The BelEve application helps you to know what to believe about things concerning which you only have limited information. For example, water companies have lots of pipes under the ground but they can't dig them all up to discover their condition. Likewise, telephone companies have miles of cables of different types and ages and conditions that they would like to know as much as possible about. Similarly with roads and railtracks and bridges. The list of partially understood assets whose condition would be better known is endless.

BelEve takes the data you have and builds a statistical model, including sets of equations that relate base quantities to yield derived quantities of interest. As new data becomes available, the model is updated using linear Bayesian algorithms to yield a new model that has incorporated correlated modifications that thereby modify exisiting beliefs. In this way prior beliefs are influenced by new knowledge and the overall understanding is incrementally improved. This system was originally designed by world-renouned Bayesian statistician Tony O'Hagan (see ABLE) and includes a unique variance learning algorithm which incrementally increases the accuracy of detailed error handling.

BelEve is supplied as a .NET assembly in a DLL. This means that a programmer who knows how to make use of methods and properties available through a .NET assembly can prepare standard (large) files of data and then apply exact calculations, simulations and updates through BelEve methods, and obtain standard output files from the reporting module.

Space is an application that is designed to accomodate inherently multi-dimensional data in an inherently multi-dimensional manner. Excel is inherently 2-dimensional. Understanding 3 dimensions is not too hard either because we live in a 3-dimensional world and therefore we can easily imagine a stack of worksheets, but 4, 5 or 6 dimensions are hard to imagine without experience. Space allows up to 15 dimensions which very few applications have a need for, but 3 or 4 is very common.

Anyone who has prepared high-level reports for a large company will know that the number of combinations for displaying this against that can become very large. When the data structure is understood in terms of the basic underlying dimensions, and is fed into Space accordingly, then all the various reports of this against that fall out naturally. For example, consider the accounts of a large manufacturing company such as Apple Inc. You may have their product, the iPhone 6, made perhaps in Vietnam, in November 2016, costing $20 to manufacture, sold to you in the UK for £500. There you have a couple of numbers that fit into a 5 dimensional scheme of Product against Manufacturing Country against Date against Sales Country against Accounting Items, with the possibility of a 6th Currency dimension. Apple UK might then want a report of total of all sales of all products made in Vietnam in 2016. That's what Space can do for you, easily.

Space is designed like a classic Microsoft Office application with File, Edit, View, etc.. menus. There are 5 modules accessible through tabs at the bottom of the screen. The first tab reveals the Coordinates - the dimension names and labels. The second tab reveals the Array - the multi-dimensional grid layout of data. The third tab allows Deductions - a line by line program of calculations in which the variables involved are picturial representations of the multi-dimensional data coordinate selection. Fourth is the View tab allowing a multi-dimensional representation of the n-dimensional data selection. The fifth Induction tab has not yet been fully designed and built but will allow data to be entered at a highly consolidated level and then distributed back down the inverse calculation tree to low level statistically plausible base data.

SEEK is an Associative Knowledge Base. Any circumstance requiring complex searches of information concerning pointers, indicators, abstracts, summaries, parts, items, or references forms a suitable potential application.

SEEK is intended for all sorts of applications. There are 3 main aspects. There are Records which contain arbitrary rich text, a picture element, and a list of keys. There are Keys which include key phrases and sets of .. sets of key phrases. And there are Selections which use logical operators and set theoretic operators to make detailed selections of records from the database.

Suitable lists of keys are created and put into sets, and sets of sets... Records and pictures are created by hand or by cut and paste, and appropriate keys are assigned to each record. (Automatic means of creating large numbers of records from external sources may be developed for individual requirements.)

There are two levels in the selection methodology. At the lower level, selections of records may be made using set theoretic operations on keys. These operations include ElementsOf(), SetsContaining(), Union, Intersection and Complement(). At the higher level, selections of records produced via key identification may be combined using logical operations of And, Or and Not(). Thus, for example, it is possible to display all records that satisfy the request
(ElementsOf(SetsContaining(blue zippers)))And Not(sports wear)