ReactiveBayes · albertpod · Dec 14, 2023 · Nov 21, 2023 · Nov 23, 2023 · Nov 23, 2023
diff --git a/benchmarks/Linear Multivariate Gaussian State Space Model Benchmark.ipynb b/benchmarks/Linear Multivariate Gaussian State Space Model Benchmark.ipynb
@@ -220,7 +220,7 @@
     "function reactivemp_inference_smoothing(observations, A, B, P, Q)\n",
     "    n = length(observations) \n",
     "    \n",
-    "    result = inference(\n",
+    "    result = infer(\n",
     "        model = linear_gaussian_ssm_smoothing(n, A, B, P, Q),\n",
     "        data = (y = observations, ),\n",
     "        options = (limit_stack_depth = 500, )\n",
@@ -237,7 +237,7 @@
     "        x_min_t_mean, x_min_t_cov = mean_cov(q(x_t))\n",
     "    end\n",
     "    \n",
-    "    result = rxinference(\n",
+    "    result = infer(\n",
     "        model = linear_gaussian_ssm_filtering(A, B, P, Q),\n",
     "        data = (y_t = observations, ),\n",
     "        autoupdates = autoupdates,\n",

diff --git a/benchmarks/Tiny Benchmark.ipynb b/benchmarks/Tiny Benchmark.ipynb
@@ -121,7 +121,7 @@
     "        x_prior_mean, x_prior_var = mean_var(q(x_next))\n",
     "    end\n",
     "\n",
-    "    return rxinference(\n",
+    "    return infer(\n",
     "        model       = filtering(c = 1.0, v = v),\n",
     "        datastream  = datastream,\n",
     "        autoupdates = autoupdates,\n",
@@ -460,7 +460,7 @@
    ],
    "source": [
     "function run_smoothing(data, n, v)\n",
-    "    return inference(\n",
+    "    return infer(\n",
     "        model = smoothing(n, c = 1.0, v = v), \n",
     "        data  = (y = data, ), \n",
     "        returnvars = KeepLast(),\n",

diff --git a/docs/make.jl b/docs/make.jl
@@ -115,7 +115,7 @@ makedocs(;
             "Model specification"       => "manuals/model-specification.md",
             "Constraints specification" => "manuals/constraints-specification.md",
             "Meta specification"        => "manuals/meta-specification.md",
-            "Inference specification"   => ["Overview" => "manuals/inference/overview.md", "Static dataset" => "manuals/inference/inference.md", "Real-time dataset / reactive inference" => "manuals/inference/rxinference.md", "Inference results postprocessing" => "manuals/inference/postprocess.md", "Manual inference specification" => "manuals/inference/manual.md"],
+            "Inference specification"   => ["Overview" => "manuals/inference/overview.md", "Static vs Streamline inference" => "manuals/inference/infer.md", "Inference results postprocessing" => "manuals/inference/postprocess.md", "Manual inference specification" => "manuals/inference/manual.md"],
             "Inference customization"   => ["Defining a custom node and rules" => "manuals/custom-node.md"],
             "Debugging"                 => "manuals/debugging.md",
             "Delta node"                => "manuals/delta-node.md"

diff --git a/docs/src/library/bethe-free-energy.md b/docs/src/library/bethe-free-energy.md
@@ -53,7 +53,7 @@ which internalizes the factors of the  model. The last two terms specify entropi
 
 Crucially, the BFE can be iteratively optimized for each individual variational distribution in turn. Optimization of the BFE is thus more manageable than direct optimization of the VFE.
 
-For iterative optimization of the BFE, the variational distributions must first be initialized. The `initmarginals` keyword argument to the [`inference`](@ref) and [`rxinference`](@ref) functions initializes the variational distributions of the BFE.
+For iterative optimization of the BFE, the variational distributions must first be initialized. The `initmarginals` keyword argument to the [`infer`](@ref) function initializes the variational distributions of the BFE.
 
 For disambiguation, note that the initialization of the variational distribution is a different design consideration than the choice of priors. A prior specifies a factor in the model definition, while initialization concerns factors in the variational distribution.
 

diff --git a/docs/src/manuals/constraints-specification.md b/docs/src/manuals/constraints-specification.md
@@ -162,4 +162,4 @@ end
 model, returnval = create_model(my_model(arguments...); constraints = constraints)
 ```
 
-Alternatively, it is possible to use constraints directly in the automatic [`inference`](@ref) and [`rxinference`](@ref) functions that accepts `constraints` keyword argument. 
+Alternatively, it is possible to use constraints directly in the automatic [`infer`](@ref) function that accepts `constraints` keyword argument. 
diff --git a/docs/src/manuals/custom-node.md b/docs/src/manuals/custom-node.md
@@ -279,7 +279,7 @@ end
 Finally, we can run inference with this model and the generated dataset:
 
 ```@example create-node
-result_mybernoulli = inference(
+result_mybernoulli = infer(
     model = coin_model_mybernoulli(length(dataset)), 
     data  = (y = dataset, ),
 )
@@ -312,7 +312,7 @@ As a sanity check, we can create the same model with the `RxInfer` built-in node
 
 end
 
-result_bernoulli = inference(
+result_bernoulli = infer(
     model = coin_model(length(dataset)), 
     data  = (y = dataset, ),
 )

diff --git a/docs/src/manuals/debugging.md b/docs/src/manuals/debugging.md
@@ -36,7 +36,7 @@ dataset = convert.(Int64, rand(Bernoulli(θ_real), n))
 
 end
 
-result = inference(
+result = infer(
     model = coin_model(length(dataset)), 
     data  = (x = dataset, ),
 );
@@ -56,7 +56,7 @@ vline!([θ_real], label="Real θ", title = "Inference results")
 We can figure out what's wrong by looking at the Memory Addon. To obtain the trace, we have to add `addons = (AddonMemory(),)` as an argument to the inference function.
 
 ```@example addoncoin
-result = inference(
+result = infer(
     model = coin_model(length(dataset)), 
     data  = (x = dataset, ),
     addons = (AddonMemory(),)
@@ -93,7 +93,7 @@ All the observations (purple, green, pink, blue) have much smaller rate paramete
 
 end
 
-result = inference(
+result = infer(
     model = coin_model(length(dataset)), 
     data  = (x = dataset, ),
 );

diff --git a/docs/src/manuals/delta-node.md b/docs/src/manuals/delta-node.md
@@ -63,7 +63,7 @@ end
 To execute the inference procedure:
 
 ```@example delta_node_example
-inference(model = delta_node_example(), meta=delta_meta, data = (z = 1.0,))
+infer(model = delta_node_example(), meta=delta_meta, data = (z = 1.0,))
 ```
 
 This methodology is consistent even when the delta node is associated with multiple nodes. For instance:

diff --git a/docs/src/manuals/getting-started.md b/docs/src/manuals/getting-started.md
@@ -121,7 +121,7 @@ As you can see, `RxInfer` offers a model specification syntax that resembles clo
 Once we have defined our model, the next step is to use `RxInfer` API to infer quantities of interests. To do this we can use a generic `inference` function that supports static datasets.
 
 ```@example coin 
-result = inference(
+result = infer(
     model = coin_model(length(dataset)),
     data  = (y = dataset, )
 )

diff --git a/docs/src/manuals/inference/infer.md b/docs/src/manuals/inference/infer.md
@@ -0,0 +1,17 @@
+# [Automatic Inference Specification](@id user-guide-inference)
+
+`RxInfer` provides the `infer` function for quickly running and testing your model with both static and streaming datasets. To enable streaming behavior, the `infer` function accepts an `autoupdates` argument, which specifies how to update your priors for future states based on newly updated posteriors. 
+
+It's important to note that while this function covers most capabilities of the inference engine, advanced use cases may require resorting to the [Manual Inference Specification](@ref user-guide-inference-execution-manual-specification).
+
+For details on manual inference specification, see the [Manual Inference](@ref user-guide-manual-inference) section.
+
+```@docs
+infer
+InferenceResult
+RxInfer.start
+RxInfer.stop
+@autoupdates
+RxInferenceEngine
+RxInferenceEvent
+```
diff --git a/docs/src/manuals/inference/inference.md b/docs/src/manuals/inference/inference.md
diff --git a/docs/src/manuals/inference/overview.md b/docs/src/manuals/inference/overview.md
@@ -11,12 +11,12 @@ The inference engine itself isn't aware of different algorithm types and simply
 
 ## [Automatic inference specification on static datasets](@id user-guide-inference-execution-automatic-specification-static)
 
-`RxInfer` exports the `inference` function to quickly run and test you model with static datasets. See more information about the `inference` function on the separate [documentation section](@ref user-guide-inference). 
+`RxInfer` exports the `infer` function to quickly run and test you model with static datasets. See more information about the `infer` function on the separate [documentation section](@ref user-guide-inference). 
 
 ## [Automatic inference specification on real-time datasets](@id user-guide-inference-execution-automatic-specification-realtime)
 
-`RxInfer` exports the `rxinference` function to quickly run and test you model with dynamic and potentially real-time datasets. See more information about the `rxinference` function on the separate [documentation section](@ref user-guide-rxinference). 
+`RxInfer` supports running inference the with dynamic and potentially real-time datasets with enabled `autoupdates` keyword. See more information about the `infer` function on the separate [documentation section](@ref user-guide-inference). 
 
 ## [Manual inference specification](@id user-guide-inference-execution-manual-specification)
 
-While both `inference` and `rxinference` use most of the `RxInfer` inference engine capabilities in some situations it might be beneficial to write inference code manually. The [Manual inference](@ref user-guide-manual-inference) documentation section explains how to write your custom inference routines.
+While `infer` uses most of the `RxInfer` inference engine capabilities in some situations it might be beneficial to write inference code manually. The [Manual inference](@ref user-guide-manual-inference) documentation section explains how to write your custom inference routines.
diff --git a/docs/src/manuals/inference/postprocess.md b/docs/src/manuals/inference/postprocess.md
@@ -1,7 +1,6 @@
 # [Inference results postprocessing](@id user-guide-inference-postprocess)
 
-Both [`inference`](@ref) and [`rxinference`](@ref) allow users to postprocess 
-the inference result with the `postprocess = ...` keyword argument. The inference engine 
+[`infer`](@ref) allow users to postprocess the inference result with the `postprocess = ...` keyword argument. The inference engine 
 operates on __wrapper__ types to distinguish between marginals and messages. By default 
 these wrapper types are removed from the inference results if no addons option is present.
 Together with the enabled addons, however, the wrapper types are preserved in the 

diff --git a/docs/src/manuals/inference/rxinference.md b/docs/src/manuals/inference/rxinference.md
diff --git a/docs/src/manuals/meta-specification.md b/docs/src/manuals/meta-specification.md
@@ -90,16 +90,10 @@ end
 model, returnval = create_model(my_model(arguments...); meta = my_meta)
 ```
 
-Alternatively, it is possible to use meta directly in the automatic [`inference`](@ref) and [`rxinference`](@ref) functions that accepts `meta` keyword argument:
+Alternatively, it is possible to use meta directly in the automatic [`infer`](@ref) function that accepts `meta` keyword argument:
 
 ```julia
-inferred_result = inference(
-    model = my_model(arguments...),
-    meta = my_meta,
-    ...
-)
-
-inferred_result = rxinference(
+inferred_result = infer(
     model = my_model(arguments...),
     meta = my_meta,
     ...
@@ -116,7 +110,7 @@ inferred_result = rxinference(
     ...
 end
 ```
-If you add node-specific meta to your model this way, then you do not need to use the `meta` keyword argument in the `inference` and `rxinference` functions.
+If you add node-specific meta to your model this way, then you do not need to use the `meta` keyword argument in the `infer` function.
 
 
 ## Create your own meta
@@ -179,7 +173,7 @@ y_data = 4.0
 end
 
 #do inference
-inference_result = inference(
+inference_result = infer(
     model = gaussian_model(),
     data = (y = y_data,)
 )

diff --git a/examples/advanced_examples/Active Inference Mountain car.ipynb b/examples/advanced_examples/Active Inference Mountain car.ipynb
diff --git a/examples/advanced_examples/Advanced Tutorial.ipynb b/examples/advanced_examples/Advanced Tutorial.ipynb
@@ -645,7 +645,7 @@
     "\n",
     "dataset = float.(rand(Bernoulli(p), 500));\n",
     "\n",
-    "result = inference(\n",
+    "result = infer(\n",
     "    model = coin_toss_model(length(dataset)),\n",
     "    data  = (y = dataset, )\n",
     ")\n",
@@ -978,7 +978,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "rxresult = rxinference(\n",
+    "rxresult = infer(\n",
     "    model = online_coin_toss_model(),\n",
     "    data  = (y = dataset, ),\n",
     "    autoupdates = autoupdates,\n",
@@ -1297,7 +1297,7 @@
     }
    ],
    "source": [
-    "result = inference(\n",
+    "result = infer(\n",
     "    model         = test_model6(length(dataset)),\n",
     "    data          = (y = dataset, ),\n",
     "    constraints   = constraints6, \n",

diff --git a/examples/advanced_examples/Assessing People Skills.ipynb b/examples/advanced_examples/Assessing People Skills.ipynb
@@ -179,7 +179,7 @@
    ],
    "source": [
     "test_results = [0.1, 0.1, 0.1]\n",
-    "inference_result = inference(\n",
+    "inference_result = infer(\n",
     "    model = skill_model(),\n",
     "    data  = (r = test_results, )\n",
     ")"

diff --git a/examples/advanced_examples/Chance Constraints.ipynb b/examples/advanced_examples/Chance Constraints.ipynb
@@ -254,11 +254,11 @@
     "    m_u = zeros(T)\n",
     "    v_u = lambda^(-1)*ones(T)\n",
     "    \n",
-    "    function infer(x_t::Float64)\n",
+    "    function compute(x_t::Float64)\n",
     "        model_t = regulator_model(; T=T, lo=lo, hi=hi, epsilon=epsilon, atol=atol)\n",
     "        data_t = (m_u = m_u, v_u = v_u, x_t = x_t)\n",
     "        \n",
-    "        result = inference(\n",
+    "        result = infer(\n",
     "            model = model_t, \n",
     "            data = data_t,\n",
     "            iterations = n_its)\n",
@@ -272,7 +272,7 @@
     "    pol = zeros(T) # Predefine policy variable\n",
     "    act() = pol[1]\n",
     "\n",
-    "    return (infer, act)\n",
+    "    return (compute, act)\n",
     "end;"
    ]
   },
@@ -309,15 +309,15 @@
    "outputs": [],
    "source": [
     "(execute, observe) = initializeWorld() # Let there be a world\n",
-    "(infer, act) = initializeAgent() # Let there be an agent\n",
+    "(compute, act) = initializeAgent() # Let there be an agent\n",
     "\n",
     "a = Vector{Float64}(undef, N) # Actions\n",
     "x = Vector{Float64}(undef, N) # States\n",
     "for t = 1:N\n",
     "    a[t] = act()\n",
     "           execute(t, a[t])\n",
     "    x[t] = observe()\n",
-    "           infer(x[t])\n",
+    "           compute(x[t])\n",
     "end"
    ]
   },

diff --git a/examples/advanced_examples/Conjugate-Computational Variational Message Passing.ipynb b/examples/advanced_examples/Conjugate-Computational Variational Message Passing.ipynb
@@ -544,7 +544,7 @@
     }
    ],
    "source": [
-    "results = inference(\n",
+    "results = infer(\n",
     "    model = measurement_model(nr_observations),\n",
     "    data = (y = measurements,),\n",
     "    iterations = 5,\n",
@@ -1164,7 +1164,7 @@
     }
    ],
    "source": [
-    "res = inference(\n",
+    "res = infer(\n",
     "    model = normal_square_model(1000),\n",
     "    data = (y = y,),\n",
     "    iterations = 10,\n",

diff --git a/examples/advanced_examples/GP Regression by SSM.ipynb b/examples/advanced_examples/GP Regression by SSM.ipynb
@@ -418,7 +418,7 @@
     }
    ],
    "source": [
-    "result_32 = inference(\n",
+    "result_32 = infer(\n",
     "    model = gp_regression(n, P∞, A, Q, H, σ²_noise),\n",
     "    data = (y = y_data,)\n",
     ")"
@@ -506,7 +506,7 @@
     }
    ],
    "source": [
-    "result_52 = inference(\n",
+    "result_52 = infer(\n",
     "    model = gp_regression(n, P∞, A, Q, H, σ²_noise),\n",
     "    data = (y = y_data,)\n",
     ")"