 3-8334-5373-7


{{DEFAULTSORT:Service Level}}
[[fr:Décalage de Bernoulli (langage formel)]]
[[Category:Distribution, retailing, and wholesaling]]
[[ru:Символическая динамика]]
[[Category:Supply chain management]]

Revision as of 13:29, 14 August 2014

In mathematics, the Bernoulli scheme or Bernoulli shift is a generalization of the Bernoulli process to more than two possible outcomes.[1][2] Bernoulli schemes are important in the study of dynamical systems, as most such systems (such as Axiom A systems) exhibit a repellor that is the product of the Cantor set and a smooth manifold, and the dynamics on the Cantor set are isomorphic to that of the Bernoulli shift.[3] This is essentially the Markov partition. The term shift is in reference to the shift operator, which may be used to study Bernoulli schemes. The Ornstein isomorphism theorem[4] shows that Bernoulli shifts are isomorphic when their entropy is equal. Finite stationary stochastic processes are isomorphic to the Bernoulli shift; in this sense, Bernoulli shifts are universal.

Definition

A Bernoulli scheme is a discrete-time stochastic process where each independent random variable may take on one of N distinct possible values, with the outcome i occurring with probability , with i = 1, ..., N, and

The sample space is usually denoted as

as a short-hand for

The associated measure is called the Bernoulli measure[5]

The σ-algebra on X is the product sigma algebra; that is, it is the (countable) direct product of the σ-algebras of the finite set {1, ..., N}. Thus, the triplet

is a measure space. The elements of are commonly called cylinder sets. Given a cylinder set , its measure is

The equivalent expression, using the notation of probability theory, is

for the random variables

The Bernoulli scheme, as any stochastic process, may be viewed as a dynamical system by endowing it with the shift operator T where

Since the outcomes are independent, the shift preserves the measure, and thus T is a measure-preserving transformation. The quadruplet

is a measure-preserving dynamical system, and is called a Bernoulli scheme or a Bernoulli shift. It is often denoted by

The N = 2 Bernoulli scheme is called a Bernoulli process. The Bernoulli shift can be understood as a special case of the Markov shift, where all entries in the adjacency matrix are one, the corresponding graph thus being a clique.

Generalizations

Most of the properties of the Bernoulli scheme follow from the countable direct product, rather than from the finite base space. Thus, one may take the base space to be any standard probability space , and define the Bernoulli scheme as

This works because the countable direct product of a standard probability space is again a standard probability space.

As a further generalization, one may replace the in integers by a countable discrete group , so that

For this last case, the shift operator is replaced by the group action

for group elements and understood as a function (any direct product can be understood to be the set of functions , as this is the exponential object). The measure is taken as the Haar measure, which is invariant under the group action:

These generalizations are also commonly called Bernoulli schemes, as they still share most properties with the finite case.

Properties

Ya. Sinai demonstrated that the Kolmogorov entropy of a Bernoulli scheme is given by[6][7]

This may be seen as resulting from the general definition of the entropy of a Cartesian product of probability spaces, which follows from the asymptotic equipartition property. For the case of a general base space (i.e. a base space which is not countable), one typically considers the relative entropy. So, for example, if one has a countable partition of the base Y, such that , one may define the entropy as

In general, this entropy will depend on the partition; however, for many dynamical systems, it is the case that the symbolic dynamics is independent of the partition (or rather, there are isomorphisms connecting the symbolic dynamics of different partitions, leaving the measure invariant), and so such systems can have a well-defined entropy independent of the partition.

The Ornstein isomorphism theorem states that two Bernoulli schemes with the same entropy are isomorphic.[8] The result is sharp[9], in that very similar, non-scheme systems, such as Kolmogorov automorphisms, do not have this property.

The Ornstein isomorphism theorem is in fact considerably deeper: it provides a simple criterion by which many different measure-preserving dynamical systems can be judged to be isomorphic to Bernoulli schemes. The result was surprising, as many systems previously believed to be unrelated proved to be isomorphic. These include all finiteTemplate:Clarify stationary stochastic processes, subshifts of finite type, finite Markov chains, Anosov flows, and Sinai's billiards: these are all isomorphic to Bernoulli schemes.

For the generalized case, the Ornstein isomorpism theorem still holds if the group G is a countably infinite amenable group. [10][11]

Bernoulli automorphism

An invertible, measure-preserving transformation of a standard probability space (Lebesgue space) is called a Bernoulli automorphism if it isomorphic to a Bernoulli shift.[12]

See also

References

  1. P. Shields, The theory of Bernoulli shifts , Univ. Chicago Press (1973)
  2. Michael S. Keane, "Ergodic theory and subshifts of finite type", (1991), appearing as Chapter 2 in Ergodic Theory, Symbolic Dynamics and Hyperbolic Spaces, Tim Bedford, Michael Keane and Caroline Series, Eds. Oxford University Press, Oxford (1991). ISBN 0-19-853390-X
  3. Pierre Gaspard, Chaos, scattering and statistical mechanics(1998), Cambridge University press
  4. Other Sports Official Kull from Drumheller, has hobbies such as telescopes, property developers in singapore and crocheting. Identified some interesting places having spent 4 months at Saloum Delta.

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  5. Achim Klenke, Probability Theory (2006) Springer-Verlag, ISBN 978-1-848000-047-6 doi:10.1007/978-1-848000-048-3
  6. Ya.G. Sinai, (1959) "On the Notion of Entropy of a Dynamical System", Doklady of Russian Academy of Sciences 124, pp. 768–771.
  7. Ya. G. Sinai, (2007) "Metric Entropy of Dynamical System"
  8. Donald Ornstein, "Bernoulli shifts with the same entropy are isomorphic", Advances in Math. 4 (1970), pp.337–352
  9. Christopher Hoffman, "A K counterexample machine", Trans. Amer. Math. Soc. 351 (1999), pp 4263–4280
  10. D. Ornstein and B. Weiss. "Entropy and isomorphism theorems for actions of amenable groups." J. Analyse Math. 48 (1987), pp. 1–141.
  11. Lewis Bowen (2011), "Every countably infinite group is almost Ornstein", ArXiv abs/1103.4424
  12. Peter Walters (1982) An Introduction to Ergodic Theory, Springer-Verlag, ISBN 0-387-90599-5


fr:Décalage de Bernoulli (langage formel) ru:Символическая динамика