Queueing Theory In Hospital Management

Queueing Theory In Hospital Management
Applications of queueing theory in hospital management: a literature review
Abstract
This paper reviews the applications and usage of the queueing theory in the aspect of health care
management problems. This paper review presents a way of optimizing the use of hospital resources
in order to improve hospital care. A queueing model is used to determine the main characteristics of
the access of patients to hospital beds, such as mean bed occupancy and the probability that hospital
care demand is lost because all beds are occupied. The aim of this paper review is to provide
detailed information to analysts who are interested in using queueing theory to model a health care
process and want to look into a technique for optimizing the number of beds in order to maintain an
acceptable delay probability at a sufficiently low level.
Keywords
Queueing theory, hospital planning, bed management, literature review, Poisson process
Introduction ... Show more content on Helpwriting.net ...
[1, 5] Queuing theory is applicable to any situation in general life ranging from cars arriving at
filling stations for fuel, customers arriving at a bank for various services, customers at a supermarket
waiting to be attended to by a cashier and in healthcare settings. [8, 3] Queuing theory can also be
applied to the analysis of waiting lines in healthcare settings. Most of healthcare systems have
excess capacity to accommodate random variations while some do not, so queuing theory analysis
can be used as short term measures, or for facilities and resource planning. The major problem
hospitals face
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Optimized Dynamic Latent Topic Model For Big Text Data...
JOMO KENYATTA UNIVERSITY
OF
AGRICULTURE AND TECHNOLOGY
SCHOOL OF COMPUTING AND INFORMATION TECHNOLOGY
Optimized Dynamic Latent Topic Model for Big Text Data Analytics
NAME: Geoffrey Mariga Wambugu
REGISTRATION NUMBER: CS481–4692/2014
LECTURER: Prof. Waweru Mwangi
A thesis proposal submitted in partial fulfilment of the requirement for the Unit SCI 4201 Advanced
Research Methodology of the degree of Doctor of Philosophy in Information Technology at the
School of Computing and Information Technology, Jomo Kenyatta University of Agriculture and
Technology
June 2015
Abstract
Probabilistic topic modeling provides computational methods for large text data analysis. Today
streaming text mining plays an important role within real–time social media mining. Latent Dirichlet
Allocation (LDA) model was developed a decade ago to aid discovery of the hidden thematic
structure in large archives of documents. It is acknowledged by many researchers as the most
popular approach for building topic models. In this study, we discuss topic modeling and more
specifically LDA. We identify speed as one of the major limitations of LDA application in streaming
big text data analytics. The main aim of this study is to enhance inference speed of LDA thereby
develop a new inference method and algorithm. Given the characteristics of this specific research
problem, the approach to the proposed research will follow the experimental model. We will
investigate causal relationships using a test

Reliability Based Cost Model Essay
Reliability–Based Cost Model Development As it is of December 2017, AWST contracts Romax for
wind farm Operation and Maintenance costs estimates. The motivation for contracting Romax
instead of performing an estimate ourselves is that Romax can do a better job than AWST due to a
larger database of failure modes and deeper expertise. Due to this reason, AWST does not have a
model that could have ingested the data even if it was available that is an additional barrier for in–
house cost model. This memo will lay down the difficulties and the way to overcome them to create
an in–house reliability based cost model. An additional benefit of pursuing reliability statistics is the
enhanced suitability review that could flag potential component ... Show more content on
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For a numerical implementation, this equation can be written in vector matrix form below. ψ(P ⃗
)=f^1 (P ⃗ )+∫_T▒〖ψ(P ⃗')K((P') ⃗,P ⃗ )d(P') ⃗ 〗 ψ(P ⃗ )= event density for a state vector P at
time t. f^1 (P ⃗ ) = first state probability ψ(P ⃗') = event density at time t' K((P') ⃗,P ⃗ )= state
transition kernel. This equation looks complicated but it is based only on the Weibull failure
distribution and Markov matrices process which some people of our team are very familiar with.
Alternatively, a simple Monte Carlo simulation for two states "up" and "down" can be calculated
using the Weibull failure distribution described below. Developing an algorithm to process the time
in operating, failing, and failed condition for each of the components is the key element for the cost
modeling. This effort should go in parallel with obtaining the Weibull distributions for each of the
components described below. A feedback should be obtained from Brian Kramak and Stephen
Lightfoote to quantify the time and effort required for developing Markov Chain Monte Carlo
algorithm. Weibull Distribution Most of the failure modes follow a two–parameter Weibull failure
distribution as shown below. F(t)=1–exp[–(t/η(X) )^β ] η(X)=characteristic life (scale parameter)
β=Weibull slope (shape parameter) β is obtained through Weibull–Log fit from field data. η(X) is
obtained through fitting a Weibull–Log Linear model (also known as Weibull–regression or
Weibull–proportional–hazards).

Complications Of Engineering And Engineering
In almost any quantitative field of research (as well as in applied science), the researcher (or, e.g.,
engineer or economist) frequently needs to fit a parametrized function to observed data. In some
cases to make interpolations or extrapolations; the engineer may be interested in values between
expensive measurement points, and the economist may be interested in giving a prognosis for the
future. In other cases, the parameters (–– removed HTML ––) themselves (–– removed HTML ––)
can be the primary interest. In nuclear physics, it can be of interest to know the fraction of nuclear
reactions yielding a particular reaction product; this is an example we will return to repeatedly
throughout this paper, starting in Sec. (–– removed HTML ... Show more content on Helpwriting.net
...
Everything is presented in general terms, allowing for any type of data covariance matrix, i.e., not
only to uncorrelated observations. (–– removed HTML ––) (–– removed HTML ––) It is often
fruitful to adopt a Bayesian view, in which the parameters of the fitting function can have a prior
distribution (prior to observing the data), and from fitting, the posterior distribution is obtained.
Informally stated, we have an idea about some of the parameters before observing the data (see Sec.
(–– removed HTML ––) I A (–– removed HTML ––) for an illuminating example), and we wish to
include this knowledge in our final estimate of the parameters and/or the fitted function. It is a
standard procedure to incorporate such a prior distribution in linear least squares, and it can be
included in the LM algorithm by, formally, treating the prior information as an additional set of data.
In this work, however, it is clearly presented how the data and the prior information can be separated
by exploiting the structure of the involved matrices and vectors, see Sec. (–– removed HTML ––) II
B (–– removed HTML ––) . (–– removed HTML ––) (–– removed HTML ––) Unfortunately, it is
not enough that models often are non–linear; even worse, they are often (not to say always) (––
removed HTML ––) wrong (–– removed HTML ––) . That is, whatever parameters we choose, it is
impossible to reproduce the truth which is lying behind the observed data. We call this a model
defect. Model defects can

Advantages And Disadvantages Of Stochastic Model
3.1 Deterministic models There are two types of model that we are going to look at, firstly the
deterministic model and then the stochastic model. [23]A deterministic model is used in a situation
where the result can be established straightforwardly from a series of conditions. It has no stochastic
elements and both the input and the outputs are determined conclusively. On the other hand a
stochastic model is one where the cause and effect relationship is stochastically or randomly
determined. Therefore the system having stochastic element is generally not solved analytically and
hence there are several cases for which it is difficult to build an intuitive perspective. When
simulating a stochastic model a random number is usually generated ... Show more content on
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This is illustrated in figure 3 below. The chain ladder method explicitly relies on the assumption that
the expected cumulative losses settled up to and including the development year divided by the
expected cumulative claims losses settled up to and including the previous development year hold
for all claim occurrence years. 3.1.4. A Loss development data Let us consider a range of risks and
assume that each claim of the portfolio is settled either in the accident year or in the following n
development years. The data can be modelled by cumulative losses and incremental losses. 3.1.4. B
Incremental losses Let CI,J where i, j Ɛ{1.2...n} (a) represent incremental losses of accident year i
which is settled with a delay of j years and therefore in development year j. Let us also assume that
incremental losses C I, j are observable for calendar years i + J ≤ n and are non–observable for
calendar years i + J ≥ n + 1. The runoff triangle below shows the incremental losses for accident
years 2000 developing over 10 years. In this case the incremental loss for 2000 development year 5
(C2000,5) is given by 89837.06 1 2 3 4 5 6 7 8 9 10 2000 24698 58384 112485 61605 89837 36174
22525 48206 19747

Essay On Engineering Service Systems
LCurrent technology–driven innovations in service systems tend to take the human server out of the
loop. That being the case, the substitution of human labor will potentially affect the United States
and other developed economies most, as the service sector in these countries is responsible for the
majority of employment. To improve this outlook, effective ways of integrating humans with
engineered service systems is needed. Instead of replacing human workers with machines, one could
think of an engineered partnership between both agents. For example, the necessary improvements
in the healthcare and education sectors will use people to do what people do best (e.g. creativity,
synthesis, improvisation, social skills), and machines to do ... Show more content on Helpwriting.net
...
Hence, considering humans in the optimization of their designs. What is needed is convergent
research. Convergence is a research approach that cuts across fields to tackle societal problems that
require solutions at the interfaces of different disciplines. As stated by the National Academies, what
is needed is a "comprehensive synthetic framework" that melds the knowledge at the intersection of
these disciplines. But there are multiple difficulties to be overcome for the principles and models of
behavioral and cognitive science to converge with engineering and mathematics.
To overcome the challenges for convergence, languages and lingos need to be shared to guide
engineers to important human aspects that need to be represented mathematically. In turn, this space
might guide behavioral and cognitive scientists to research questions about humans that are
meaningful for engineers and vice versa. This middle ground could conceivably be the right meeting
space to foster the mathematical language that could incorporate randomness, improvisation and
other human characteristics that we need to model to achieve perfect cooperation between machines
and humans. This mathematical language or framework could be based on advances in the calculus
of finite differences, Markov chains, or a completely different paradigm. We are just beginning this
exploration of potential modeling approaches that

Essay On Road Deterioration Analysis
3.7 Modeling techniques used for road deterioration analysis
(Madanat et al., 1997) in their research exhibit incremental facility deterioration model on bridge
deck sample. Since infrastructure moves from one transitional state to another with a set probability
associated with the transition process, with the help of explanatory variables predicts the changes in
condition of infrastructure over a period using the incremental models. The data used in this case is
panel data. The previous research that has been done in this area does not account for the effects of
heterogeneity in panel data. Due to the presence of unobserved factors the coefficient estimates of
the model may be biased. The previous models like linear regression had ... Show more content on
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Finally, the researchers could develop a model that was theoretically sound, produced satisfactory
estimates and in which set of explanatory variables were linked to deterioration.
(Prozzi et al., 2003) The condition of the pavement should be known to the authorities to make an
accurate and informed decision about the maintenance program and subsequently about the budget
that is required for the program. But knowing the condition of road for maintenance purpose is not
straightforward as failure can occur any time, as it is a highly variable event. The modeling of event
duration becomes difficult because of the variability in failure time. Truncation bias and censoring
bias are associate with the failure events. In a survey if we include only failure events it will give
rise to truncation bias and if failure events are censored model may suffer from censoring bias. The
author uses probabilistic duration modeling techniques for analysis because these models can
evaluate stochastic nature of payment failure and takes care of the censored data to be incorporated
for modeling because in case the censored data is not accounted for modeling it will result in biased
model parameters. The advantages of using probabilistic duration modeling techniques is that it is
based on robust statistical principles and the failure times are predicted better. In short, the pavement

A & M Research Statement
Research Statement
Nilabja Guha Texas A&M University
My current research at Texas A&M University is in a broad area of uncertainty quantification (UQ),
with applications to inverse problems, transport based filtering, graphical models and online
learning. My research projects are motivated by many real–world problems in engineering and life
sciences. In my current postdoctoral position in the Institute for Scientific Computation (ISC) at
Texas A&M University, I have worked with Professor Bani K. Mallick from the department of
statistics and Professor Yalchin Efendiev from the department of mathematics. I have collaborated
with researchers in engineering and bio–sciences on developing rigorous uncertainty quantification
methods within the Bayesian ... Show more content on Helpwriting.net ...
A hierarchical Bayesian model is developed in the inverse problem setup. The Bayesian approach
contains a natural mechanism for regularization in the form of a prior distribution, and a LASSO
type prior distribution is used to strongly induce sparseness. We propose a variational type algorithm
by minimizing the Kullback–Leibler divergence between the true posterior distribution and a
separable approximation. The proposed method is illustrated on several two–dimensional linear and
nonlinear inverse problems, e.g., Cauchy problem and permeability estimation problem. The
proposed method performs comparably with full Markov chain Monte Carlo (MCMC) in terms of
accuracy and is computationally

Key Properties Of Galaxy Clusters
section{Results} label{sec::result}
The fact that this galaxy cluster was not identified by textit{ROSAT} as a cluster suggests that there
may be a hidden population of galaxy clusters hosting extreme central galaxies (i.e. starbursts and/or
QSOs). Table~ref{table::keyvalue} shows the key properties of both PKS1343–341 which are
derived in this work ($R_{500}, M_{500}, M_{rm{gas},500}, T_x, L_x, t_{rm{cool},0},
rm{SFR}$) and other similar clusters, including Abell 1795 (a strong cool core cluster) and 3C 186
(a quasar–mode cluster).
begin{deluxetable*}{ccccc}
tabletypesize{footnotesize}
tablecaption{Key properties for the galaxy clusterlabel{table::keyvalue}}
tablecolumns{0}
tablewidth{0pt}
tablehead{ colhead{Property ... Show more content on Helpwriting.net ...
We assume that the cluster is located at the same redshift as the central AGN.}
tablenotetext{b}{$T_x$ is measured from 0.15$R_{500}$ to 1.0$R_{500}$.}
tablenotetext{c}{SFR is measured from the UV luminosity of the BCG for PKS1353–341. (see
Section~ref{sec::sfr})}
tablenotetext{d}{Most of the numbers for Abell 1795 are from~citet{2006VikhlininA}, except
SFR is from~citet{2005Hicks}.}
tablenotetext{e}{All the numbers of 3C 186 are
from~citet{2005Siemiginowska,2010Siemiginowska}}
tablenotetext{f}{$0.85,R_{500}$ is the edge of the chip to guarantee the luminosity calculation.}
tablenotetext{g}{These numbers are from~citet{2010Russell,2014Walker}}
tablenotetext{h}{The cooling radius is defined to be a radius at which the cooling time fell to 7.7
Gyr while the cooling rate is defined within the cooling radius.}
end{deluxetable*}
In the following sections, we discuss morphology and different derived properties of the cluster,
involving the gas fraction, entropy, total hydrostatic mass and its cooling time.
subsection{X–ray and Optical Morphology}
begin{figure}[!ht]

My Teaching Philosophy
Since the beginning of my academic career, teaching has always been an important part of my
academic duties.
The interaction that I have with students is not only enjoyable to me, but it also gives me an
invaluable perspective on the subjects I am teaching. Since I started my position at the Mathematical
Institute at the University of
Oxford, I have tutored in four classes across three semesters and supervised two projects, as detailed
in my CV.
I am also tutoring two new undergraduate classes in the first semester of 2017. I was also a teaching
assistant to my PhD advisor for various classes and given have given multiple practical short–
courses on my software library for Uncertainty Quantification, mimclib. Throughout, I was lucky to
have ... Show more content on Helpwriting.net ...
I was particularly happy when a student would give a solution that is different from the one I had in
mind. In that instance, I would encourage the student to give further details and I would ask other
students if they had other methods. This ensured that the students were not only engaged but
actively contributing to the lecture. Even though student engagement is easier to accomplish in
smaller classrooms, it is even more important in larger classrooms where students' voices drown in
the hollow of the lecture hall. Ensuring that at least a portion of the students is engaged will
encourage certain students to ask questions which are likely to be on the mind of other, more
reserved, students.
In my opinion, learning in a class should simulate scientific research as much as possible. When a
researcher in mathematics studies a new subject, she starts with an observation, makes a conjecture,
verifies the conjecture with experiments and finally, formulates a generalisation with a proof. This
process enforces a context which the researcher keep referring to, namely the original example. The
result is a deeper understanding of the concepts and the ability to predict the future ones. As a
teacher, I try to simulate a faster version of this research process.
I try to start from simple examples that demonstrate some aspect of the topic. I then try to make the

Capital Structure Decisions
Capital Structure Decisions: Which Factors are Reliably Important? Murray Z. Frank1 and Vidhan
K. Goyal2 First draft: March 14, 2003. Current draft: December 20, 2003. ABSTRACT This paper
examines the relative importance of 38 factors in the leverage decisions of publicly traded U.S.
firms from 1950 to 2000. The most reliable factors are median industry leverage (+ effect on
leverage), market–to–book ratio (–), collateral (+), bankruptcy risk as measured by Altman's Z–
Score (–), dividend–paying (–), log of sales (+), and expected inflation (+). These seven factors all
have the sign predicted by the trade–off theory. The pecking order and market timing theories are
not as helpful in predicting the importance and the signs of the reliable ... Show more content on
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To address this serious concern the effect of conditioning on firm circumstances is studied. We do
find reliable empirical patterns.3 From a set of 38 factors that have been used in the literature, seven
have reliable relationships to corporate leverage. Firms that compete in industries in which the
median firm has high leverage tend also to have high leverage. Firms that have high levels of sales
tend to have high leverage. Firms that have more collateral tend to have more leverage. When
inflation is expected to be high firms tend to have high leverage. Firms that have a high risk of
bankruptcy, as measured by Altman's Z–score, have low leverage. Firms that pay dividends tend to
have lower leverage than do firms that do not pay dividends. Finally firms that have a high market–
to–book ratio tend to have low levels of leverage. These seven factors account for more than 30% of
the variation in leverage, while then remaining 31 factors only add a further 6%. These seven factors
have very consistent sign and statistical significance across many alternative treatments of the data.
The remaining factors are not nearly as consistent. All seven of the reliable factors have signs that
are predicted by the trade–off theory of leverage. Market timing theory makes correct predictions for
the market–to–book and inflation variables. However it does not make any predictions for the

Data Preparation And Quality Of Data Essay
Introduction Data gathering methods are often loosely controlled, resulting in out–of–range values
(e.g., Income: –100), impossible data combinations (e.g., Gender: Male, Pregnant: Yes), missing
values, etc. Analyzing data that has not been carefully screened for such problems can produce
misleading results. Thus, the representation and quality of data is first and foremost before running
an analysis. If there is much irrelevant and redundant information present or noisy and unreliable
data, then knowledge discovery during the training phase is more difficult. Data preparation and
filtering steps can take considerable amount of processing time. Data pre–processing includes
cleaning, normalization, transformation, feature extraction and selection, etc. The product of data
pre–processing is the final training set. Data Pre–processing Methods Raw data is highly susceptible
to noise, missing values, and inconsistency. In order to help improve the quality of the data and,
consequently of the results, raw data is pre–processed. Data preprocessing is one of the most critical
steps in data analysis which deals with the preparation and transformation of the initial dataset. Data
preprocessing methods are divided into following categories:  Data Cleaning  Data Integration 
Data Transformation  Data Reduction Data Cleaning Data that is to be analyzed can be incomplete
(lacking attribute values or certain attributes of interest, or containing only aggregate data), noisy

A & M Research Statement
Research Statement
Nilabja Guha Texas A&M University
My current research at Texas A&M University is in a broad area of uncertainty quantification (UQ),
with applications to inverse problems, transport based filtering, graphical models and online
learning. My research projects are motivated by many real–world problems in engineering and life
sciences. I have collaborated with researchers in engineering and bio–sciences on developing
rigorous uncertainty quantification methods within Bayesian framework for computationally
intensive problems. Through developing scalable and multi–level Bayesian methodology, I have
worked on estimating heterogeneous spatial fields (e.g., subsurface properties) with multiple scales
in dynamical systems. In ... Show more content on Helpwriting.net ...
Some of the areas I have explored in my Ph.D. work include measurement error model with
application in small area estimation, risk analysis of dose–response curves. The stochastic
approximation methods have application in density estimation, deconvolution and posterior
computation. A discussion of my current and earlier projects are given next.
1 UQ for estimating heterogeneous fields
To predict the behavior of a physical system governed by a complex mathematical model depends
on un– derlying model parameters. For example, predicting the contaminant transport or oil
production strongly influenced by subsurface properties, such as permeability, porosity and other
spatial fields. These spatial fields are highly heterogeneous and vary over a rich hierarchy of scales,
which makes the forward models
1
be computationally intensive. The quantities determining the system are partially known and
represent information at some range of spatio–temporal scales. Bayesian modeling is important in
quantifying the un– certainty, identifying dominant scales and features, and learning the system.
Bayesian methodology provides a natural framework for such problems with specifying prior
distribution on the unknown and the likelihood equation. Solution procedure use Markov Chain
Monte Carlo (MCMC) or related methodology, where, for each of the proposed parameter value, we
solve

Past, Present & Future Role of Computers in Fisheries
Chapter 1
Past, Present and Future Trends in the Use of Computers in Fisheries Research
Bernard A. Megrey and Erlend Moksness
I think it's fair to say that personal computers have become the most empowering tool we've ever
created. They're tools of communication, they're tools of creativity, and they can be shaped by their
user. Bill Gates, Co–founder, Microsoft Corporation Long before Apple, one of our engineers came
to me with the suggestion that Intel ought to build a computer for the home. And I asked him, 'What
the heck would anyone want a computer for in his home?' It seemed ridiculous! Gordon Moore, Past
President and CEO, Intel Corporation
1.1 Introduction
Twelve years ago in 1996, when we prepared the first edition of ... Show more content on
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Our aim is to provide critical reviews on the latest, most significant developments in selected topic
areas that are at the cutting edge of the application of computers in fisheries and their application to
the conservation and management of aquatic resources. In many cases, these are the same authors
who contributed to the first edition, so the decade of perspective they provide is unique and
insightful. Many of the topics in this book cover areas that were predicted in 1989 to be important in
the future (Walters 1989) and continue to be at the forefront of applications that drive our science
forward: image processing, stock assessment, simulation and games, and networking. The chapters
that follow update these areas as well as introduce several new chapter topic areas. While we
recognize the challenge of attempting to present up to date information given the rapid pace of
change in computers and the long time lines for publishing books, we hope that the chapters in this
book taken together, can be valuable where they suggest emerging trends and future directions that
impact the role computers are likely to serve in fisheries research.
1
Past, Present and Future Trends in the Use of Computers
3

1.2 Hardware Advances
It is difficult not to marvel at how quickly

Marketing Literature Review
Marketing Literature Review
This section is based on a selection of article abstracts from a comprehensive business literature
database. Marketing–related abstracts from over 125 journals (both academic and trade) are
reviewed by JM staff. Descriptors for each entry are assigned by JM staff. Each issue of this section
represents three months of entries into the database. JM thanks UMI for use of the ABI/INFORM
business database. Each entry has an identifying number. Cross–references appear immediately
under each subject heading. The following article abstracts are available online from the
ABI/INFORM database, which is published and copyrighted by UMI. For additional information
about access to the database or about obtaining photocopies ... Show more content on
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64 (April 2000), 109–121
Marketing Literature Review / 109
dictors for potential online–service adoption; Implications for advertisers.] 7 Using Self–Concept
to Assess Advertising Effectiveness. Abhilasha Mehta, Journal of Advertising Research, 39
(January/February 1999), pp. 81–89. [Literature review, Data collection (Gallup and Robinson),
Advertising performance by age and psychological segments (adventurous, sensual/elegant,
sensitive), Recall, Purchase intent, Brand rating, Commercial liking, Diagnostics, Concept
Convergence Analysis.] 8 Consumers' Extent of Evaluation in Brand Choice. B.P.S. Murthi and
Kannan Srinivasan, Journal of Business, 72 (April 1999), pp. 229–56. [Literature review, Model
proposal and estimation, Scanner data, Impacts, Price, Display feature, Purchase occasions,
Weekday, Store loyalty, Household income, Education, Frequency of purchases, Time availability,
Deal–proneness, Statistical analysis, Managerial implications.] 9 Consumer Behavioral Loyalty: A
Segmentation Model and Analysis. Chi Kin (Bennett) Yim and P.K. Kannan, Journal of Business
Research, 44 (February 1999), pp. 75–92. [Literature review, Scanner panel data, Loyalty–building
strategies depend on the composition of a brand's hard–core loyal and reinforcing loyal base and on
factors (marketing mix or product attributes) that motivate reinforcers to repeat purchase the
brands.] 10 The Effect of Time Pressure on Consumer Choice Deferral. Ravi Dhar and

Test For Aggregation Bias On The United States Personal...
The purpose of this analysis is to test for aggregation bias in the United States Personal
Consumption Expenditure (PCE). This paper uses first and second generation panel unit root
testsfootnote{For more information see Hurlin (2007). } on the National Income and Product
Accounts (NIPA) that make up the PCE. Second generation tests differ from first generation tests in
that the latter drop the assumption of cross sectional independence the error term. Aggregation bias
exists if NIPA inflation differentials converge or diverge at different levels of aggregation. An
inflation differential is the difference between inflation rates in one sector and the inflation rate in
another sector. Higher levels of aggregation are made to represent the lower, more dis–aggregate
levels. If aggregates properly represent the underlying data then each level should converge or
diverge the same. Aggregation is important because the process used to aggregate the data may
remove information from the data and create divergent inflation differentials when dis–aggregate
inflation rates converge. Monetary policy of the Federal Open Market Committee (FOMC) is based
on a target inflation rate, however there are concerns that if the FOMC focus 's on aggregate
inflation it may cause individual sectors to diverge. Clark (2006) uses dis–aggregate quarterly NIPA
accounts to study the distribution of inflation persistence across consumption sectors. Inflation
persistence is the tendency of inflation to

Monte Carlo Simulation
Preface This is a book about Monte Carlo methods from the perspective of financial engineering.
Monte Carlo simulation has become an essential tool in the pricing of derivative securities and in
risk management; these applications have, in turn, stimulated research into new Monte Carlo
techniques and renewed interest in some old techniques. This is also a book about financial
engineering from the perspective of Monte Carlo methods. One of the best ways to develop an
understanding of a model of, say, the term structure of interest rates is to implement a simulation of
the model; and finding ways to improve the efficiency of a simulation motivates a deeper
investigation into properties of a model. My intended audience is a mix of graduate ... Show more
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Students often come to a course in Monte Carlo with limited exposure to this material, and the
implementation of a simulation becomes more meaningful if accompanied by an understanding of a
model and its context. Moreover, it is precisely in model details that many of the most interesting
simulation issues arise. If the first three chapters deal with running a simulation, the next three deal
with ways of running it better. Chapter 4 presents methods for increasing precision by reducing the
variance of Monte Carlo estimates. Chapter 5 discusses the application of deterministic quasi–
Monte Carlo methods for numerical integration. Chapter 6 addresses the problem of discretization
error that results from simulating discrete–time approximations to continuous–time models. The last
three chapters address topics specific to the application of Monte Carlo methods in finance. Chapter
7 covers methods for estimating price sensitivities or "Greeks." Chapter 8 deals with the pricing of
American options, which entails solving an optimal stopping problem within a simulation. Chapter 9
is an introduction to the use of Monte Carlo methods in risk management. It discusses the
measurement of market risk and credit risk in financial portfolios. The models and methods of this
final chapter are rather different from vii those in the other chapters,

The Cost Effectiveness Of A Drug Or Treatment
Rising healthcare costs are a growing concern among individuals, employers, and the federal
government. The national conversation on how to best control those costs has forced many drug
manufacturers to reevaluate the economics of new, expensive drugs and therapies. Now more than
ever, the need to evaluate outcomes and costs associated with alternative treatments has never been
greater.
Understanding the cost effectiveness of a drug or treatment can be a challenge. Clinical trials are
traditionally performed on subsets of the population in tightly controlled environments for a
relatively short time. They are primarily responsible for evaluating treatment efficacy. But pressure
to control healthcare costs has increased the emphasis on ... Show more content on Helpwriting.net
...
Chance nodes (circles) depict the possible consequence – positive or negative – of the decision.
They are referred to as transition states. Transition probabilities are assigned to each transition state
and they must always sum to one. Triangles indicate the point at which the analysis ends and the
health impact and/or costs of each consequence is quantified. When decision tree analysis is done at
the same time as the clinical trial, the payoff may also be expressed as utilities. Utility can be
described in numerous ways. For example, as a percentage of full health. A value of 0.7 corresponds
to a person living at 70% of full health. Another way to express utility is quality adjusted life years
(QALY). Expected value of each therapy is calculated by multiplying the payoff (dollars, percent,
QALYS etc.) with the probability of occurrence for every possible transition state.
While decision trees are simple to comprehend, complicated real–world scenarios cannot be
adequately modeled with basic decision tree analysis. The tree cannot model repetitive events or
transitions back and forth between two states. To model repetitive events or transitions backward
would require numerous repetitive transition states. Trying to create a path for every possible
scenario can quickly lead to a complicated, unmanageable decision tree.
Another inherent limitation of decision tree analysis is its stagnant nature. Model conditions, such as
transition probabilities or costs, are not

Project Description Of A Mathematical Model
Project Description In many science and engineering applications, such as petroleum engineering,
aerospace engineer– ing and material sciences, inference based on a mathematical model and
available observations from the model has garnered importance in recent years. With the lack of the
analytical expres– sion, in most scenarios this solution involves numerical approximation. The
underlying system may contain unknown parameters which requires solving an inverse problem
based on the ob– served data. In many cases the underlying model may contain high dimensional
field which varies in multiple scales such as composite material, porous media etc. This high
dimensional solution can become computationally taxing even with the recent advent of ... Show
more content on Helpwriting.net ...
For example, in petroleum engineering the reservoir permeability may be unknown. From oil/water
pressure data from different well locations estimating the unknown κ is an inverse problem. 1
Figure 1: Left hand panel shows one dimensional basis at coarse level of discretization at grid
points 1,2,3,.... Basis corresponding grid point 2, φ2 is supported in [1,3] interval and zero otherwise
and linear in [1,2] and [2,3]. Right hand panel shows typical multiscale basis at two dimension,
which takes non zero value on coarse neighborhood of some coarse grid points but has high
resolution by solving a local problem. The solution u, the parameter κ can have oscillatory nature
(both in temporal and spatial scale) with multiple scales/periods. A numerical solution that captures
the local property of this solution requires capturing the local structure which involves solving a
homogenous version of (1) locally and use these solutions as basis to capture the global solution,
which is known as multiscale solution (Fish et al., 2012; Franca et al., 2005). A highly oscillatory
κ(x, t) = κ(x) is given for a two dimensional domain in Figure 2 . In numerical solution, the domain
is split into many small grids and basis corresponding to each grid, also known as fine scale basis,
can capture the oscillatory solution (see Figure 1). The linear pde system can be reduced into

Summer Training Report : Data Pre Processing Techniques Essay
Summer Training Report "Data Pre–processing Techniques" Under Supervision of : Mr. Soumitra
Bose Ideal Analytics Solutions Pvt. Ltd. Kolkata May – July 2015 Submitted By: Manan Mishra
B.Tech. and M.Tech. in Electrical Engineering with specialization in Power Electronics Enrollment
No. 12212004 1. Introduction Analysis of data is a process of inspecting, cleaning, transforming,
and modeling data with the objective of finding useful information, advising conclusions, and
supporting decision–making. Data analysis has multiple aspects and approaches, covering various
techniques under a lot of names, in different fields such as business, science, and social science.
Data gathering methods are often loosely controlled, resulting in out–of–range values (e.g., Income:
–100), impossible data combinations (e.g., Gender: Male, Pregnant: Yes), missing values, etc.
Analyzing data that has not been carefully screened for such problems can produce misleading
results. Thus, the representation and quality of data is first and foremost before running an analysis.
If there is much irrelevant and redundant information present or noisy and unreliable data, then
knowledge discovery during the training phase is more difficult. Data preparation and filtering steps
can take considerable amount of processing time. Data pre–processing includes cleaning,
normalization, transformation, feature extraction and selection, etc. The product of data pre–
processing is the final training

Nike's Long Term Financial Goals
How important is it for the financial managers of Nike Inc. to use economic variables in identifying
long term financial goals? For Nike's business model to continually flourish and stay profitable, the
senior management team and strategic planners must continually monitor short, intermediate and
long–term economic factors that will affect their operations. Nike's business model is heavily
dependent on supply chains, as the majority of their products are manufactured in Asian nations,
either in their own manufacturing centers or contract manufacturing partners. Sales forecasts for
next–generation shoes, apparel and sporting equipment must be accurate to ensure the supply chain
estimates and forecasts can meet product demand. The influence of economic factors on sales and
marketing planning and strategy development is among the most immediate and significant for any
enterprise operating in global markets (Cerullo, Avila, 1975). Strategic planners at Nike, working in
conjunction with product development and product launch teams, must understand the price
elasticity of demand for a given new product or an entirely new division before launching it.
Economic data gives Nike senior management and strategic planners the insight necessary to
determine which new products to launch or not, when, and in which specific regions of the world.
Economic variables will in short tell Nike's senior management how to navigate risk and capitalize
on opportunities as quickly as possible.

Unsupervised Transcription Of Piano Music
Unsupervised Transcription of Piano Music
MS Technical Paper
Fei Xiang
Mar.14, 2015
1. Motivation
Audio signal processing has been a very active research area. Automatic piano music transcription,
of all the tasks in this area, is an especially interesting and challenging one. There are many
examples of how this technique can contribute to our life. For instance, in today's music lessons and
tests, we often rely on people's hearing ability to judge whether a piano player performed well based
on whether the notes played are accurate or not. The process requires man–power and is not always
fair and accurate because people's judgement is subjective. If a good automatic transcription system
can be designed and implemented with high ... Show more content on Helpwriting.net ...
To tackle this problem, source–separation techniques must be utilized.
2. Existing Approaches
In this section, we will discuss what has been done in this area of unsupervised music transcription.
Undoubtedly there are different aspects to this task. And different ways and techniques are used in
attempt to solve this problem efficiently and accurately. In an effort to provide a clear picture of
what has been done, we will categorize different approaches based on technique used.
The classic starting point for the problem of unsupervised piano transcription where the test
instrument is not seen during training, is a non–negative factorization of the acoustic signal's
spectrogram [1]. Most research work has been improving on this baseline in the one of the following
two ways: better modeling of the discrete musical structure of the piece being transcribed [2,3] or by
better adapting to the timbral properties of the source instrument [4,5].
Combining the above two approaches are difficult. Hidden Markov or semi–Markov models are
widely used as the standard approach to model discrete musical structures. This approach needs fast
dynamic programming for inference. To combine discrete models with timbral adaption and source
separation, it would break the conditional independence assumptions that dynamic programming
rely on. Previous research work to avoid this inference problem typically postpones detailed
modeling the discrete structure of timbre

Models From Simple Random And Complex Survey Designs
Models (GLM) to data from simple random and complex survey designs. This program supports a
range of sampling distributions including Gaussian, inverse Gaussian, multinomial, binomial,
negative binomial, Bernoulli, Poisson, and gamma. The last program, MAPGLIM can be used to fit
GLM models to the multilevel data. Each of these programs can function as stand–alone program.
To estimate a model with missing data, LISREL by default uses the full information maximum
likelihood (FIML) approach. However, users may also opt to impute missing values using either
expectation maximization or Markov Chain Monte Carlo algorithms. Beginning with version 9.10,
LISREL will automatically provide robust estimation of standard errors and chi–square statics if a
raw file is used as a data input. The default estimation method in LISREL is Maximum Likelihood
(ML) even if an estimated asymptotic covariance matrix is provided, but the users may override the
default when setting up the model.
Together, the LISREL 9.2 package makes possible estimations of a wide range of statistical used in
educational research, such as exploratory– and confirmatory factor analysis (EFA and CFA) with
continuous and ordinal variables, multiple–group analysis, multilevel linear and nonlinear models,
latent growth curve models, and generalized linear models (GLM) to complex survey data and
simple random sample data (Byrne 2012; Sörbom 2001).
LISREL interface
The data analysis workflow in LISREL involves three most

Advantages And Disadvantages Of Density Forecasts
The density forecast of a random variable is an estimation based on the past observed data. This is a
symmetric interval prediction which means that the outcomes will fall into an interval that is a band
of plus/minus a fixed times of standard errors. The estimation provides a probability distribution of
all possible future values of that variable. Over the past decades, the price density forecast has been
widely used to study microeconomic and financial issues. Forecasting the future development of the
economy is of great importance for proper government monetary decisions and individual risk
management. A good macroeconomic density forecast presents a subjective description of
inflationary pressure and other information related to economics. ... Show more content on
Helpwriting.net ...
Although the methods they first used have many drawbacks, these analyses still help governments in
understanding the macroeconomic environment and making adjustments to current monetary policy.
The oldest quarterly survey of macroeconomics forecasts in the US is the Survey of Professional
Forecasters (SPF) whose name changed to ASA–NBER survey later (Diebold, Tay and Wallis,

Wikipedia Content Analysis
Wikipedia is a free online encyclopedia that has the freedom of a user interface to edit almost all of
its contents. Currently, Wikipedia is considered to be one of the most popular website along with a
credit of being the most popular general reference work website (Ref.3&5 of web). It was launched
on January 15, 2001 by Jimmi Wales and Larry Sanger (Wiki ref). Though it was only composed of
articles written in English in its initial days, now it has included almost 292 languages which
happens to have similar versions which differs in article contents and editing practices.For example
Wikipedia has currently more than 5260000 English, 111000 Hindi, 1801000 French, 1306000
Italian, and a lot many (approximately 40 million in 250 languages) ... Show more content on
Helpwriting.net ...
This method significantly out performs more complex methods for the article quality assessment. In
brief, the word count discrimination rule says article with more or less than 2000 words are
classified as featured or non–featured respectively. This method yielded an accuracy of 0.96 for an
unbalanced corpus. However, the value of the accuracy limit was varied for different subject
articles. They were found to be less for biological sciences and more for history. A study by Stvilia
measures information quality dynamics at both macro and micro levels (ref). They have postulated
seven IQ matrices that can easily be tested on a representative Wikipedia content. They further
added statistical characterization, content construction, process metadata and social context of
Wikipedia articles. The parameters include authorocity/reputation, completeness, complexities,
informativeness, consistencies, currency and

Quantum Chromodynamics : The Theory Of The Strong Reaction...
The theory of the strong interaction force –– Quantum Chromodynamics (QCD) –– predicts that at
sufficient high temperature and/or baryon density, nuclear matter undergoes a phase transition from
hadrons to a new state of the deconfined quarks and gluons: the quark gluon plasma
(QGP)~cite{Bjorken:1982qr}. Over the past two decades, ultra–relativistic heavy–ion collision
experiments at the Relativistic Heavy Ion Collider (RHIC) and the Large Hadron Collider (LHC)
have been searching and exploring this new state of matter under extreme conditions. Compelling
discoveries, for instance the strong suppression of hadrons at large transverse momenta (jet
quenching), reveal the creation of the QGP medium at RHIC and the LHC~cite{Teaney:2000cw}.
... Show more content on Helpwriting.net ...
In current studies of the open heavy flavor diffusion coefficient, it is common that the diffusion
coefficient is directly or indirectly encoded in the model and one can relate its physical properties to
one or multiple parameters. By comparing the heavy quark observables (such as the nuclear
modification factor $R_{mathrm{AA}}$ and elliptic flow $v_2$) between the theoretical
calculation and the experimental data, these parameters can be tuned until one finds a satisfactory
fit. However, the disadvantage of such an "eyeball" comparison is that it gets exceedingly difficult to
vary multiple parameters simultaneously or to compare with a larger selection of experimental
measurements, as all parameters are interdependent and affect multiple observables at once.
%~cite{Andronic:2015wma}.
A more rigorous and complete approach to optimizing the model and determining the parameters
would be to perform a random walk in the parameter space and calibrate to the experimental data by
applying a modern Bayesian statistical analysis~cite{Higdon:2014tva,Higdon:2008cmc}. In such
an analysis, the computationally expensive physics model is first evaluated for a small number of
points in parameter space. These calculations are used to train

Capstone Project
The
Student
Guide to the MSA
Capstone
Project
Part 1: The Research
Proposal and the
Research Project
Central Michigan University
August 2012
Contents
What is the MSA 699 Project? ........................................................................................................ 4
Overview of the MSA 699 Project................................................................................................... 5
Plagiarism and Ethics ...................................................................................................................... 7
The Research Proposal .................................................................................................................... 8
Chapter 1: Definition of ... Show more content on Helpwriting.net ...
43
Sample Table of Contents ...................................................................................................................
44
Executive Summary Helps ...................................................................................................................
45
APA 6th Edition Helps...........................................................................................................................
47
THE STUDENT GUIDE TO THE MSA CAPSTONE PROJECT, Part 1
August 2012
Page 3
WELCOME TO THE MSA 699 PROJECT
MSA 699 is designed as the culminating activity in the Master of Science in Administration degree
program of Central Michigan University. Unlike most courses you have taken, MSA 699 will be
completed on an individual basis. 24 hours will be taken in a classroom setting. Much of the
planning, organizing, research, analysis, and writing will be done independently in close association

with the MSA 699 monitor. The MSA 699 monitor is the instructor of your course.
This guide has been prepared to provide you with assistance in a readily accessible form; use it for
specific guidance as you undertake your MSA 699 project. Important note: Do not assume that your
MSA 600 research proposal will be the basis of your MSA 699 project. The
MSA 600 research proposal is intended to familiarize you with the parts of the

Genetic Cluster Number Of Genetic Clusters
2.5 Number of genetic clusters
To infer genetic cluster number (K) in our sample set, we used two Bayesian approaches based on
the clustering method which differed in that they: a) incorporate or not a null allele model, and b)
use a non–spatial or spatial algorithm. We selected this approach because Bayesian models capture
genetic population structure by describing the genetic variation in each population using a separate
joint posterior probability distribution over loci. First, we used STRUCTURE v.2.3.3 (Falush et al.,
2003; Pritchard et al., 2000), which does not incorporate a null allele model, but uses a non–spatial
model based on a clustering method and it is able to quantify the individual genome proportion from
each inferred population. A previous run had been carried out to define what ancestry models (i.e. no
admixture model and admixture model) and allele frequency models (i.e. correlated and
uncorrelated allele frequency models) fit our dataset. All these previous runs were conducted with
locality information prior to improving the detection of structure when this could be weak (Hubisz
et al., 2009). Run parameters of previous simulations included five runs with 50,000 iterations
following a burn–in period of 5,000 iterations for K = 1–10 as number of tested clusters. Before
choosing models to run our dataset we evaluated Evanno's index ΔK (Evanno et al., 2005), to
identify whether different models yielded different K values, implemented in STRUCTURE
HARVESTER

Essay On Flood Forecasting
SURVEY ON FLOOD FORECASTING METHODS
SANGEETHA.S1 JAYAKUMAR.D2 PG Scholar, Department of Computer Science & Engineering,
IFET college of Engineering, Villupuram.
Associate Professor, Department of Computer Science & Engineering, IFET college of Engineering,
Villupuram.
ABSTRACT
Artificial intelligent models (AIMs) have been successfully adopted in hydrological forecasting in a
plenty of literatures. However, the comprehensive comparison of their applicability in particular
short–term (i.e. hourly) water level prediction under heavy rainfall events was rarely discussed.
Therefore, in this study, the artificial neural networks (ANN), Intelligent multi agent approach,
Markov Chain Monte Carlo (MCMC) were selected for ... Show more content on Helpwriting.net ...
Flood warnings must be provided with an adequate lead time for the public and the emergency
services to take actions to minimize flood damages.
Real time flood forecasting is an important and integral part of a flood warning service, and can help
to provide more accurate and timely warnings. Depending on catchment characteristics and
catchment response to rainfall, various types of flood forecasting models, including correlations,
simple trigger flood forecasting, and more sophisticated real time catchment–wide integrated
hydrological and hydrodynamic models may be adopted. These models provide flow and level
forecasts at the selected key locations known as Forecast Points, which are usually located along
major rivers or on streams near urban areas that have a history of flooding.
2. ARTIFICIAL NEURAL NETWORK: ANN consists of a large number of parallel processing
neuros, working independently and connecting to each other by weighted links. It is capable of
simulating complex nonlinear system due to its ability of self–learning, self adaption and
generalization. The feed forward neural network (FFNN), with one input layer, one or more hidden
layer and one output layer, is employed in this study. BP algorithm, firstly introduced by Rumelhart,
is employed for training. The global error

Queueing Theory In Hospital Management

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