🪄SSRS (& Paginated Reports): Parameter Dependencies in Dropdowns (Fourth Magic Class)

Introduction

Quite often there are dependencies between the values of two distinct dropdowns, typically the values representing the different levels of a direct on implied hierarchy. This means that the choice at the upper level determines the values which will be shown at a lower level, the corresponding query needing to handle the value as a parameter. There are several ways on how one can handle this, the choice depending also on the data provider considered for the report.

Because the AdventureWorks database is used within an SQL Server, the available choices are MSOLEDBSQL, SQLOLEDB, ODBC or SQL Server Native Client. It should be noted that the SQLOLEDB and the Native Clients providers have been deprecated and shouldn't be used for new developments! In the project was used the Microsoft OLE DB Provider for SQL Server (MSOLEDBSQL) provider.

For exemplification will be used the reports build previously. The hierarchy will be built based on the State and Territory, a Country belonging to a Territory, while a State to a Country. For the beginning we will build the dependency existing between the Country and State. 

First Level of Dependency 

The text parameter for StateCountry dataset will be defined similarly to the one consider for the Country, and have as source the following query:

-- State Provinces
SELECT '' Id
, '(All)' Name
UNION ALL
SELECT StateProvinceCode
, StateProvinceCode + ' ' + Name
FROM [Person].[StateProvince]
WHERE CountryRegionCode = ISNULL(NullIf(?, ''), CountryRegionCode)
ORDER BY Id

The Country parameter will be handled in the WHERE constraint - it's the role of the question mark. When an empty string was selected, the equivalent for ignoring the parameter, the formula used will result to an identity, which however makes sense only if there's always a value for the Country Code in the Person.StateProvince table. Otherwise the same line needs to be written as follows:

WHERE IsNull(CountryRegionCode, '') = COALESCE(NullIf(?, ''), CountryRegionCode, '')

This might look strange, though it does the trick of handling the nulls. According to the rules of defensive programming, one should treat the eventuality of nulls. However, this depends also on application's design. If one develops reports with this supposition, then the supposition should be made explicit in the documentation!

Once the query provided, SSRS recognizes that a parameter was provided and requires to map the question mark to the respective parameter. Within dataset's properties click on the "Parameter" tab and selected @Country as Parameter Value:

Parameters in Dataset Properties

A question mark needs to be considered in the query for each parameter, the parameters from the Dataset Properties being displayed in the order in which they appear in the query.

Once the dataset defined, one can go on and define the StateProvince parameter similarly to the Country parameter and consider the StateProvinces dataset as source. The StateProvince parameter needs to be added also in report's header:
= "State Province: " & Parameters!StateProvince.Label

Within the Addresses main dataset there needs to be added the declaration for the StateProvince parameter, while in where the corresponding clause:

-- SQL Parameter
& " DECLARE @StateProvince as nvarchar(3) = '" & Parameters!StateProvince.Value & "'" & vbcrlf 

-- constraint in WHERE clause
& IIf(Parameters!StateProvince.Value<> "", "     AND SIC.StateProvinceCode = @StateProvince", "") & vbcrlf W

With these changes the report should be ready to run.

Notes:
The above query for the StateProvince dataset can be used inside of a formula with a few small changes. This should work independently of the provider used. Just replace the query in the formula and delete the parameters previously created for the data source via the Dataset Properties:

= " -- State Provinces " & vbCrLf
 & " DECLARE @Country as nvarchar(3) = '" & Parameters!Country.Value & "'" & vbcrlf 
 & " SELECT '' Id " & vbCrLf
 & " , '(All)' Name " & vbCrLf
 & " UNION ALL " & vbCrLf
 & " SELECT StateProvinceCode " & vbCrLf
 & " , StateProvinceCode + ' ' + Name " & vbCrLf
 & " FROM [Person].[StateProvince] " & vbCrLf
 & IIf(Parameters!Country.Value<>"", "     WHERE CountryRegionCode = @Country", "") & vbcrlf 
 & " ORDER BY Id " & vbCrLf

Second Level of Dependency

The second dependency is based on the Territory Group formed of North America, Pacific and Europe. There is however no Territory Group table, the relations between Countries and Territory Groups being stored into the Sales.SalesTerritory table. Therefore, the query for the corresponding dropdown can be written via a DISTINCT:

-- Territories
SELECT '' Id
, '(All)' Name
UNION ALL
SELECT DISTINCT [Group] Id
, [Group] Name
FROM Sales.SalesTerritory
ORDER BY Id

When the user selects a Territory, in the Countries and State Province dropdowns are supposed to be shown only the corresponding values for the selected value. We need to modify thus both queries via formulas.
The query for the Country dataset includes the Territory parameter and the corresponding constraint via an EXIST:

= "  -- Countries  " & vbCrLf 
& " DECLARE @Territory as nvarchar(50) = '" & Parameters!Territory.Value & "'" & vbcrlf 
& " SELECT '' Id  " & vbCrLf 
& " , '(All)' Name  " & vbCrLf 
& " UNION ALL  " & vbCrLf 
& " SELECT SSP.CountryRegionCode Id  " & vbCrLf 
& " , SSP.Name   " & vbCrLf 
& " FROM [Person].[CountryRegion] SSP " & vbCrLf 
& IIf(Parameters!Territory.Value<> "", "     WHERE EXISTS (SELECT * FROM [Sales].[SalesTerritory] SST WHERE SSP.CountryRegionCode = SST.CountryRegionCode AND SST.[Group] = @Territory)", "") & vbcrlf 
& " ORDER BY Id " & vbCrLf

In exchange the StateProvince dataset is slightly more complex:

= " -- State Provinces " & vbCrLf
 & " DECLARE @Country as nvarchar(3) = '" & Parameters!Country.Value & "'" & vbcrlf 
 & " DECLARE @Territory as nvarchar(50) = '" & Parameters!Territory.Value & "'" & vbcrlf 
 & " SELECT '' Id " & vbCrLf
 & " , '(All)' Name " & vbCrLf
 & " UNION ALL " & vbCrLf
 & " SELECT PSP.StateProvinceCode " & vbCrLf
 & " , PSP.StateProvinceCode + ' ' + PSP.Name " & vbCrLf
 & " FROM [Person].[StateProvince] PSP" & vbCrLf
 & "      JOIN [Sales].[SalesTerritory] PST" & vbCrLf
 & "       ON PSP.TerritoryId = PST.TerritoryId " & vbCrLf
 & " WHERE 0=0 " & vbCrLf
 & IIf(Parameters!Country.Value<> "", "     AND PSP.CountryRegionCode = @Country", "") & vbcrlf 
 & IIf(Parameters!Territory.Value<> "", "     AND PST.[Group] = @Territory", "") & vbcrlf 
 & " ORDER BY Id " & vbCrLf

Once the datasets changed, is still needed to modify the order of the parameters to reflect the order in which they are called within dependency, respectively Territory, State Province, Country. In the end the report looks like this:

Notes:
(1) If the order of the parameters doesn't reflect the otder in which they are called, you'll get a generic error similar with the following one (the error appears when there are problems with the query):
An error occurred during local report processing. The definition of the report '/Customer Addresses' is invalid. The CommandText expression for the query 'Countries' contains an error: [BC30277] Type character '8e does not match declared data type 'String'.

(2) When designing parameters one should consider also the best practices for avoiding the poor design of parameters.

🪄SSRS (& Paginated Reports): Report Formatting (Third Magic Class)

Introduction

In the previous post were defined the parameters for the Customer Addresses report, however the design looks pretty basic as it was generated by the 'Reporting Wizard'. This can be slightly enhanced with a few changes. The order in which they are applied usually doesn't matter much.

Changing Columns' length

In Design mode drag of the columns to match the length of the attributes stored in them. This might require several tries.

Adding a Header/Footer

In Within reports body right click and from the floating menu choose 'Add Page Header', respectively 'Add Page Footer' like in the below screenshot: 

Adding the header/footer

Usually in the head I consider report's name as well two text boxes containing the date at which the report was added, respectively the number of pages the report displays. 

Cut report's header textbox from report's body into the header and move it to match the leftmost side of the report. In the header add two text boxes and provide the following formulas for the current date, respectively the number of pages: 

= Now().Date().ToString("dd.MM.yyyy"))

= Globals!PageNumber & " from " & Globals!TotalPages

Then drag of controls to match the dimensions of the blow grid like in the screen below:

Header

Note:

Then It's important to align the various controls so their margins match together, otherwise will be added empty columns or rows in the Excel exports, users having to delete the empty cells manually each time they want to use the data.

Adding the Parameters:

Even if the parameters are displayed within browser, as soon the data are exported to Excel they are lost. Therefore, to identify with which values the report was used, it is useful to add the parameters in table's header. Add one or two lines above the header, and merge a few cells to accommodate the parameters as below:

The merged cells contain the following formulas:

= "Country: " & Parameters!Country.Label

= "Customer Name: " & Parameters!CustomerName.Value

= "Postal Code: " & Parameters!PostalCode.Value

= "Phone Number:" & Parameters!PhoneNumber.Value

= "Address Type: " & Parameters!AddressType.Label

= "Person Type: " & Parameters!PersonType.Label

Formatting Table

One can go with the available formatting, however table's header could be changed of 10 pt font size. One can add also a background color for the header, format the top and bottom border and align columns' name in the center. The background color for the rows with the parameters must be non-transparent (e.g. white) if the header is fixed.

As concerns the data rows, one can add different colors for alternate rows. For this select the row and as background color provide the following formula:

=IIf(RowNumber(Nothing) Mod 2 = 0, "White", "LightSteelBlue")

Freezing the Header

One can freeze the header during scrolling by setting the property FixedData='True' for each static row. For this you need to change to Advance mode and set FixedData  property for the first three static rows as follows:

Thus, when scrolling, the header will remain fix:

Probably the choice of collars is not the best, though it can be for example adapted to organizations' colors. One can add also a logo, the name of the user who run the report, or whatever information are still needed.

🪄SSRS (& Paginated Reports): Report Parameters (Second Magic Class)

Introduction

One of the advantages of SQL Server Reporting Services (SSRS) is that once  a query available, in just a few minutes one can display the data into a report. This post shows how to add parameters to a report. For exemplification is used the same view from the first post of the series.

As prerequisite for this post one needs to have ideally the Reporting Services Extension installed. 

Identifying the Parameters

The first step is deciding which parameters are required or make sense to add to the report. The parameters together with their type can be documented in a list similar to the one below:

Parameter list

SSRS differentiates between five types of parameters:  Text, Boolean, Date/Time, Integer and Float. 

Reviewing the Source Query

Before adding the parameters to the report is recommended to look at the source query to check whether it allows the efficient use of parameters. Based on the above list of parameters the standard view needs to be modified as follow (the new columns are marked with a 'new' comment):

-- Individual Customers view
ALTER VIEW [Sales].[vIndividualCustomer] 
AS 
SELECT p.[BusinessEntityID]
, p.PersonType PersonTypeId--new
, CASE p.PersonType 
	WHEN 'SC' THEN 'Store Contact'
    WHEN 'IN' THEN 'Individual (retail) customer'
    WHEN 'SP' THEN 'Sales person'
    WHEN 'EM' THEN 'Employee (non-sales)'
    WHEN 'VC' THEN 'Vendor contact'
    WHEN 'GC' THEN 'General contact'
 END PersonType --new
, p.[Title]
, p.[FirstName]
, p.[MiddleName]
, p.[LastName]
, p.[Suffix]
, pp.[PhoneNumber]
, pnt.[Name] AS [PhoneNumberType]
, ea.[EmailAddress]
, p.[EmailPromotion]
, at.[AddressTypeID] --new
, at.[Name] AS [AddressType]
, a.[AddressLine1]
, a.[AddressLine2]
, a.[City]
, sp.StateProvinceCode --new
, [StateProvinceName] = sp.[Name]
, a.[PostalCode]
, sp.CountryRegionCode -- new
, [CountryRegionName] = cr.[Name]
, p.[Demographics]
FROM [Person].[Person] p
    INNER JOIN [Person].[BusinessEntityAddress] bea 
    ON bea.[BusinessEntityID] = p.[BusinessEntityID] 
    INNER JOIN [Person].[Address] a 
    ON a.[AddressID] = bea.[AddressID]
    INNER JOIN [Person].[StateProvince] sp 
    ON sp.[StateProvinceID] = a.[StateProvinceID]
    INNER JOIN [Person].[CountryRegion] cr 
    ON cr.[CountryRegionCode] = sp.[CountryRegionCode]
    INNER JOIN [Person].[AddressType] at 
    ON at.[AddressTypeID] = bea.[AddressTypeID]
	INNER JOIN [Sales].[Customer] c
	ON c.[PersonID] = p.[BusinessEntityID]
	LEFT OUTER JOIN [Person].[EmailAddress] ea
	ON ea.[BusinessEntityID] = p.[BusinessEntityID]
	LEFT OUTER JOIN [Person].[PersonPhone] pp
	ON pp.[BusinessEntityID] = p.[BusinessEntityID]
	LEFT OUTER JOIN [Person].[PhoneNumberType] pnt
	ON pnt.[PhoneNumberTypeID] = pp.[PhoneNumberTypeID]
WHERE c.StoreID IS NULL;

Preparing the Source Query

Based on this, the source query can be defined as follows:

-- Customer Addresses
SELECT SIC.Title   
, SIC.PersonType   
, SIC.FirstName   
, SIC.LastName   
, SIC.AddressType   
, SIC.AddressLine1   
, SIC.City   
, SIC.PostalCode   
, SIC.CountryRegionName   
, SIC.PhoneNumber   
, SIC.EmailAddress   
FROM Sales.vIndividualCustomer SIC 
WHERE CountryRegionName = 'Germany'
  AND PostalCode = '42651'
ORDER BY SIC.FirstName  
, SIC.LastName   
, SIC.AddressLine1   

Note:
When preparing a source query for your report is important to limit the number of the records returned to reduce the waiting time until the data are displayed and minimize the traffic to the database. 

Generating the Report

Creating a report is straightforward in SSRS because the 'Report Wizard' takes care of everything in the background. 

Generated report with the Report Wizard

  After testing the report, right click on the created dataset (Dataset1) in design mode and change its name to be meaningful (e.g. Addresses).

Dataset properties

   In this step I prefer transforming the query into a formula because it allows more flexibility in handling the parameters dynamically. Click on the formula and replace the query with the following text: 

 = " -- Customer Addresses " & vbCrLf
 & " SELECT SIC.Title    " & vbCrLf
 & " , SIC.PersonType    " & vbCrLf
 & " , SIC.FirstName    " & vbCrLf
 & " , SIC.LastName    " & vbCrLf
 & " , SIC.AddressType    " & vbCrLf
 & " , SIC.AddressLine1    " & vbCrLf
 & " , SIC.City    " & vbCrLf
 & " , SIC.PostalCode    " & vbCrLf
 & " , SIC.CountryRegionName    " & vbCrLf
 & " , SIC.PhoneNumber    " & vbCrLf
 & " , SIC.EmailAddress    " & vbCrLf
 & " FROM Sales.vIndividualCustomer SIC  " & vbCrLf
 & " WHERE CountryRegionName = 'Germany' " & vbCrLf
 & "   AND PostalCode = '42651' " & vbCrLf
 & " ORDER BY SIC.FirstName   " & vbCrLf
 & " , SIC.LastName    " & vbCrLf
 & " , SIC.AddressLine1 " & vbCrLf

This can be obtained pretty easy by using a formula in Excel like below:

Preparing the dynamic query statement

Formula: = "& "" " & A1 & " "" & vbCrLf "

Note:
In previous versions of reporting environment there must be an empty space after each vbCrLf except the last line, otherwise an error will be shown and the report can't be used at all. When the empty space misses is tricky to find the line where this happened. To validate this one can put the mouse at the end of each line and see whether it lands after the vbCrLf or after an empty space.

Preparing the Queries for Dropdowns

Before adding the parameters it might be a good idea to prepare the queries for the dropdowns. For this let's consider the simplest parameters: 

-- Address Types
SELECT 0 Id
, '(All)' Name
UNION ALL
SELECT AddressTypeID
, Name
FROM [Person].[AddressType]
ORDER BY Id

The query has two parts, one made from the database query to which is added a row marking the '(All)' scenario in which the parameter is actually ignored, and thus all the records for this parameter will be shown. This is typically the default value. 

SSRS has another solution for showing all the values, though I prefer this approach. 

In all the dropdown queries I prefer to use the same names ('Id', respectively 'Name'), independently on how the attributes are called, as they make the purpose explicit. Because the data type of the parameter is integer and is a reference key, the default value can be 0 or also -1 if 0 values are available in the dataset. 

For Text parameters one can use the empty string as parameter, like in the below example for Country Codes: 

-- Countries
SELECT '' Id
, '(All)' Name
UNION ALL
SELECT CountryRegionCode Id
, Name 
FROM [Person].[CountryRegion]
ORDER BY Id

Adding the Parameters

To add a Parameter right click on 'Parameters' within the 'Report Data' and then klick on 'Add Parameter'. This will open the 'Report Parameter Properties' which by default looks like this:

Report Parameter Properties

Each parameter type requires different settings to be considered. 

Defining Freetext parameters

Customer Name, Postal Code and Phone Number are Text parameters shown in Freetext controls and all three are setup similarly (see Parameter List above): 

General settings for text parameters

Defining Dropdowns with Fix Values

The General setting for the Person Type are similar with the ones considered for Freetext, however it has a set of predefined values which can be added in the Available Values as follows: 

Available Values

The default value can be provided into the 'Default Values' section. Just click on 'Add' and enter an empty space:

Default Values for fix dropdown

Defining Dropdowns with Dynamics Values

Country parameter is defined similarly, however as Available Values is selected the 'Get values from a query' option. The Countries dataset is selected as source while 'Id' and 'Name' are provided as below: 

Available Values for dynamic dropdown

Same can be done also for the AddressType parameter having the AddressTypes datasource as source. 

After adding all the parameters and choosing the order in which they should be displayed, the Report Data section will look like this:

Report's parameters

Note:

When designing parameters one should consider also the best practices for avoiding the poor design of parameters.

Adding the Parameters to the Query

For this the query needs to be added as follows: 

= "-- Customer Addresses " & vbCrLf
 & " DECLARE @Country as nvarchar(3) = '" & Parameters!Country.Value & "'" & vbcrlf 
 & " DECLARE @CustomerName as nvarchar(100) = '" & Parameters!CustomerName.Value & "'" & vbcrlf 
 & " DECLARE @PostalCode as nvarchar(15) = '" & Parameters!PostalCode.Value & "'" & vbcrlf 
 & " DECLARE @PhoneNumber as nvarchar(25) = '" & Parameters!PhoneNumber.Value & "'" & vbcrlf 
 & " DECLARE @AddressType as int = " & Parameters!AddressType.Value & vbcrlf 
 & " DECLARE @PersonType as nvarchar(3) = '" & Parameters!PersonType.Value & "'" & vbcrlf 
 & " SELECT SIC.Title   " & vbCrLf
 & ", SIC.PersonType   " & vbCrLf
 & ", SIC.FirstName   " & vbCrLf
 & ", SIC.LastName   " & vbCrLf
 & ", SIC.AddressType   " & vbCrLf
 & ", SIC.AddressLine1   " & vbCrLf
 & ", SIC.City   " & vbCrLf
 & ", SIC.PostalCode   " & vbCrLf
 & ", SIC.CountryRegionName   " & vbCrLf
 & ", SIC.PhoneNumber   " & vbCrLf
 & ", SIC.EmailAddress   " & vbCrLf
 & " FROM Sales.vIndividualCustomer SIC " & vbCrLf
 & " WHERE 0=0 " & vbCrLf
 & IIf(Parameters!Country.Value<> "", " 		  AND SIC.CountryRegionCode = @Country", "") & vbcrlf 
 & IIf(Parameters!CustomerName.Value<> "", " 		  AND (SIC.FirstName  LIKE '%' + @CustomerName + '%' OR SIC.LastName  LIKE '%' + @CustomerName + '%')", "") & vbcrlf 
 & IIf(Parameters!PostalCode.Value<> "", " 		  AND SIC.PostalCode = @PostalCode", "") & vbcrlf 
 & IIf(Parameters!PhoneNumber.Value<> "", " 		  AND SIC.PhoneNumber LIKE '%' + @PhoneNumber + '%'", "") & vbcrlf
 & IIf(Parameters!PersonType.Value<> "", " 		  AND SIC.PersonTypeId = @PersonType", "") & vbcrlf
 & IIf(Parameters!AddressType.Value<> 0, " 		  AND SIC.AddressTypeId = @AddressType", "") & vbcrlf 
 & " ORDER BY SIC.FirstName  " & vbCrLf
 & ", SIC.LastName   " & vbCrLf
 & ", SIC.AddressLine1   " & vbCrLf

For each report parameter is defined a SQL parameter in the header with the appropriate data type and length, and a constraint within the WHERE clause. The parameter will be considered only if a valid value was provided. One has to take into consideration the type of the parameter. 

Testing the Report

If everything worked correctly the report will run without problems:

Testing the report

For testing the report it makes sense to choose the parameters one by one from the general to the more particular case. For example for a first test I would choose 'Germany' as Country, then in a second run i would choose one of the values from the resulting dataset, e.g. 'Arthur' as Customer Name, while in a thirst run I would pick a value for the Postal Code. This approach allows minimizing the volume of time required for testing. 

Troubleshooting

Errors in formulas are sometimes difficult to troubleshoot especially when the report refuses to run. The SQL errors are easier to troubleshoot into Management Studio (SSMS). Just copy the query in SSMS and replace the added formatting.

Errors into the formula require unfortunately more time, however most of the errors I had were related to the missing empty space, or forgetting to close a quote or incorrect formatting.

🔭Data Science: Parameters (Just the Quotes)

"The essential feature is that we express ignorance of whether the new parameter is needed by taking half the prior probability for it as concentrated in the value indicated by the null hypothesis and distributing the other half over the range possible." (Harold Jeffreys, "Theory of Probablitity", 1939)

"The general method involved may be very simply stated. In cases where the equilibrium values of our variables can be regarded as the solutions of an extremum (maximum or minimum) problem, it is often possible regardless of the number of variables involved to determine unambiguously the qualitative behavior of our solution values in respect to changes of parameters." (Paul Samuelson, "Foundations of Economic Analysis", 1947)

"A primary goal of any learning model is to predict correctly the learning curve - proportions of correct responses versus trials. Almost any sensible model with two or three free parameters, however, can closely fit the curve, and so other criteria must be invoked when one is comparing several models." (Robert R Bush & Frederick Mosteller, "A Comparison of Eight Models?", Studies in Mathematical Learning Theory, 1959)

"A satisfactory prediction of the sequential properties of learning data from a single experiment is by no means a final test of a model. Numerous other criteria - and some more demanding - can be specified. For example, a model with specific numerical parameter values should be invariant to changes in independent variables that explicitly enter in the model." (Robert R Bush & Frederick Mosteller,"A Comparison of Eight Models?", Studies in Mathematical Learning Theory, 1959)

"The usefulness of the models in constructing a testable theory of the process is severely limited by the quickly increasing number of parameters which must be estimated in order to compare the predictions of the models with empirical results" (Anatol Rapoport, "Prisoner's Dilemma: A study in conflict and cooperation", 1965)

"Since all models are wrong the scientist cannot obtain a ‘correct’ one by excessive elaboration. On the contrary following William of Occam he should seek an economical description of natural phenomena. Just as the ability to devise simple but evocative models is the signature of the great scientist so overelaboration and overparameterization is often the mark of mediocrity." (George Box, "Science and Statistics", Journal of the American Statistical Association 71, 1976)

"Mathematical model making is an art. If the model is too small, a great deal of analysis and numerical solution can be done, but the results, in general, can be meaningless. If the model is too large, neither analysis nor numerical solution can be carried out, the interpretation of the results is in any case very difficult, and there is great difficulty in obtaining the numerical values of the parameters needed for numerical results." (Richard E Bellman, "Eye of the Hurricane: An Autobiography", 1984)

"A mechanistic model has the following advantages: 1. It contributes to our scientific understanding of the phenomenon under study. 2. It usually provides a better basis for extrapolation (at least to conditions worthy of further experimental investigation if not through the entire range of all input variables). 3. It tends to be parsimonious (i.e, frugal) in the use of parameters and to provide better estimates of the response." (George E P Box, "Empirical Model-Building and Response Surfaces", 1987)

"Whenever parameters can be quantified, it is usually desirable to do so." (Norman R Augustine, "Augustine's Laws", 1987)

"In addition to dimensionality requirements, chaos can occur only in nonlinear situations. In multidimensional settings, this means that at least one term in one equation must be nonlinear while also involving several of the variables. With all linear models, solutions can be expressed as combinations of regular and linear periodic processes, but nonlinearities in a model allow for instabilities in such periodic solutions within certain value ranges for some of the parameters." (Courtney Brown, "Chaos and Catastrophe Theories", 1995)

"Bayesian inference is appealing when prior information is available since Bayes’ theorem is a natural way to combine prior information with data. Some people find Bayesian inference psychologically appealing because it allows us to make probability statements about parameters. […] In parametric models, with large samples, Bayesian and frequentist methods give approximately the same inferences. In general, they need not agree." (Larry A Wasserman, "All of Statistics: A concise course in statistical inference", 2004)

"The Bayesian approach is based on the following postulates: (B1) Probability describes degree of belief, not limiting frequency. As such, we can make probability statements about lots of things, not just data which are subject to random variation. […] (B2) We can make probability statements about parameters, even though they are fixed constants. (B3) We make inferences about a parameter θ by producing a probability distribution for θ. Inferences, such as point estimates and interval estimates, may then be extracted from this distribution." (Larry A Wasserman, "All of Statistics: A concise course in statistical inference", 2004)

"The important thing is to understand that frequentist and Bayesian methods are answering different questions. To combine prior beliefs with data in a principled way, use Bayesian inference. To construct procedures with guaranteed long run performance, such as confidence intervals, use frequentist methods. Generally, Bayesian methods run into problems when the parameter space is high dimensional." (Larry A Wasserman, "All of Statistics: A concise course in statistical inference", 2004)

"Each fuzzy set is uniquely defined by a membership function. […] There are two approaches to determining a membership function. The first approach is to use the knowledge of human experts. Because fuzzy sets are often used to formulate human knowledge, membership functions represent a part of human knowledge. Usually, this approach can only give a rough formula of the membership function and fine-tuning is required. The second approach is to use data collected from various sensors to determine the membership function. Specifically, we first specify the structure of membership function and then fine-tune the parameters of membership function based on the data." (Huaguang Zhang & Derong Liu, "Fuzzy Modeling and Fuzzy Control", 2006)

"It is also inevitable for any model or theory to have an uncertainty (a difference between model and reality). Such uncertainties apply both to the numerical parameters of the model and to the inadequacy of the model as well. Because it is much harder to get a grip on these types of uncertainties, they are disregarded, usually." (Manfred Drosg, "Dealing with Uncertainties: A Guide to Error Analysis", 2007)

"Traditional statistics is strong in devising ways of describing data and inferring distributional parameters from sample. Causal inference requires two additional ingredients: a science-friendly language for articulating causal knowledge, and a mathematical machinery for processing that knowledge, combining it with data and drawing new causal conclusions about a phenomenon." (Judea Pearl, "Causal inference in statistics: An overview", Statistics Surveys 3, 2009)

"In negative feedback regulation the organism has set points to which different parameters (temperature, volume, pressure, etc.) have to be adapted to maintain the normal state and stability of the body. The momentary value refers to the values at the time the parameters have been measured. When a parameter changes it has to be turned back to its set point. Oscillations are characteristic to negative feedback regulation […]" (Gaspar Banfalvi, "Homeostasis - Tumor – Metastasis", 2014)

"Today we routinely learn models with millions of parameters, enough to give each elephant in the world his own distinctive wiggle. It’s even been said that data mining means 'torturing the data until it confesses'." (Pedro Domingos, "The Master Algorithm", 2015)

"An estimate (the mathematical definition) is a number derived from observed values that is as close as we can get to the true parameter value. Useful estimators are those that are 'better' in some sense than any others." (David S Salsburg, "Errors, Blunders, and Lies: How to Tell the Difference", 2017)

"Estimators are functions of the observed values that can be used to estimate specific parameters. Good estimators are those that are consistent and have minimum variance. These properties are guaranteed if the estimator maximizes the likelihood of the observations." (David S Salsburg, "Errors, Blunders, and Lies: How to Tell the Difference", 2017)

"What properties should a good statistical estimator have? Since we are dealing with probability, we start with the probability that our estimate will be very close to the true value of the parameter. We want that probability to become greater and greater as we get more and more data. This property is called consistency. This is a statement about probability. It does not say that we are sure to get the right answer. It says that it is highly probable that we will be close to the right answer." (David S Salsburg, "Errors, Blunders, and Lies: How to Tell the Difference", 2017)

More quotes on "Parameters" the-web-of-knowledge.blogspot.com.

🕸Systems Engineering: Parameters (Just the Quotes)

"Clearly, if the state of the system is coupled to parameters of an environment and the state of the environment is made to modify parameters of the system, a learning process will occur. Such an arrangement will be called a Finite Learning Machine, since it has a definite capacity. It is, of course, an active learning mechanism which trades with its surroundings. Indeed it is the limit case of a self-organizing system which will appear in the network if the currency supply is generalized." (Gordon Pask, "The Natural History of Networks", 1960)

"Prediction of the future is possible only in systems that have stable parameters like celestial mechanics. The only reason why prediction is so successful in celestial mechanics is that the evolution of the solar system has ground to a halt in what is essentially a dynamic equilibrium with stable parameters. Evolutionary systems, however, by their very nature have unstable parameters. They are disequilibrium systems and in such systems our power of prediction, though not zero, is very limited because of the unpredictability of the parameters themselves. If, of course, it were possible to predict the change in the parameters, then there would be other parameters which were unchanged, but the search for ultimately stable parameters in evolutionary systems is futile, for they probably do not exist… Social systems have Heisenberg principles all over the place, for we cannot predict the future without changing it." (Kenneth E Boulding, Evolutionary Economics, 1981)

"A conceptual model is a qualitative description of the system and includes the processes taking place in the system, the parameters chosen to describe the processes, and the spatial and temporal scales of the processes." (A Avogadro & R C Ragaini, "Technologies for Environmental Cleanup", 1993)

"Fundamental to catastrophe theory is the idea of a bifurcation. A bifurcation is an event that occurs in the evolution of a dynamic system in which the characteristic behavior of the system is transformed. This occurs when an attractor in the system changes in response to change in the value of a parameter. A catastrophe is one type of bifurcation. The broader framework within which catastrophes are located is called dynamical bifurcation theory." (Courtney Brown, "Chaos and Catastrophe Theories", 1995)

"The dimensionality and nonlinearity requirements of chaos do not guarantee its appearance. At best, these conditions allow it to occur, and even then under limited conditions relating to particular parameter values. But this does not imply that chaos is rare in the real world. Indeed, discoveries are being made constantly of either the clearly identifiable or arguably persuasive appearance of chaos. Most of these discoveries are being made with regard to physical systems, but the lack of similar discoveries involving human behavior is almost certainly due to the still developing nature of nonlinear analyses in the social sciences rather than the absence of chaos in the human setting."  (Courtney Brown, "Chaos and Catastrophe Theories", 1995)

"Visualizations can be used to explore data, to confirm a hypothesis, or to manipulate a viewer. [...] In exploratory visualization the user does not necessarily know what he is looking for. This creates a dynamic scenario in which interaction is critical. [...] In a confirmatory visualization, the user has a hypothesis that needs to be tested. This scenario is more stable and predictable. System parameters are often predetermined." (Usama Fayyad et al, "Information Visualization in Data Mining and Knowledge Discovery", 2002)

"The existence of equilibria or steady periodic solutions is not sufficient to determine if a system will actually behave that way. The stability of these solutions must also be checked. As parameters are changed, a stable motion can become unstable and new solutions may appear. The study of the changes in the dynamic behavior of systems as parameters are varied is the subject of bifurcation theory. Values of the parameters at which the qualitative or topological nature of the motion changes are known as critical or bifurcation values." (Francis C Moona, "Nonlinear Dynamics", 2003)

"The methodology of feedback design is borrowed from cybernetics (control theory). It is based upon methods of controlled system model’s building, methods of system states and parameters estimation (identification), and methods of feedback synthesis. The models of controlled system used in cybernetics differ from conventional models of physics and mechanics in that they have explicitly specified inputs and outputs. Unlike conventional physics results, often formulated as conservation laws, the results of cybernetical physics are formulated in the form of transformation laws, establishing the possibilities and limits of changing properties of a physical system by means of control." (Alexander L Fradkov, "Cybernetical Physics: From Control of Chaos to Quantum Control", 2007)

"Generally, these programs fall within the techniques of reinforcement learning and the majority use an algorithm of temporal difference learning. In essence, this computer learning paradigm approximates the future state of the system as a function of the present state. To reach that future state, it uses a neural network that changes the weight of its parameters as it learns." (Diego Rasskin-Gutman, "Chess Metaphors: Artificial Intelligence and the Human Mind", 2009)

"Principle of Equifinality: If a steady state is reached in an open system, it is independent of the initial conditions, and determined only by the system parameters, i.e. rates of reaction and transport." (Kevin Adams & Charles Keating, "Systems of systems engineering", 2012)

"One kind of probability - classic probability - is based on the idea of symmetry and equal likelihood […] In the classic case, we know the parameters of the system and thus can calculate the probabilities for the events each system will generate. […] A second kind of probability arises because in daily life we often want to know something about the likelihood of other events occurring […]. In this second case, we need to estimate the parameters of the system because we don’t know what those parameters are. […] A third kind of probability differs from these first two because it’s not obtained from an experiment or a replicable event - rather, it expresses an opinion or degree of belief about how likely a particular event is to occur. This is called subjective probability […]." (Daniel J Levitin, "Weaponized Lies", 2017)