The Bayesian Occupancy Filter (BOF) is a discretized grid structure based bayesian algorithm, in which the environment is subdivised in cells to which random variables are linked. These random variables represent the state of occupancy and the motion field of the scene, without any notion of object detection and tracking, making the updating part of the filter an evaluation of the distribution of these variables, according to the new data acquisition. In the classic representation of the BOF, the motion field of each cell is represented as a neighborhood grid, the probability of the cell moving from the current one to another of the neighborhood being stocked in an histogram. If this representation is convenient for the update, since the potential antecedents of any cell is exactly determined by the structure, and so the propagation model is easily parallelizable, it also raises determinant issues :

Considering those limitations, a new way to represent the motion field has been developped. To do so, a new formulation of the BOF has been elaborated. This new version allow to introduce in the filter itself a distinction between static and dynamic parts, and so adapt the computation power. The main idea of this new representation is to mix two forms of sampling : a uniform one, represented as a grid, for the static objects and the empty areas, and a non uniform one, based on particles drawn from dynamic regions. The motion field in a cell will be represented as a set of samples from the distribution for values which are not null, and a weight given to the static hypothesis. The use of a set of samples to represent the motion field leads to a important decrease of the needed memory space, as well as the classification between dynamic objects and static objects or free areas. In the updating process, the antecedent of a cell can be either from the static configuration or from the dynamic configuration, which are both way easier to project in the new reference frame of the moving grid. The first results are stimulating, in term of occupancy evaluation and mostly in term of velocity prediction, being way more accurate and responsive than the older version. Those improvements will soon be presented in detail in upcoming papers, one being currently in the process of redaction.

In the current work, we have been developing a general framework for tracking multiple targets from lidar data.

In the past decades, multiple target tracking has been an active research topic. When object observations are known, object tracking becomes a data association (DA) problem. Among popular DA methods, multiple hypothesis tracking (MHT) is widely used. MHT is a multi-frame tracking method that is capable of handling ambiguities in data association by propagating hypotheses until they can be solved when enough observations are collected. The main disadvantage of MHT is its computational complexity since the number of hypotheses grows exponentially over time. The joint probabilistic data association (JPDA) filter is more efficient but prone to make erroneous decision since only single frame is considered and the association made in the past is not reversible. Other sequential approaches using particle filters share the same weakness that they cannot reverse time back when ambiguities exist. All DA approaches mentioned above requires a strong assumption of one-to-one mapping between targets and observations which is usually violated in real environments. For instance, a single object can be seen by several observations due to occlusion, or multiple moving objects can be merged into a single observation when moving closely.

In the research, we propose a new data association approach that deals with split/merge nature of object observations. In addition, our approach also tackles ambiguities by taking into account a sequence of observations in a sliding window of frames. To avoid the high computational complexity, a very efficient Markov Chain Monte Carlo (MCMC) technique is proposed to sample and search for the optimum solution in the spatio-temporal solution space. Moreover, various aspects including prior information, object model, motion model and measurement model are explicitly integrated in a theoretically sound framework.

We have developped an object class recognition method. The method uses local image features and follows the part-based detection approach. It fuses intensity and depth information in a probabilistic framework. The depth of each local feature is used to weigh the probability of finding the object at a given distance. To train the system for an object class, only a database of images annotated with bounding boxes is required, thus automatizing the extension of the system to different object classes. We apply our method to the problem of detecting vehicles from a moving platform. The experiments with a data set of stereo images in an urban environment show a significant improvement in performance when using both information modalities.

In 2013, the method has been published in IEEE Transactions on Intelligent Transportation Systems .

The objective of this recently started research work is to re-design in a highly parallel fashion our Bayesian Perception approach for dynamic environments (based on the BOF concept), in order to deeply integrate the software components into new multi-processor hardware boards. The goal is to miniaturize the software/hardware perception system (i.e., to reduce the size, the load, the energy consumption and the cost, while increasing the efficiency of the system).

For supported this research, we began to work in the “Perfect” project. This project, included in the IRT-Nano program, involves the CEA-LETI DACLE lab and ST-Microelectronics. Perfect is focusing onto the second integration objectives (6 years) and the development of integrated open platforms in the domain of transportation (vehicle and infrastructure) and in a second step in the domain of health sector (mobility of elderly and handicapped people, monitoring of elderly people at home…). The objective of e-Motion in this project is to transfer and port its main Bayesian perception modules from traditional computing system to embedded low-power multi-processors board. The targeted board is a STHorm from ST Microelectronics which is has a many-core architecture with a very low consumption. In 2013 we worked with the CEA to obtain a first implementation of the Bayesian occupancy grid filter on STHorm. Those preliminary results demonstrated the feasibility of the concepts but highlighted some key points to improve such as the memory footprint we need to reduce to obtain real-time accurate results.

In the field of vehicle risk assessment system, vehicle to road localization is an essential information to predict drivers behaviors as well as collision risk evaluation. To achieve this task, we have developed a vision based lane tracker to estimate the geometry of the lane using the line markers. Previous development was based on a Monte-Carlo particle filter to estimate simultaneously the road plane orientation, the lane curvature and the camera position. To perform the particle evaluation, the algorithm first process a ridge extraction of the camera image and then projected the left and right marker line represented by the particle on the ridge image.

The first improvement of the tracker consists on dynamically adapting the scale of the ridge filter to improve the efficiency and the precision of the particle evaluation. The second improvement is capability the perform multi-lane tracking for example in highway environment. To solve this problem, the position of the lane is added to the state and the particle evaluation consider the total number of line marker (a-priori known). Figure  shows the results of the lane tracker program on a highway environment.

For both Advanced Driving Assistance Systems and Autonomous Vehicles, it is very important to have the capability of predicting and understanding the driver's behavior. This work addresses this subject in a bottom-up fashion by first detecting low-level “atomic” maneuvers which can be used as a building block for more complex behavior. Concretely, we have developed a learning-based approach that uses lane tracking data to predict lane changes.

Most works in the literature address this as a classification problem, and often use some version of Support Vector Machines (SVM) to solve it. The problem with this approach is that it is sensitive to noise and can yield high-frequency oscillations in the obtained predictions, moreover, they do not provide any information concerning the Time To Change (TTC). Other approaches use a filtering approaches, using Hidden Markov Models (HMM), for example. Although they produce smoother predictions and, in some cases, even a TTC estimate, some studies suggest that HMM-based approaches are less accurate than those based on SVMs.

Our work combines the advantages of both approaches in a hierarchical fashion. First, lane tracking data (i.e. lane-relative yaw, lane-relative lateral position and their fist-order derivatives) are used as an input of a multi-class SVM. Then, the Bradley-Terry model is used to translate the SVM output into a probability which is used as the observation model of a Bayesian filter (Fig. ).

This work has been published in the Intelligent Vehicles conference . Since then, we have worked on an improved HMM-only approach which addresses the shortcomings of similar approaches by using a continuous observation model. In our preliminary experiments, this approach leads to improved predictions over the hybrid one.

When it comes to modeling and learning complex human behavior, the preferred approach in the literature is to try to learn the typical motion patterns that people or vehicles get engaged into in a given environment. This has, however, a major drawback: the learned patterns only apply to the environment where they have been learned. This means that, for a new environment, previous knowledge cannot be used and patterns should be learned from scratch.

This situation has been recently addressed by a family of approaches which rely on two complementary hypotheses:

Under these assumptions, the problem becomes that of learning the unknown cost function by observing how people move. This is often called apprenticeship learning and, when the underlying planning model is a Markov Decision Process, inverse reinforcement learning.

We have been working on a software library and evaluation testbed for different features and cost-function learning algorithms. We have conducted, in collaboration with the University of Freiburg, a first round of experiments concerning people moving in crowds. The results will be the subject of a submission to the IROS 2014 conference.

We have also been working on an application for intelligent vehicles and ADAS. As a first step, we have developed a ROS interface for the TORCS racing simulator, as well as a road simulation using the same platform (Fig. ). This work has been the product of a collaboration with the Beijing University and IIIT Hyderabad.

From a safety perspective, road intersections are the most dangerous areas in the road network. They are also the most complex. Because of the extended situational awareness that they provide, wireless vehicular communications (or Vehicle-to-X communications, V2X) could greatly reduce the rate of intersection accidents. However, numerous research challenges remain before the full use of this technology can be achieved. A PhD was started on this topic in 2009 in collaboration with Renault, and was successfully defended in 2012 . The purpose was to formulate and develop a probabilistic reasoning framework which would allow combining the information shared by the vehicles to estimate the situation and the associated risk as a vehicle negotiates an intersection. The first contribution of the PhD was to model the motion of vehicles using a Dynamic Bayesian Network where the maneuvers of different vehicles influence each other via an “expected maneuver”. This “expected maneuver” represents what a driver is expected to do given the state of the other vehicles in the area and the traffic rules which apply at the intersection. Thanks to the use of a probabilistic framework, uncertainties related to sensor errors and interpretation ambiguities are handled. The second contribution was a novel approach to risk estimation based on the comparison between what drivers intend to do and what they are expected to do. The reasoning is carried out by performing inference on the Dynamic Bayesian Network introduced earlier, using a particle filter. The approach was validated with field trials using Renault passenger vehicles equipped with vehicle-to-vehicle wireless communication modems , and in simulation . The results show that the algorithm is able to detect dangerous situations early and complies with real-time constraints. We also developed a theoretical extension of the model to generalize it to arbitrary traffic situations . This work is still ongoing thanks to an Inria@SiliconValley fellowship granted to S. Lefevre at the end of her PhD. Since January 2013 she is working in the Teleimmersion group at the University of California Berkeley, as a postdoctoral researcher. The research conducted there lead to two new developments on the topic of “Safety applications at road intersections for connected vehicle”.

The first development concerns probabilistic decision making for Collision Avoidance (CA) systems. In the processing chain of a CA system, the “Decision making” module follows the “Risk assessment” module. The research done during the PhD stopped at the “Risk assessment” module, and we now address the challenges present in the “Decision making” module. We identified two main challenges:

In recent work we proposed to introduce the possibility for a CA system to postpone making a decision. Our objective is to implement the fact that in some situations the new observations obtained by waiting will reduce the uncertainty about the occurrence of a collision, therefore the decision will be more reliable if it is made later using this additional information. The important question to solve is whether the potential gain brought by the additional information outweighs the cost of waiting. In order to answer this question, our decision making approach runs a preposterior analysis to determine the expected value and cost of the additional information. The value of the additional information can be quantified by means of the Expected Value of Sample Information (EVSI). It corresponds to the additional expected payoff possible through knowledge of the additional information and is computed by subtracting the expected costs of deciding with and without additional information. The cost of the additional information is quantified by means of the Expected Cost of Waiting (ECW). It is computed as the difference between the probability that the CA system will be able to avoid the potential collision if it intervenes now and if it intervenes at time t+1. Our decision making strategy is to postpone the decision making process to time t+1 if and only if the EVSI is positive and the ECW is null. The algorithm was tested in simulation at a two-way stop intersection for collision scenarios and no-collision scenarios involving two vehicles. A comparative evaluation with a decision making strategy which does not allow postponing decisions showed that our approach generates fewer false alarms and avoids as many collisions. These results were published at the conference IEEE IROS'13 and a patent application was filed with UC Berkeley .

The second development addresses the relationship between privacy strategies for V2X and safety applications which rely on V2X. User privacy is a requirement for wireless vehicular communications, and a number of privacy protection strategies have already been developed and standardized. In particular, methods relying on the use of temporary pseudonyms and silent periods have proved their ability to confuse attackers who would attempt to track vehicles. However, these privacy protection schemes are not without consequences for safety applications. Such applications make decisions (e.g. warning drivers of an upcoming danger) based on their current estimation of the state of the real world, and this representation is created from the information contained in beacons received from other vehicles. Therefore, interruptions in the transmission of information will impact the decision-making process. If a silent period is scheduled to start at a safety-critical moment, it could result in safety systems not intervening when they should have, namely a “missed intervention”. From a user and safety perspective, this is not acceptable. In this work we address this issue and evaluate the impact of pseudonym change strategies on V2X-based Intersection Collision Avoidance (ICA) system. We use the ICA system developed during the PhD and simulated 3 different privacy protection strategies:

Simulations were conducted using the same simulator and the same scenarios as the tests run during the PhD. The performance of the three privacy strategies was evaluated both in terms of privacy and in terms of successful interventions of the ICA system. The results show that the ICA application requires silent periods to be shorter than two seconds in order to operate correctly in conjunction with the SAE J2735 standard. They also indicate that the addition of simple rules which authorize or not a pseudonym change depending on the context leads to major safety improvements compared to the SAE J2735 standard alone (see Figure ). These results, which were published at the conference IEEE VNC'13 , highlight the necessity of a joint design. That is, the requirements of safety applications should be taken into account when designing privacy strategies, and pseudonym change schemes should be accounted for when designing safety applications which rely on V2X communications. This collaboration is necessary in order to ensure that vehicular communications and safety applications do not neutralize each other, but instead, work together toward safer roads.

This study is made in collabortion with MBDA (Monsieur Le Menec ) and Probayes (Monsieur Laurent Saroul ) under the ITP framework financed by the british MOD and the french DGA.

From N°51 of Vector Magazine (copyright MBDA) It’s an enduring question facing those in military conflict: ‘when do you pull the trigger?’ However, for pilots in air combat there is an added question: ‘Once you’ve pulled the trigger, when do you break the link with your missile?’ A new answer to that problem was a highlight of the MCM-ITP conference at Lille in May (see Vector 50). Entitled ‘Guidance in Uncertain Shooting Domains’, this joint project between MBDA and French company Probayes has produced a set of algorithms to help a pilot decide when to break a telemetry link. It’s a decision fraught with danger. As pilots approach a target, the longer they keep the link, the greater the chance of their missile finding its target. However, closing in on the target usually means entering the enemy’s own kill zone. So, a calculation of the best trade-off between mission success and pilot safety needs to be made.

“At the moment, when a pilot is engaging an enemy aircraft, he’s obliged to do sums in his head,” explained Graham Wallis, MBDA UK’s Chief Technologist. “What we’re trying to do is to take that away, and hide it behind a probability display.” The problem arises because a seeker’s scanning range is often far shorter than the maximum travelling distance, or kinematic range, of a missile. Though less of an issue at short range, it’s a problem for medium-range weapons – where the target is likely to manoeuvre beyond the initial seeker scan area; hence the need for guidance from the pilot, who may have the target in radar sight.

SUCCESS PROBABILITY Stepping back, there are two sides to the firing equation. As customers require an air-to-air missile that will find its target almost 100 of the time, MBDA provides distance data (known as Launch Success Zone tables) to achieve that. They are understandably conservative. Clearly, though, the final decision to break the datalink can only be taken by the pilot, who is also making mental calculations to minimise his or her exposure to deadly risk. GUSD effectively offers a ‘probability meter’ to help reconcile those two imperatives.

Physically, GUSD could be a display with four bars and the circle of a pie-chart. Each bar represents the probability in percentage terms of an enemy pilot adopting one of four typical behaviours during air combat: flying head-on to attack; turning tail and heading for home, and either turning left or right – along with the probabilities that the enemy has just launched its own missile. The pie chart gives a single percentage – of the MBDA missile’s chances of hitting its target. “The figures displayed are effectively our computer trying to read the mind of the enemy pilot,” said Graham Wallis, whose team also drew on the experience of MBDA’s former air force pilots.

QUICK CALCULATIONS Not surprisingly, the computations behind GUSD are hugely complex. The main input is the realtime radar tracking data of the enemy aircraft, although other elements such as seeker acquisition data, missile dynamics and the missile’s inertial navigation errors are also included. A set of algorithms (and their associated mathematical methods, see box) then process this input – with the three key algorithms covering target behaviour and identification (Hidden Markov Models); the generation of bundles of trajectories (Markov Chains and Monte Carlo techniques) and trajectory collision checking (R-Tree).

Currently at Technology Readiness Level 3, GUSD’s future depends on being incorporated into a programme. The generic data used to date would be replaced by real missile and seeker data, requiring GUSD to move out of the open forum of MCM-ITP and into the area covered by defence secrecy. “Implementation of research and technology is particularly difficult when it comes to jet fighters,” Graham Wallis declared. “But I’m optimistic it will get into a future upgrade of current aircraft, and could even replace the Launch Success Zone tables as a firing cue for pulling that trigger.”

MATHEMATICAL MODELS Named after 19th century Russian mathematician Andrey Markov, the Markov Chain uses a set of rules to predict what will happen next in a situation, when all the variables are known. In a game of bridge, if all the hands were known to all players and they had no discretion over which card to play next, every card laid down could be predicted as a Markov Chain. With a Hidden Markov Model, a player doesn’t know what an opponent holds but can infer that, for example, they no longer have any trump cards because of how they have played their last hand. The Model takes multiple states e.g. ‘Opponent Has Trumps’ or ‘Opponent Doesn’t Have Trumps’ and establishes a percentage weighting of probability to each – which can then be used for a decision over the best card to play. Though it is only an analogy, GUSD’s algorithms use the mass of input data to infer the probability of an enemy pilot’s tactical moves. R-trees are a way of handling multi-dimensional information - in GUSD’s case by using geometric models that drastically cut the computation load.

OTHER APPLICATIONS Even before its own future is decided, GUSD could already lead to a successor MCM-ITP project with Probayes in the area of mission planning for long-range missiles. Called Rapid Mission Planning and Rehearsal, it could automate much of what is currently a labour-intensive process and to provide the customer with different options for mission routes – each with its own probability rating for success.

With robots technology shifting towards entering human populated environments, the need for augmented perceptual robotic skills emerges that complement to human presence. In this integration, perception and adaptation to the implicit human social conventions plays a fundamental role. Toward this goal, we introduce a novel methodology to detect and analyse complex spatial interactions of multiple people and encode them in the form of a social map, whose structure is obtained by computing a latent space representation of human proxemic behaviour. We accomplish this by appointing to humans distinct, skew-normal density functions that quantify social sensitivity and by using them in the sequel to induce a training set for regressing a collective density function of social sensitivity (see fig. ). Finally, we extract level-sets of constant social sensitivity levels within the social map by which we can effectively and efficiently analyse individual as well as shared interaction zones of varying shape and size. Extensive experiments on human interaction scenarios demonstrate the feasibility and utility of the proposed approach in diverse conditions and promote its application to social mapping of human-populated environments. This work was published at IROS and submitted to RAS journal.

Navigation in large dynamic spaces has been adressed often using deterministic representations, fast updating and reactive avoidance strategies. However, probabilistic representations are much more informative and their use in mapping and prediction methods improves the quality of obtained results. Since 2008 we have proposed a new concept to integrate a probabilistic collision risk function linking planning and navigation methods with the perception and the prediction of the dynamic environments . Moving obstacles are supposed to move along typical motion patterns represented by Gaussian Processes or Growing HMM. The likelihood of the obstacles' future trajectory and the probability of occupation are used to compute the risk of collision. The proposed planning algorithm, call RiskRRT (see Figure for an illustration), is a sampling-based partial planner guided by the risk of collision. Results concerning this work were published in . and . In 2013, Jorge Rios defended his phD on this topic. We obtained an Inria ADT to optimize and share the RiskRRT algorithm.

In this work, we proposes a different form of robotic navigation in dynamic environments, where the robot takes advantage of the motion of pedestrians, in order to improve its own navigation capabilities. The main idea is that, instead of treating persons as dynamic obstacles that should be avoided, they should be treated as special agents with an expert knowledge of navigating in dynamic scenarios. To benefit from the motion of pedestrians, this work proposes that the robot select and follow them, so it can move along optimal paths, deviate from undetected obstacles, improve navigation in densely populated areas and increase its acceptance by other humans. To accomplish this proposition, novel approaches are developed in the area of leader selection, where two methods are explored. The first uses motion prediction approaches while the second uses a machine learning method, to evaluate the leader quality of subjects, which is trained with real examples. Finally, the leader selection methods are integrated with motion planning algorithms and experiments are conducted in order to validate the proposed techniques. One of the most relevant application is navigation among crowds. Figure illustrates the concept.

The work is published in and Procopio Stein defended his phD the 11th of december 2013 at the Aveiro University (phD co-directed by Anne Spalanzani and Vitor Santos).

The aging of world's population is bringing the need to provide robotic platforms capable to assist elder people to move . It is necessary that such transportation is reliable, safe and comfortable. People with motor disabilities and elders are expected to benefit from new developments in the field of autonomous navigation robotics.

Autonomously driven wheelchairs are a real need for those patients who lack the strength or skills to drive a normal electric wheelchair. The services provided by this kind of robots can also be used to provide a service of comfort, assisting the user to perform difficult tasks as traversing a door, driving in a narrow corridor etc.

Simple improvements of the classical powered wheelchair can often diminish several difficulties while driving. This idea of comfort has emerged as a design goal in autonomous navigation systems, designers are becoming more aware of the importance of the user when scheming solution algorithms. This is particularly important when designing services or devices intended to assist people with some disability. In order for the robot to have a correct understanding of the intention of the user (when moving around) it is necessary to create a model of the user that takes into account his habits, type of disability and environmental information. The ongoing research project is centered in the understanding of the intentions of the user while driving an autonomous wheelchair, so that we can use this information to make this task easier.

During the last two years the work was centered in the improvement of the usability of the system. A review of the state of the art in user's intention estimation algorithms was made and a new model to infer the intentions of the user in a known environment was presented , .

The algorithm models the intention of the user as 2D topological goals in the environment. Those places are selected according to how frequently they are visited by the user (user habits). The system was designed so that the user can give orders to the wheelchair by using any type of interface, as long as he can show the direction of the intended movement (joystick, head tracking, brain control, etc). As shown in figure, the chosen approach uses a Dynamic Bayesian Network to model and infer the intentions. The main contribution of this work is to model the intention of the user as topological goals instead of normal trajectory-based methods, therefore the model is simpler to deal with.

In 2013 the results of the user intention destination method were published in the IROS conference . The presented work covered the following aspects:

User Intention Estimation: Two different methods to drive the wheelchair were compared, a semi-autonomous and a manual mode. In semi-autonomous mode the user’s intention is estimated from the position of the face and the wheelchair takes care of all the planning to arrive there while avoiding obstacles. In manual mode the wheelchair is driven using the face without assistance from the robotic controller.

The Bayesian estimator shown in Fig. was used to do the inference of the desired destination of the user in semi-autonomous mode. The user’s intention was modeled as a set of destinations commonly visited by the user and the task consisted in finding the destination targeted by the user.

Interfaces: People with motor disabilities and elders often have problems using joysticks and other standard control devices. Under this consideration our experimental platform was equipped with different types of user-interfaces to provide a multi-modal functionality as described in . A face pose interface allows to control the wheelchair's motion by changing the face direction, while voice recognition interface is used to guarantee an adequate control of the wheelchair for those commands that otherwise would be difficult to give by only using the face (Stop, start, etc). This exploitation of more natural and easy-to-use human machine interfaces was one of the main contributions of the work presented in .

Experimental evaluation: Experiments were done in the hall of the Inria Rhône-Alpes laboratory. People in the scene were tracked to detect the most visited destinations in this setting (red circles). Those typical destinations were then placed in the map used by the robotic wheelchair. Each destination has a probability value related to the number of times that it is visited by people. The extracted typical destinations and related probabilities are used as prior knowledge when inferring the user’s desired destination. In Fig.(Right) the spheres represent the typical destinations placed in the internal map o the wheelchair and the size of the sphere represents the probability of being the desired destination of the user in the wheelchair given it's position in the map and direction of the face (blue arrow).

To evaluate the performance of the method different persons were asked to drive the wheelchair in both “manual” and “semi-autonomous” mode. The trajectories of followed by the wheelchair were recorded and evaluated in , In Fig. one sample of those trajectories is presented where it can be appreciated how those trajectories executed with assistance of the robot are considerably softer than those obtained in manual mode as explained in .

Human aware navigation: Current work is being done in the construction of a social cost-map that is able to work with different open source path planning algorithms. This plug-in was developed using the method presented by Rios in .

A central issue in speech science concerns the nature of representations and processes involved in communication. The search for phoneme or syllable specific invariants led to three major sets of approaches: motor, auditory and perceptuo-motor theories. They have been widely argued for and against, but the theoretical debate appears to be stagnating. It is our belief that computationnal models designed within a rigorous mathematical framework may allow to put forward new arguments to support either theory, and new ideas for experiments to be carried out on human subjects.

We have designed an integrative Bayesian model which allows to study auditory, motor and perceptuo-motor aspects of speech production and perception. This model was tested on perception tasks on evaluation corpora with more and more variability compared to the learning corpus. This showed a really high robustness of the purely motor model, which contained more information that it is the case in practise, due to unrealistic learning methods. The work was then focused on more realistic learning algorithms, where speech motor gestures are unsupervisedly learned through imitation, by generating motor gestures trying to reach auditory targets, and memorising the acoustics corresponding to these motor commands. This work was published in 2013 . Raphael Laurent defended his phD in 2013.

A book and the compagnon software on bayesian programming have been released this year :

The book “Bayesian Programming” is available at http://www.crcpress.com/product/isbn/9781439880326 and the associated software at http://www.probayes.com/fr/Bayesian-Programming-Book/

Features

Presents a new modeling methodology and inference algorithms for Bayesian programming Explains how to build efficient Bayesian models Addresses controversies, historical notes, epistemological debates, and tricky technical questions in a dedicated chapter separate from the main text Encourages further research on new programming languages and specialized hardware for computing large-scale Bayesian inference problems Offers an online Python package for running and modifying the Python program examples in the book

Summary

Probability as an Alternative to Boolean Logic While logic is the mathematical foundation of rational reasoning and the fundamental principle of computing, it is restricted to problems where information is both complete and certain. However, many real-world problems, from financial investments to email filtering, are incomplete or uncertain in nature. Probability theory and Bayesian computing together provide an alternative framework to deal with incomplete and uncertain data.

Decision-Making Tools and Methods for Incomplete and Uncertain Data

Emphasizing probability as an alternative to Boolean logic, Bayesian Programming covers new methods to build probabilistic programs for real-world applications. Written by the team who designed and implemented an efficient probabilistic inference engine to interpret Bayesian programs, the book offers many Python examples that are also available on a supplementary website together with an interpreter that allows readers to experiment with this new approach to programming.

Principles and Modeling

Only requiring a basic foundation in mathematics, the first two parts of the book present a new methodology for building subjective probabilistic models. The authors introduce the principles of Bayesian programming and discuss good practices for probabilistic modeling. Numerous simple examples highlight the application of Bayesian modeling in different fields.

Formalism and Algorithms

The third part synthesizes existing work on Bayesian inference algorithms since an efficient Bayesian inference engine is needed to automate the probabilistic calculus in Bayesian programs. Many bibliographic references are included for readers who would like more details on the formalism of Bayesian programming, the main probabilistic models, general purpose algorithms for Bayesian inference, and learning problems.

FAQs Along with a glossary, the fourth part contains answers to frequently asked questions. The authors compare Bayesian programming and possibility theories, discuss the computational complexity of Bayesian inference, cover the irreducibility of incompleteness, and address the subjectivist versus objectivist epistemology of probability.

The First Steps toward a Bayesian Computer A new modeling methodology, new inference algorithms, new programming languages, and new hardware are all needed to create a complete Bayesian computing framework. Focusing on the methodology and algorithms, this book describes the first steps toward reaching that goal. It encourages readers to explore emerging areas, such as bio-inspired computing, and develop new programming languages and hardware architectures.