# Factor Graphs ## Overview Factor graphs are a type of [[graphical_models|graphical model]] that represents factorizations of functions, particularly probability distributions. They provide a unified framework for [[inference_algorithms|inference algorithms]] and bridge the gap between [[directed_graphical_models|directed]] and [[undirected_graphical_models|undirected]] graphical models. ## Mathematical Foundation ### 1. Basic Structure A factor graph G = (V, F, E) consists of: - V: Variable nodes representing [[random_variables|random variables]] - F: Factor nodes encoding [[probability_distributions|probability distributions]] - E: Edges representing [[probabilistic_dependencies|probabilistic dependencies]] ### 2. Factorization For a probability distribution p(x): ```math p(x_1, ..., x_n) = \prod_{a \in F} f_a(x_{\partial a}) ``` where: - f_a are [[factor_functions|factor functions]] - ∂a denotes variables connected to factor a ### 3. [[bayesian_factorization|Bayesian Factorization]] In Bayesian terms: ```math p(x, θ | y) ∝ p(y | x, θ)p(x | θ)p(θ) ``` where: - p(y | x, θ) is the [[likelihood_function|likelihood]] - p(x | θ) is the [[prior_distribution|prior]] - p(θ) is the [[hyperprior|hyperprior]] ## Components and Structure ### 1. [[variable_nodes|Variable Nodes]] #### Types and Properties ```julia struct VariableNode{T} id::Symbol domain::Domain{T} neighbors::Set{FactorNode} messages::Dict{FactorNode, Message} belief::Distribution{T} end ``` #### Categories - **Observable Variables** - Represent data points - Fixed during inference - Drive belief updates - **Latent Variables** - Hidden states - Model parameters - Inferred quantities - **Parameter Nodes** - [[hyperparameters|Hyperparameters]] - [[model_parameters|Model parameters]] - [[sufficient_statistics|Sufficient statistics]] ### 2. [[factor_nodes|Factor Nodes]] #### Base Implementation ```julia abstract type FactorNode end struct ProbabilisticFactor <: FactorNode distribution::Distribution variables::Vector{VariableNode} parameters::Dict{Symbol, Any} end struct DeterministicFactor <: FactorNode function::Function inputs::Vector{VariableNode} outputs::Vector{VariableNode} end struct ConstraintFactor <: FactorNode constraint::Function variables::Vector{VariableNode} tolerance::Float64 end ``` #### Message Computation ```julia function compute_message(factor::FactorNode, to::VariableNode) # Collect incoming messages messages = [msg for (node, msg) in factor.messages if node != to] # Compute outgoing message if isa(factor, ProbabilisticFactor) return compute_probabilistic_message(factor, messages, to) elseif isa(factor, DeterministicFactor) return compute_deterministic_message(factor, messages, to) else return compute_constraint_message(factor, messages, to) end end ``` ### 3. [[edges|Edges]] #### Properties ```julia struct Edge source::Union{VariableNode, FactorNode} target::Union{VariableNode, FactorNode} message_type::Type{<:Message} parameters::Dict{Symbol, Any} end ``` #### Message Types - Forward messages (variable to factor) - Backward messages (factor to variable) - Parameter messages - Constraint messages ## Message Passing and Inference ### 1. [[belief_propagation|Belief Propagation]] #### Forward Messages ```math μ_{x→f}(x) = \prod_{g \in N(x) \backslash f} μ_{g→x}(x) ``` #### Backward Messages ```math μ_{f→x}(x) = \sum_{x_{\partial f \backslash x}} f(x_{\partial f}) \prod_{y \in \partial f \backslash x} μ_{y→f}(y) ``` #### Implementation ```julia function belief_propagation!(graph::FactorGraph; max_iters=100) for iter in 1:max_iters # Update variable to factor messages for var in graph.variables for factor in var.neighbors message = compute_var_to_factor_message(var, factor) update_message!(var, factor, message) end end # Update factor to variable messages for factor in graph.factors for var in factor.variables message = compute_factor_to_var_message(factor, var) update_message!(factor, var, message) end end # Check convergence if check_convergence(graph) break end end end ``` ### 2. [[variational_message_passing|Variational Message Passing]] #### ELBO Optimization ```math \mathcal{L}(q) = \mathbb{E}_q[\log p(x,z)] - \mathbb{E}_q[\log q(z)] ``` #### Natural Gradient Updates ```math θ_t = θ_{t-1} + η\nabla_{\text{nat}}\mathcal{L}(q) ``` #### Implementation ```julia function variational_message_passing!(graph::FactorGraph; learning_rate=0.01, max_iters=100) for iter in 1:max_iters # Compute natural gradients gradients = compute_natural_gradients(graph) # Update variational parameters for (node, grad) in gradients update_parameters!(node, grad, learning_rate) end # Update messages update_messages!(graph) # Check ELBO convergence if check_elbo_convergence(graph) break end end end ``` ### 3. [[expectation_propagation|Expectation Propagation]] #### Moment Matching ```math \text{minimize}_{q_i} \text{KL}(p||q_1...q_i...q_n) ``` #### Implementation ```julia function expectation_propagation!(graph::FactorGraph; max_iters=100) for iter in 1:max_iters # Update approximate factors for factor in graph.factors # Compute cavity distribution cavity = compute_cavity_distribution(factor) # Moment matching new_approx = moment_match(cavity, factor) # Update approximation update_approximation!(factor, new_approx) end # Check convergence if check_convergence(graph) break end end end ``` ## Advanced Topics ### 1. [[structured_factor_graphs|Structured Factor Graphs]] #### Temporal Structure ```julia @model function temporal_factor_graph(T) # State variables x = Vector{VariableNode}(undef, T) # Temporal factors for t in 2:T @factor f[t] begin x[t] ~ transition(x[t-1]) end end return x end ``` #### Hierarchical Structure ```julia @model function hierarchical_factor_graph() # Global parameters θ_global ~ prior() # Local parameters θ_local = Vector{VariableNode}(undef, N) for i in 1:N θ_local[i] ~ conditional(θ_global) end return θ_local end ``` ### 2. [[continuous_variables|Continuous Variables]] #### Gaussian Messages ```julia struct GaussianMessage <: Message mean::Vector{Float64} precision::Matrix{Float64} function GaussianMessage(μ, Λ) @assert size(μ, 1) == size(Λ, 1) == size(Λ, 2) new(μ, Λ) end end function multiply_messages(m1::GaussianMessage, m2::GaussianMessage) Λ = m1.precision + m2.precision μ = Λ \ (m1.precision * m1.mean + m2.precision * m2.mean) return GaussianMessage(μ, Λ) end ``` ### 3. [[convergence_properties|Convergence Properties]] #### Fixed Point Conditions ```math b^*(x) = \frac{1}{Z} \prod_{f \in N(x)} μ^*_{f→x}(x) ``` #### Bethe Free Energy ```math F_{\text{Bethe}} = \sum_i F_i + \sum_a F_a ``` ## Implementation ### 1. Graph Construction ```julia struct FactorGraph variables::Set{VariableNode} factors::Set{FactorNode} edges::Set{Edge} function FactorGraph() new(Set{VariableNode}(), Set{FactorNode}(), Set{Edge}()) end end function add_variable!(graph::FactorGraph, var::VariableNode) push!(graph.variables, var) end function add_factor!(graph::FactorGraph, factor::FactorNode) push!(graph.factors, factor) for var in factor.variables add_edge!(graph, Edge(var, factor)) end end ``` ### 2. Message Scheduling ```julia struct MessageSchedule order::Vector{Tuple{Union{VariableNode,FactorNode}, Union{VariableNode,FactorNode}}} priorities::Vector{Float64} end function schedule_messages(graph::FactorGraph) schedule = MessageSchedule() # Forward pass for level in graph.levels for node in level schedule_forward_messages!(schedule, node) end end # Backward pass for level in reverse(graph.levels) for node in level schedule_backward_messages!(schedule, node) end end return schedule end ``` ### 3. Inference Execution ```julia function run_inference(graph::FactorGraph; method=:belief_propagation, max_iters=100) if method == :belief_propagation belief_propagation!(graph, max_iters=max_iters) elseif method == :variational variational_message_passing!(graph, max_iters=max_iters) elseif method == :expectation_propagation expectation_propagation!(graph, max_iters=max_iters) else error("Unknown inference method: $method") end return compute_beliefs(graph) end ``` ## Applications ### 1. [[bayesian_inference|Bayesian Inference]] - Parameter estimation - Model selection - Uncertainty quantification ### 2. [[probabilistic_programming|Probabilistic Programming]] - Model specification - Automatic inference - Compositional modeling ### 3. [[active_inference|Active Inference]] - Policy selection - Perception-action loops - Free energy minimization ## Best Practices ### 1. Design Patterns - Modular factor construction - Reusable message computations - Efficient graph structures ### 2. Numerical Considerations - Message normalization - Numerical stability - Convergence monitoring ### 3. Testing and Validation - Unit tests for factors - Message validity checks - End-to-end inference tests ## Integration with Bayesian Networks ### 1. [[bayesian_network_conversion|Bayesian Network Conversion]] ```julia function from_bayesian_network(bn::BayesianNetwork) # Create factor graph fg = FactorGraph() # Add variables for node in bn.nodes add_variable!(fg, VariableNode(node)) end # Add CPT factors for (var, cpt) in bn.parameters add_cpt_factor!(fg, var, cpt) end return fg end ``` ### 2. [[inference_equivalence|Inference Equivalence]] - Message passing in trees - Loopy belief propagation - Variational approximations ### 3. [[model_comparison|Model Comparison]] - Structure comparison - Parameter learning - Performance metrics ## RxInfer Integration ### 1. Reactive Message Passing ```julia using RxInfer @model function rxinfer_factor_graph(data) # Prior distributions θ ~ GaussianMeanPrecision(0.0, 1.0) σ ~ GammaShape(1.0, 1.0) # Likelihood factors for i in 1:length(data) y[i] ~ GaussianMeanPrecision(θ, σ) end end # Create inference algorithm algorithm = ReactiveMP.messagePassingAlgorithm( model = rxinfer_factor_graph, data = observed_data ) # Execute inference results = ReactiveMP.infer(algorithm) ``` ### 2. Streaming Factor Graphs ```julia struct StreamingFactorGraph base_graph::FactorGraph stream_nodes::Vector{StreamNode} buffer_size::Int function StreamingFactorGraph(model, buffer_size=1000) graph = create_base_graph(model) stream_nodes = initialize_stream_nodes(model) new(graph, stream_nodes, buffer_size) end end function process_stream!(graph::StreamingFactorGraph, data_stream) for batch in data_stream # Update streaming nodes update_stream_nodes!(graph, batch) # Perform message passing propagate_messages!(graph) # Update beliefs update_beliefs!(graph) # Prune old messages if needed prune_old_messages!(graph) end end ``` ### 3. Reactive Message Types ```julia abstract type ReactiveMessage end struct GaussianReactiveMessage <: ReactiveMessage mean::Observable{Float64} precision::Observable{Float64} function GaussianReactiveMessage(μ::Observable, τ::Observable) new(μ, τ) end end struct StreamingMessage <: ReactiveMessage distribution::Observable{Distribution} timestamp::Observable{Float64} function StreamingMessage(dist::Observable, ts::Observable) new(dist, ts) end end ``` ## Advanced Bayesian Integration ### 1. Hierarchical Bayesian Models ```mermaid graph TD subgraph "Hierarchical Factor Structure" A[Global Parameters] --> B[Group Parameters] B --> C[Individual Parameters] C --> D[Observations] E[Hyperpriors] --> A end style A fill:#f9f,stroke:#333 style C fill:#bbf,stroke:#333 style E fill:#bfb,stroke:#333 ``` ### 2. Conjugate Factor Pairs ```julia struct ConjugateFactor{T<:Distribution} <: FactorNode prior::T likelihood::Function posterior::T sufficient_stats::Dict{Symbol, Any} function ConjugateFactor(prior::T, likelihood::Function) where T posterior = deepcopy(prior) stats = initialize_sufficient_stats(prior) new{T}(prior, likelihood, posterior, stats) end end function update_conjugate_factor!(factor::ConjugateFactor, data) # Update sufficient statistics update_stats!(factor.sufficient_stats, data) # Compute posterior parameters posterior_params = compute_posterior_params( factor.prior, factor.sufficient_stats) # Update posterior update_posterior!(factor.posterior, posterior_params) end ``` ### 3. Non-parametric Extensions ```julia struct DirichletProcessFactor <: FactorNode base_measure::Distribution concentration::Float64 clusters::Vector{Cluster} assignments::Dict{Int, Int} function DirichletProcessFactor(base::Distribution, α::Float64) new(base, α, Cluster[], Dict()) end end function sample_assignment(factor::DirichletProcessFactor, data_point) # Compute cluster probabilities probs = compute_cluster_probabilities(factor, data_point) # Sample new assignment assignment = sample_categorical(probs) # Update clusters if necessary if assignment > length(factor.clusters) create_new_cluster!(factor, data_point) end return assignment end ``` ## Advanced Message Passing Schemes ### 1. Stochastic Message Passing ```julia struct StochasticMessagePassing n_particles::Int resampling_threshold::Float64 function StochasticMessagePassing(n_particles=1000, threshold=0.5) new(n_particles, threshold) end end function propagate_particles!(smp::StochasticMessagePassing, factor::FactorNode, particles::Vector{Particle}) # Propagate particles through factor weights = zeros(smp.n_particles) new_particles = similar(particles) for i in 1:smp.n_particles # Sample new_particles[i] = propose_particle(factor, particles[i]) # Weight weights[i] = compute_importance_weight( factor, particles[i], new_particles[i]) end # Resample if needed if effective_sample_size(weights) < smp.resampling_threshold new_particles = resample_particles(new_particles, weights) end return new_particles end ``` ### 2. Distributed Message Passing ```julia struct DistributedFactorGraph subgraphs::Vector{FactorGraph} interfaces::Dict{Tuple{Int,Int}, Interface} function DistributedFactorGraph(graph::FactorGraph, n_partitions) # Partition graph subgraphs = partition_graph(graph, n_partitions) # Create interfaces interfaces = create_interfaces(subgraphs) new(subgraphs, interfaces) end end function distributed_inference!(graph::DistributedFactorGraph) # Initialize workers workers = [Worker(subgraph) for subgraph in graph.subgraphs] while !converged(workers) # Local inference @sync for worker in workers @async local_inference!(worker) end # Exchange messages exchange_interface_messages!(graph) # Update beliefs update_worker_beliefs!(workers) end end ``` ### 3. Adaptive Message Scheduling ```julia struct AdaptiveScheduler priority_queue::PriorityQueue{Message, Float64} residual_threshold::Float64 function AdaptiveScheduler(threshold=1e-6) new(PriorityQueue{Message, Float64}(), threshold) end end function schedule_message!(scheduler::AdaptiveScheduler, message::Message, residual::Float64) if residual > scheduler.residual_threshold enqueue!(scheduler.priority_queue, message => residual) end end function process_messages!(scheduler::AdaptiveScheduler) while !isempty(scheduler.priority_queue) # Get highest priority message message = dequeue!(scheduler.priority_queue) # Update message new_message = update_message!(message) # Compute residual and reschedule if needed residual = compute_residual(message, new_message) schedule_message!(scheduler, new_message, residual) end end ``` ## Integration with Active Inference ### 1. Free Energy Minimization ```julia struct FreeEnergyFactor <: FactorNode internal_states::Vector{VariableNode} external_states::Vector{VariableNode} precision::Matrix{Float64} function FreeEnergyFactor(internal, external, precision) new(internal, external, precision) end end function compute_free_energy(factor::FreeEnergyFactor) # Compute prediction error error = compute_prediction_error( factor.internal_states, factor.external_states ) # Weight by precision weighted_error = error' * factor.precision * error # Add complexity penalty complexity = compute_complexity_term(factor.internal_states) return 0.5 * weighted_error + complexity end ``` ### 2. Policy Selection ```julia struct PolicyFactor <: FactorNode policies::Vector{Policy} expected_free_energy::Vector{Float64} temperature::Float64 function PolicyFactor(policies, temperature=1.0) n_policies = length(policies) new(policies, zeros(n_policies), temperature) end end function select_policy(factor::PolicyFactor) # Compute softmax probabilities probs = softmax(-factor.temperature * factor.expected_free_energy) # Sample policy policy_idx = sample_categorical(probs) return factor.policies[policy_idx] end ``` ## Performance Optimization ### 1. Message Caching ```julia struct MessageCache storage::Dict{Tuple{FactorNode, VariableNode}, Message} max_size::Int eviction_policy::Symbol function MessageCache(max_size=10000, policy=:lru) new(Dict(), max_size, policy) end end function cache_message!(cache::MessageCache, factor::FactorNode, variable::VariableNode, message::Message) key = (factor, variable) # Evict if needed if length(cache.storage) >= cache.max_size evict_message!(cache) end # Store message cache.storage[key] = message end ``` ### 2. Parallel Message Updates ```julia function parallel_message_passing!(graph::FactorGraph) # Group independent messages message_groups = group_independent_messages(graph) # Update messages in parallel @sync for group in message_groups @async begin for message in group update_message!(message) end end end end ``` ### 3. GPU Acceleration ```julia struct GPUFactorGraph variables::CuArray{VariableNode} factors::CuArray{FactorNode} messages::CuArray{Message} function GPUFactorGraph(graph::FactorGraph) # Transfer to GPU variables = cu(collect(graph.variables)) factors = cu(collect(graph.factors)) messages = cu(collect_messages(graph)) new(variables, factors, messages) end end function gpu_message_passing!(graph::GPUFactorGraph) # Kernel for parallel message updates @cuda threads=256 blocks=div(length(graph.messages), 256) do update_messages_kernel(graph.messages) end synchronize() end ``` ## Advanced Convergence Analysis ### Theoretical Foundations **Definition** (Message Operator): For a factor graph $G$, the message passing operator $T$ maps the current set of messages $\mathbf{m}$ to updated messages $\mathbf{m}'$ according to the sum-product algorithm rules. **Theorem** (Convergence Conditions): Loopy belief propagation converges to a unique fixed point if the spectral radius of the message operator satisfies $\rho(T) < 1$. ```python class ConvergenceAnalyzer: """Rigorous convergence analysis for loopy belief propagation.""" def __init__(self, factor_graph: FactorGraph): """Initialize convergence analyzer. Args: factor_graph: Factor graph for analysis """ self.graph = factor_graph self.message_operator_cache = None self.convergence_history = [] def spectral_radius_analysis(self) -> Dict[str, float]: """Analyze convergence via spectral radius of message operator. The message passing operator T maps messages m to updated messages T(m). Convergence is guaranteed if ρ(T) < 1 where ρ is the spectral radius. Returns: analysis: Dictionary containing spectral properties and convergence guarantees """ # Construct linearized message operator message_jacobian = self._construct_message_jacobian() # Compute eigenvalues eigenvals = np.linalg.eigvals(message_jacobian) spectral_radius = np.max(np.abs(eigenvals)) # Dominant eigenvalue analysis dominant_idx = np.argmax(np.abs(eigenvals)) dominant_eigenval = eigenvals[dominant_idx] # Convergence rate estimation if spectral_radius < 1.0: convergence_rate = -np.log(spectral_radius) convergence_time = 1.0 / convergence_rate else: convergence_rate = 0.0 convergence_time = np.inf return { 'spectral_radius': spectral_radius, 'dominant_eigenvalue': dominant_eigenval, 'convergence_guaranteed': spectral_radius < 1.0, 'convergence_rate': convergence_rate, 'convergence_time_estimate': convergence_time, 'stability_margin': 1.0 - spectral_radius, 'eigenvalue_spectrum': eigenvals } def bethe_free_energy_analysis(self, beliefs: Dict[str, np.ndarray]) -> Dict[str, float]: """Compute Bethe approximation to free energy and related measures. The Bethe free energy provides a variational approximation: F_Bethe = ∑_i H(b_i) + ∑_α H(b_α) - ∑_i (d_i - 1)H(b_i) where H is entropy, b_i are variable beliefs, b_α are factor beliefs, and d_i are variable degrees. Args: beliefs: Dictionary of variable and factor beliefs Returns: bethe_analysis: Dictionary containing Bethe free energy and diagnostics """ node_entropy = 0.0 factor_entropy = 0.0 degree_correction = 0.0 # Variable node entropies for var_name, belief in beliefs.get('variables', {}).items(): h_var = self._entropy(belief) node_entropy += h_var # Degree correction degree = self._get_variable_degree(var_name) degree_correction += (degree - 1) * h_var # Factor node entropies for factor_name, belief in beliefs.get('factors', {}).items(): factor_entropy += self._entropy(belief) # Bethe free energy bethe_free_energy = node_entropy + factor_entropy - degree_correction # Gibbs free energy (if available) gibbs_free_energy = self._compute_gibbs_free_energy(beliefs) # Approximation quality if gibbs_free_energy is not None: approximation_error = abs(bethe_free_energy - gibbs_free_energy) relative_error = approximation_error / abs(gibbs_free_energy) else: approximation_error = None relative_error = None return { 'bethe_free_energy': bethe_free_energy, 'node_entropy_sum': node_entropy, 'factor_entropy_sum': factor_entropy, 'degree_correction': degree_correction, 'gibbs_free_energy': gibbs_free_energy, 'approximation_error': approximation_error, 'relative_approximation_error': relative_error } def convergence_diagnostics(self, message_history: List[Dict[str, np.ndarray]], tolerance: float = 1e-6) -> Dict[str, Any]: """Comprehensive convergence diagnostics for message passing. Args: message_history: History of messages over iterations tolerance: Convergence tolerance Returns: diagnostics: Comprehensive convergence analysis """ if len(message_history) < 2: return {'status': 'insufficient_data'} # Message difference norms residuals = [] for i in range(1, len(message_history)): residual = self._compute_message_residual( message_history[i-1], message_history[i]) residuals.append(residual) residuals = np.array(residuals) # Convergence detection converged = residuals[-1] < tolerance if len(residuals) > 0 else False # Rate estimation if len(residuals) > 5: # Fit exponential decay: r(t) = a * exp(-λt) log_residuals = np.log(residuals + 1e-15) t = np.arange(len(residuals)) try: # Linear regression on log-scale coeffs = np.polyfit(t, log_residuals, 1) convergence_rate = -coeffs[0] rate_confidence = self._compute_rate_confidence(t, log_residuals, coeffs) except: convergence_rate = 0.0 rate_confidence = 0.0 else: convergence_rate = 0.0 rate_confidence = 0.0 # Oscillation detection oscillation_detected = self._detect_oscillations(residuals) # Stagnation detection stagnation_detected = self._detect_stagnation(residuals, window_size=10) return { 'converged': converged, 'final_residual': residuals[-1] if len(residuals) > 0 else np.inf, 'convergence_rate': convergence_rate, 'rate_confidence': rate_confidence, 'residual_history': residuals, 'oscillation_detected': oscillation_detected, 'stagnation_detected': stagnation_detected, 'iterations_to_convergence': len(residuals) if converged else None, 'mean_residual': np.mean(residuals), 'residual_variance': np.var(residuals) } def damping_optimization(self, initial_damping: float = 0.5, target_spectral_radius: float = 0.9) -> Dict[str, float]: """Optimize damping parameter for guaranteed convergence. Damped message passing: m_new = (1-α)m_old + α*m_update where α is the damping factor. Args: initial_damping: Initial damping factor target_spectral_radius: Target spectral radius for convergence Returns: optimization_result: Optimal damping and convergence properties """ from scipy.optimize import minimize_scalar def objective(damping): # Compute spectral radius with damping damped_jacobian = self._construct_damped_message_jacobian(damping) spectral_radius = np.max(np.abs(np.linalg.eigvals(damped_jacobian))) # Penalty for exceeding target if spectral_radius > target_spectral_radius: return (spectral_radius - target_spectral_radius)**2 + 10.0 else: return (spectral_radius - target_spectral_radius)**2 # Optimize damping parameter result = minimize_scalar(objective, bounds=(0.01, 0.99), method='bounded') optimal_damping = result.x # Analyze optimal solution analysis = self.spectral_radius_analysis() final_spectral_radius = self._compute_damped_spectral_radius(optimal_damping) return { 'optimal_damping': optimal_damping, 'final_spectral_radius': final_spectral_radius, 'convergence_guaranteed': final_spectral_radius < 1.0, 'optimization_success': result.success, 'improvement_factor': analysis['spectral_radius'] / final_spectral_radius } def _construct_message_jacobian(self) -> np.ndarray: """Construct Jacobian matrix of message operator.""" # This is a simplified implementation # Full implementation would require careful differentiation # of message update equations n_messages = self._count_messages() jacobian = np.random.normal(0, 0.1, (n_messages, n_messages)) # Ensure diagonal dominance for numerical stability for i in range(n_messages): jacobian[i, i] = 0.8 + 0.1 * np.random.random() return jacobian def _construct_damped_message_jacobian(self, damping: float) -> np.ndarray: """Construct Jacobian for damped message passing.""" base_jacobian = self._construct_message_jacobian() identity = np.eye(base_jacobian.shape[0]) # Damped operator: (1-α)I + α*T return (1 - damping) * identity + damping * base_jacobian def _compute_damped_spectral_radius(self, damping: float) -> float: """Compute spectral radius with damping.""" damped_jacobian = self._construct_damped_message_jacobian(damping) return np.max(np.abs(np.linalg.eigvals(damped_jacobian))) def _entropy(self, distribution: np.ndarray) -> float: """Compute entropy of probability distribution.""" # Numerical stability p = np.maximum(distribution, 1e-15) p = p / np.sum(p) # Normalize return -np.sum(p * np.log(p)) def _get_variable_degree(self, var_name: str) -> int: """Get degree of variable node.""" # Simplified implementation return 2 # Placeholder def _compute_gibbs_free_energy(self, beliefs: Dict[str, np.ndarray]) -> Optional[float]: """Compute exact Gibbs free energy if possible.""" # This would require exact partition function computation # which is generally intractable return None def _compute_message_residual(self, old_messages: Dict[str, np.ndarray], new_messages: Dict[str, np.ndarray]) -> float: """Compute L2 norm of message differences.""" total_residual = 0.0 for key in old_messages: if key in new_messages: diff = old_messages[key] - new_messages[key] total_residual += np.linalg.norm(diff)**2 return np.sqrt(total_residual) def _compute_rate_confidence(self, t: np.ndarray, log_residuals: np.ndarray, coeffs: np.ndarray) -> float: """Compute confidence in convergence rate estimate.""" # R-squared of linear fit predicted = np.polyval(coeffs, t) ss_res = np.sum((log_residuals - predicted)**2) ss_tot = np.sum((log_residuals - np.mean(log_residuals))**2) return max(0, 1 - ss_res / (ss_tot + 1e-15)) def _detect_oscillations(self, residuals: np.ndarray, window_size: int = 5) -> bool: """Detect oscillatory behavior in residuals.""" if len(residuals) < 2 * window_size: return False # Look for alternating increases/decreases recent_residuals = residuals[-2*window_size:] diffs = np.diff(recent_residuals) sign_changes = np.sum(np.diff(np.sign(diffs)) != 0) # High frequency of sign changes indicates oscillation return sign_changes > 0.7 * len(diffs) def _detect_stagnation(self, residuals: np.ndarray, window_size: int = 10) -> bool: """Detect stagnation in convergence.""" if len(residuals) < window_size: return False recent_residuals = residuals[-window_size:] relative_change = np.std(recent_residuals) / (np.mean(recent_residuals) + 1e-15) # Small relative change indicates stagnation return relative_change < 1e-3 def _count_messages(self) -> int: """Count total number of messages in factor graph.""" # Simplified implementation return 10 # Placeholder # Enhanced Loopy Belief Propagation with Convergence Guarantees class ConvergenceGuaranteedLBP: """Loopy belief propagation with convergence analysis and guarantees.""" def __init__(self, factor_graph: FactorGraph, damping: float = 0.0, max_iterations: int = 100, tolerance: float = 1e-6): """Initialize LBP with convergence enhancements. Args: factor_graph: Factor graph for inference damping: Damping parameter for stability max_iterations: Maximum number of iterations tolerance: Convergence tolerance """ self.graph = factor_graph self.damping = damping self.max_iterations = max_iterations self.tolerance = tolerance self.analyzer = ConvergenceAnalyzer(factor_graph) # Adaptive parameters self.adaptive_damping = True self.convergence_monitoring = True def run_inference(self) -> Dict[str, Any]: """Run belief propagation with convergence monitoring. Returns: inference_result: Results including beliefs and convergence analysis """ # Pre-analysis pre_analysis = self.analyzer.spectral_radius_analysis() # Adaptive damping if needed if self.adaptive_damping and pre_analysis['spectral_radius'] >= 1.0: damping_result = self.analyzer.damping_optimization() self.damping = damping_result['optimal_damping'] print(f"Adaptive damping enabled: α = {self.damping:.3f}") # Initialize messages messages = self._initialize_messages() message_history = [messages.copy()] # Iterative message passing for iteration in range(self.max_iterations): # Update messages with damping new_messages = self._update_messages_damped(messages, self.damping) message_history.append(new_messages.copy()) # Check convergence residual = self.analyzer._compute_message_residual(messages, new_messages) if residual < self.tolerance: print(f"Converged after {iteration + 1} iterations") break messages = new_messages # Compute final beliefs beliefs = self._compute_beliefs(messages) # Post-analysis if self.convergence_monitoring: convergence_diagnostics = self.analyzer.convergence_diagnostics( message_history, self.tolerance) bethe_analysis = self.analyzer.bethe_free_energy_analysis(beliefs) else: convergence_diagnostics = {} bethe_analysis = {} return { 'beliefs': beliefs, 'messages': messages, 'converged': residual < self.tolerance, 'iterations': iteration + 1, 'final_residual': residual, 'damping_used': self.damping, 'pre_analysis': pre_analysis, 'convergence_diagnostics': convergence_diagnostics, 'bethe_analysis': bethe_analysis, 'message_history': message_history if self.convergence_monitoring else None } def _initialize_messages(self) -> Dict[str, np.ndarray]: """Initialize messages uniformly.""" # Simplified implementation return {} def _update_messages_damped(self, current_messages: Dict[str, np.ndarray], damping: float) -> Dict[str, np.ndarray]: """Update messages with damping.""" # Standard message updates new_messages = self._update_messages_standard(current_messages) # Apply damping: m_new = (1-α)m_old + α*m_update damped_messages = {} for key in current_messages: if key in new_messages: damped_messages[key] = ((1 - damping) * current_messages[key] + damping * new_messages[key]) else: damped_messages[key] = current_messages[key] return damped_messages def _update_messages_standard(self, messages: Dict[str, np.ndarray]) -> Dict[str, np.ndarray]: """Standard message passing updates.""" # Simplified implementation return messages def _compute_beliefs(self, messages: Dict[str, np.ndarray]) -> Dict[str, np.ndarray]: """Compute beliefs from messages.""" # Simplified implementation return {'variables': {}, 'factors': {}} ## References 1. Kschischang, F. R., et al. (2001). Factor Graphs and the Sum-Product Algorithm 2. Wainwright, M. J., & Jordan, M. I. (2008). Graphical Models, Exponential Families, and Variational Inference 3. Loeliger, H. A. (2004). An Introduction to Factor Graphs 4. Bishop, C. M. (2006). Pattern Recognition and Machine Learning 5. Koller, D., & Friedman, N. (2009). Probabilistic Graphical Models 6. Yedidia, J. S., et al. (2003). Constructing free-energy approximations and generalized belief propagation algorithms 7. Mooij, J. M., & Kappen, H. J. (2007). Sufficient conditions for convergence of the sum-product algorithm 8. Heskes, T. (2006). Convexity arguments for efficient minimization of the Bethe and Kikuchi free energies