A Markovian Entropy-Maximizing Model of Population Distribution

1974 ◽  
Vol 6 (6) ◽  
pp. 693-702 ◽  
Author(s):  
R Lee
Keyword(s):  
Markov Chain ◽  
Stationary Process ◽  
Population Distribution ◽  
Population Densities ◽  
Time Intervals ◽  
Entropy Model ◽  
Regional Modelling ◽  
Distribution Vector ◽  
Biased Estimates ◽  
Over Time

The entropy-maximizing formalism used in urban and regional modelling has typically been applied within a static or equilibrium context. This paper presents a dynamic entropy model of the distribution of population over time. It is initially assumed that a Markov chain adequately represents the residential relocation process. The strategy then involves maximizing the entropy of a Markov chain, subject to suitable constraints, so as to generate least-biased estimates of the Markovian parameters. If a stationary process is assumed, these in turn allow the projection of the probability distribution vector of population densities over successive, equal, time intervals.

International Real Estate Review

2016 ◽  
Vol 19 (3) ◽  
pp. 265-296
Author(s):  
Richard D. Evans ◽  
◽  
Glenn R. Mueller ◽  
Keyword(s):  
Markov Chain ◽  
Real Estate ◽  
Cash Flow ◽  
Discounted Cash Flow ◽  
First Order ◽  
Order Markov Chain ◽  
Office Markets ◽  
Speed Up ◽  
Staying Time ◽  
Over Time

Metro market real estate cycles for office, industrial, retail, apartment, and hotel properties may be specified as first order Markov chains, which allow analysts to use a well-developed application, ¡§staying time¡¨. Anticipations for time spent at each cycle point are consistent with the perception of analysts that these cycle changes speed up, slow down, and pause over time. We find that these five different property types in U.S. markets appear to have different first order Markov chain specifications, with different staying time characteristics. Each of the five property types have their longest mean staying time at the troughs of recessions. Moreover, industrial and office markets have much longer mean staying times in very poor trough conditions. Most of the shortest mean staying times are in hyper supply and recession phases, with the range across property types being narrow in these cycle points. Analysts and investors should be able to use this research to better estimate future occupancy and rent estimates in their discounted cash flow (DCF) models.

Changing Attitudes Toward Euthanasia and Suicide for Terminally Ill Persons, 1977 to 2016

2017 ◽  
Vol 80 (3) ◽  
pp. 355-379 ◽  
Author(s):  
Brandon K. Attell
Keyword(s):  
Regression Models ◽  
Sociological Theory ◽  
Terminally Ill ◽  
Cohort Effects ◽  
Logistic Regression Models ◽  
Period Effects ◽  
Changing Attitudes ◽  
Biased Estimates ◽  
Analysis Point ◽  
Over Time

Several longitudinal studies show that over time the American public has become more approving of euthanasia and suicide for terminally ill persons. Yet, these previous findings are limited because they derive from biased estimates of disaggregated hierarchical data. Using insights from life course sociological theory and cross-classified logistic regression models, I better account for this liberalization process by disentangling the age, period, and cohort effects that contribute to longitudinal changes in these attitudes. The results of the analysis point toward a continued liberalization of both attitudes over time, although the magnitude of change was greater for suicide compared with euthanasia. More fluctuation in the probability of supporting both measures was exhibited for the age and period effects over the cohort effects. In addition, age-based differences in supporting both measures were found between men and women and various religious affiliations.

A stochastic process whose successive intervals between events form a first order Markov chain — I

1968 ◽  
Vol 5 (03) ◽  
pp. 648-668
Author(s):  
D. G. Lampard
Keyword(s):  
Markov Chain ◽  
Point Processes ◽  
Electronic Device ◽  
Statistical Properties ◽  
Time Intervals ◽  
First Order ◽  
Stochastic Point Process ◽  
Order Markov Chain ◽  
The One ◽  
Stochastic Point Processes

In this paper we discuss a counter system whose output is a stochastic point process such that the time intervals between pairs of successive events form a first order Markov chain. Such processes may be regarded as next, in order of complexity, in a hierarchy of stochastic point processes, to “renewal” processes, which latter have been studied extensively. The main virtue of the particular system which is studied here is that virtually all its important statistical properties can be obtained in closed form and that it is physically realizable as an electronic device. As such it forms the basis for a laboratory generator whose output may be used for experimental work involving processes of this kind. Such statistical properties as the one and two-dimensional probability densities for the time intervals are considered in both the stationary and nonstationary state and also discussed are corresponding properties of the successive numbers arising in the stores of the counter system. In particular it is shown that the degree of coupling between successive time intervals may be adjusted in practice without altering the one dimensional probability density for the interval lengths. It is pointed out that operation of the counter system may also be regarded as a problem in queueing theory involving one server alternately serving two queues. A generalization of the counter system, whose inputs are normally a pair of statistically independent Poisson processes, to the case where one of the inputs is a renewal process is considered and leads to some interesting functional equations.

Timing of Aldicarb Applications to Control Meloidogyne arenaria in Peanut1

2001 ◽  
Vol 28 (2) ◽  
pp. 73-75 ◽  
Author(s):  
J. R. Rich ◽  
D. W. Gorbet
Keyword(s):  
Arachis Hypogaea ◽  
Chemical Treatment ◽  
Optimal Time ◽  
Meloidogyne Arenaria ◽  
Root Knot Nematode ◽  
Population Densities ◽  
Time Intervals ◽  
Yield Increase ◽  
Post Harvest ◽  
Chemical Treatments

Abstract Four fieldtrialswere conductedin northwest Florida to determine the efficacyofaldicarb appliedat varyingtime intervals after planting on peanut (Arachis hypogaea) to manage the peanut root-knot nematode, Meloidogyne arenaria. Initial treatments with aldicarb (Temik 15G), fenamiphos (Nemacur 15G), and phorate (Thimet 15G) were made at planting of peanut cv. Southern Runner. The chemicals were applied as 20-cm-wide bands over the open seed furrow using a tractor-mounted Gandy applicator. Post-plant treatments were made with a Gandy applicator at time intervals from 28 to 104 dafter planting as 36-cm-wide bands over the row centers. Post-harvest M. arenaria population densities were affected little by any chemical treatment compared to the control. The efficacy of the chemical treatments was variable and averaged onlya 295-kglha yield increase for the single at-plant applications of aldicarb compared to the control. Allat-plant + post-plant aldicarb treatments increased yield over the control by an average of712 kg¡ ha. Results from these trials did not establish a single optimal time for post-plant application of aldicarb on peanut. Data from these tests, however, indicated that a post-plant aldicarb treatment can be applied latter than previously recommended in Florida.

Markov chains in small time intervals

1981 ◽  
Vol 18 (3) ◽  
pp. 747-751
Author(s):  
Stig I. Rosenlund
Keyword(s):  
Markov Chain ◽  
Markov Chains ◽  
Transition Probability ◽  
Small Time ◽  
Exact Order ◽  
Time Intervals ◽  
Continuous Parameter ◽  
Minimum Number

For a time-homogeneous continuous-parameter Markov chain we show that as t → 0 the transition probability pn,j (t) is at least of order where r(n, j) is the minimum number of jumps needed for the chain to pass from n to j. If the intensities of passage are bounded over the set of states which can be reached from n via fewer than r(n, j) jumps, this is the exact order.

Infinite dams with inputs forming a Markov chain

1968 ◽  
Vol 5 (1) ◽  
pp. 72-83 ◽  
Author(s):  
M. S. Ali Khan ◽  
J. Gani
Keyword(s):  
Markov Chain ◽  
Storage Systems ◽  
Random Variables ◽  
Time Intervals ◽  
Storage Theory ◽  
Annual Time ◽  
Theory Of Storage ◽  
Independent And Identically Distributed

Moran's [1] early investigations into the theory of storage systems began in 1954 with a paper on finite dams; the inputs flowing into these during consecutive annual time-intervals were assumed to form a sequence of independent and identically distributed random variables. Until 1963, storage theory concentrated essentially on an examination of dams, both finite and infinite, fed by inputs (discrete or continuous) which were additive. For reviews of the literature in this field up to 1963, the reader is referred to Gani [2] and Prabhu [3].

scholarly journals Hubungan Perilaku Perdagangan Investor Dengan Volume Perdagangan Di Bursa Efek Indonesia

2009 ◽  
Vol 2 (1) ◽  
Author(s):  
Ghazali Syamni
Keyword(s):  
Trading Volume ◽  
Stock Exchange ◽  
Time Interval ◽  
Time Intervals ◽  
Using Data ◽  
Changes Over Time ◽  
Relationship Of ◽  
The Relationship ◽  
Order Pattern ◽  
Over Time

This paper examines the relationship of behavior trading investor using data detailed transaction history-corporate edition demand and order history in Indonesia Stock Exchange during period of March, April and May 2005. Peculiarly, behavior placing of investor order at trading volume. The result of this paper indicates that trading volume order pattern to have pattern U shape. The pattern happened that investors have strong desires to places order at the opening and close of compared to in trading periods. While the largest orders are of market at the opening indicates that investor is more conservatively when opening, where many orders when opening has not happened transaction to match. In placing order both of investor does similar strategy. By definition, informed investors’ orders more large than uninformed investors. If comparison of order examined hence both investors behavior relatively changes over time. But, statistically shows there is not ratio significant. This implies behavior trading of informed investors and uninformed investors stable relative over time. The result from regression analysis indicates that informed investors to correlate at trading volume in all time intervals, but not all uninformed investors correlates in every time interval. This imply investor order inform is more can explain trading volume pattern compared to uninformed investor order in Indonesia Stock Exchange. Finally, result of regression also finds that order status match has greater role determines trading volume pattern intraday especially informed buy match and informed sale match. While amend, open and withdraw unable to have role to determine intraday trading volume pattern.

Active Optimum Control of Orthotropic Plates Using Piezoelectric Stiffeners

1993 ◽  
Author(s):  
Victor Birman ◽  
Sarp Adali
Keyword(s):  
Time Interval ◽  
Short Time Interval ◽  
Bending Moments ◽  
Optimum Control ◽  
Orthotropic Plates ◽  
Time Intervals ◽  
Control Forces ◽  
Short Time ◽  
Alternating Voltage ◽  
Over Time

Abstract Active control of orthotropic plates subjected to an impulse loading is considered. The dynamic response is minimized using in-plane forces or bending moments induced by piezoelectric stiffeners bonded to the opposite surfaces of the plate and placed symmetrically with respect to the middle plane. The control forces and moments are activated by a piece-wise constant alternating voltage with varying switch-over time intervals. The magnitude of voltage is bounded while the switch-over time intervals are constantly adjusted to achieve an optimum control. Numerical examples presented in the paper demonstrate the effectiveness of the method and the possibility of reducing the vibrations to very small amplitudes within a short time interval which is in the order of a second.

Finding Minimal Infrequent Elements in Multi-Dimensional Data Defined over Partially Ordered Sets and its Applications

2010 ◽  
pp. 98-116
Author(s):  
Khaled M. Elbassioni
Keyword(s):  
Association Rules ◽  
Partially Ordered Set ◽  
Quantitative Data ◽  
Partially Ordered Sets ◽  
Threshold Value ◽  
Ordered Sets ◽  
Time Intervals ◽  
Partially Ordered ◽  
The Given ◽  
Over Time

The authors consider databases in which each attribute takes values from a partially ordered set (poset). This allows one to model a number of interesting scenarios arising in different applications, including quantitative databases, taxonomies, and databases in which each attribute is an interval representing the duration of a certain event occurring over time. A natural problem that arises in such circumstances is the following: given a database D and a threshold value t, find all collections of “generalizations” of attributes which are “supported” by less than t transactions from D. They call such collections infrequent elements. Due to monotonicity, they can reduce the output size by considering only minimal infrequent elements. We study the complexity of finding all minimal infrequent elements for some interesting classes of posets. The authors show how this problem can be applied to mining association rules in different types of databases, and to finding “sparse regions” or “holes” in quantitative data or in databases recording the time intervals during which a re-occurring event appears over time. Their main focus will be on these applications rather than on the correctness or analysis of the given algorithms.

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